example conclusion in case study

• AI Content Shield
• AI KW Research
• AI Assistant
• SEO Optimizer
• AI KW Clustering
• Customer reviews
• The NLO Revolution
• Press Center
• Help Center
• Content Resources
• Facebook Group

How to Write Effective Case Study Conclusions

Table of Contents

Not many people realize that the conclusion is vital to writing your case study. It should summarize the entire study, clarify all the research points, and focus on a few key takeaways.

There are several ways how to write case study conclusion . And we’re here to guide you with some easy and effective steps.

A good conclusion is interesting and captures the essence of your case. It needs to reflect your information and help the reader adopt your conclusion and act on it. Keep reading to learn how to do just that.

Importance of Your Case Study Conclusion

Your conclusion is an opportunity for you to summarize your findings and highlight what this study has taught you.

It should also summarize and draw out the main points you’ve discussed and reinforce the importance of your work. Remember, your last impression needs to be just as good as your first. You want to leave readers with something to think about or act on.

Types of Case Studies

Before we proceed on  how to write case study conclusion , let’s take a brief look at the different types of case studies.

There are different types of case studies depending on how they are structured, what is the target audience, and the research methodology used. And your conclusion may vary depending on the nature of the case study.

Some of the most common case studies are:

• Historical:  Historical events have a multitude of sources offering different perspectives. These perspectives can be applied, compared, and thoroughly analyzed in the modern world.
• Problem-oriented:  This type of case study is used for solving problems. You can use theoretical situations where you immerse yourself in a situation. Through this, you can thoroughly examine a problem and find ways to resolve it.
• Cumulative:  In a cumulative study, you gather information and offer comparisons. An example of this is a business case study that tells people about a product’s value.
• Critical:  Critical case studies focus on exploring the causes and effects of a particular situation. To do this, you can have varying amounts of research and various interviews.
• Illustrative:  In this case study, certain events are described, as well as the lessons learned.

How to Write Case Study Conclusion Effectively

Writing your conclusion doesn’t need to be complicated. Follow these steps to help you get started on an effective conclusion.

1. Inform the reader precisely why your case study and your findings are relevant

Your conclusion is where you point out the significance of your study. You can cite a specific case in your work and explain how it applies to other relevant cases.

2. Restate your thesis and your main findings

Remind your readers of the thesis statement you made in your introduction but don’t just copy it directly. Also, make sure to mention your main findings to back up your thesis.

3. Give a summary of previous case studies you reviewed

What did you discover that was different about your case? How was previous research helpful? Include this in your conclusion so readers can understand your work and how it contributes to expanding current knowledge.

4. End with recommendations

Wrap up your paper by explaining how your case study and findings could form part of future research on the topic. You can also express your recommendations by commenting on how certain studies, programs, or policies could be improved.

Make sure everything you write in your conclusion section is convincing enough to tell the reader that your case is an effective solution. And if the purpose of your case is complicated, make sure to sum it up in point form. This will help the reader review the case again before approaching the conclusion.

How Long Should Your Conclusion Be?

The length of your conclusion may vary depending on whether you’re writing a thesis or a dissertation. At least 5-9 percent of your overall word count should be dedicated to your conclusion.

Often, empirical scientific studies have brief conclusions describing the main findings and recommendations for future research. On the other hand, humanities topics or systematic reviews may require more space to conclude their analysis. They will need to integrate all the previous sections into an overall argument.

Wrapping Up

Your conclusion is an opportunity to translate and amplify the information you have put in the body of the paper.

More importantly, it is an opportunity to leave a lasting positive impression . Make the right impression by following these quick steps on  how to write case study conclusion  effectively.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

Explore All Blog Post Conclusion Articles

A guide to writing a conclusion for a speech.

A quality speech or presentation is comparable to a quality play, film, or song. It begins by grabbing the listener’s…

• Blog Post Conclusion

The Ideal Length Of a Conclusion Paragraph

You have spent a lot of time writing your essay by the time you reach the final paragraph, so your…

Writing a Conclusion for Persuasive Essays!

Conclusions bring everything you have been discussing in your paper to a close. In the introduction and body paragraphs, you…

Clear Guide to Introduction & Conclusion Paragraphs Examples

The introduction and conclusion play a major role in academic essays. Writing these paragraphs typically requires much of your focus.…

Effective Guide to Write a Discussion & Conclusion

How to write a discussion and conclusion section of a paper? This is often one of the most confusing aspects,…

Importance of Good Conclusion Paragraph for a Research Paper

Writing a good conclusion paragraph for a research paper can sometimes be challenging. Writers often find it difficult to draft…

• Secure the Interview
• Get the Offer
• Application Review
• McKinsey Problem Solving Game
• BCG Online Case Assessment (Chatbot)
• Bain SOVA Assessment
• Math Drills

How to Successfully Conclude a Case Study

Knowing how to successfully conclude a case study is one of the most important parts of every case interview. A strong conclusion shows how well you summarize the entire case solution into a couple of points. In addition, it proves that you can successfully back up your arguments with both quantitative and qualitative facts. It’s also the very last point of the case, thus the point clients remember the most. 

How to Successfully Conclude a Case Study - Best Practice Approaches 

Take approximately 30 seconds before concluding the case, and use this time to jot down key messages you want to touch on during your recommendation. You want to have your ideas sorted out in advance so that you speak clearly and concisely, covering each point without referring back to your notes. 

Practice the art of the elevator pitch

Ideally, your final recommendation should not exceed more than one minute. It is a way to mimic day-to-day interactions with our clients when we are asked to give them key pointers in a short summary. 

Answer first and answer focused

As you will see more in detail with Prepmatter cases, in many case types, you should start with the answer. However, in certain case types where the client has a business problem yet to be diagnosed (e.g., competitive response strategy, profitability, operations), it’s best to start with your diagnosis and then provide recovery solutions. 

Allocate time correctly

Make sure to allocate most of your time to the delivery of a solution and its supporting evidence. Some candidates spend half - if not more - of their time in delivering risks and next steps, which dilutes the key messages in the recommendation. Conclude the case in the following structure: 

• Recommendation: Give a one-sentence action-oriented recommendation. 
• First supporting fact with figures (quantitative) 
• Second supporting fact with figures (quantitative)
• Third supporting fact (qualitative)
• Risks: Comment on the potential risks assessed during the case. Try to mention them in a way supporting your conclusion. 
• Next steps: Provide direction on how they should act going forward based on the recommendation.

Example of a Strong Conclusion

• I suggest the client should go ahead with this investment and enter the cosmetics market with their new product.
• With this investment, the client can make an $800M profit over the next three years, which is higher than our objective of $600M. 
• The cosmetics market is expected to grow at a 9% annual growth rate over the next 10 years, promising sustainable value in the long term. 
• We can create synergies by combining our back-end operations with our existing business. 
• Risks: There is a regulatory risk given that the authorities increase their health restrictions related to cosmetics products. The client should make sure that they spend additional effort to comply with all regulations. 
• Next steps: As the next step, I suggest the client design a detailed production plan for the new product. 

How to Practice Case Conclusions

There are various ways to practice concluding a case. Practice with the Prepmatter cases or any other case you may have. You can change the numbers in the case to create hypothetical facts and draw a new conclusion. By doing so, you can also change the recommendation if the numbers change significantly. For instance, if you change the 3-year profits to $400M from $800M in the example above, the recommendation would change from ‘Go’ to ‘No-go’. 

Knowing how to successfully conclude a case study is a critical part of each case interview, so we recommend you set aside specific time to review it with your coach or case partner. Take time to solve as many cases as possible to improve how well you summarize, support, and present your conclusion.

• Reviews / Why join our community?
• For companies
• Frequently asked questions

How to Write the Perfect Conclusion to Your UX Case Study

If you nail your case study’s conclusion, you’re much likelier to get called to an interview, because employers tend to recall the last parts of a case study the most. Let’s see how you can craft the perfect ending to your UX case study.

So, you’ve written a great introductory hook to your UX case study, where you defined your problem statement , your approach to solving it and your role in the project. You then brought your reader through your design process and highlighted the decisions and challenges that led to your final result. One question remains: how do you end your UX case study with a bang? As it turns out, you need to include 3 things in your UX case study’s ending to make it truly satisfying: the final product, its impacts and your reflections.

What’s the Purpose of Your UX Case Study’s Conclusion?

To wrap your story up satisfactorily.

The conclusion of your UX case study serves as your story’s resolution. It’s where you tie up loose ends and close your story’s arc by answering the main question you asked in your introduction. When done right, your case study’s ending will create immense satisfaction and a lasting impression on a recruiter.

In the last part of your UX case study’s 5-part story arc, create a nice resolution to your story. The conclusion is where you bring everything together to leave your reader satisfied, if not wowed, with what you did and the outcome. Author / Copyright holder: Teo Yu Siang and the Interaction Design Foundation. Copyright terms and license: CC BY-NC-SA 3.0.

To Create a Great Last Impression

The lasting impression you create through your UX case study’s conclusion is absolutely vital. This is because of the serial-position effect , discovered by the German psychologist Hermann Ebbinghaus, where people tend to remember the first and last parts of a series best and forget the middle parts the most.

For instance, do you remember your most recent stay at a hotel? Chances are, you can recall how your stay ended when you checked out and how it began when you checked in—but nothing much of the middle. That’s the serial-position effect.

In 1913, Hermann Ebbinghaus discovered that we tend to remember only the beginnings and endings of things, and largely forget the middle parts. This means your UX case study’s introduction and conclusion are crucial parts! Author / copyright holder: Teo Yu Siang and the Interaction Design Foundation. Copyright terms and license: CC BY-NC-SA 3.0.

In particular, the serial-position effect is found to be strongest in the last items of a list. People tend to recall the last parts of an experience the most —that’s how vital your UX case study’s ending is! That isn’t to say you can afford to neglect any part of your case study’s middle part, though—it’s merely a scientific observation as to how recruiters will remember you. In other words, if you nail your case study’s conclusion, you’re much likelier to get called to an interview.

How Long Should Your UX Case Study’s Conclusion Be?

Your conclusion should ideally be as short as your introduction, or 4–5 sentences long . However, unlike in an introduction, you’ve got room for flexibility in your conclusion. That’s because while your introduction’s role is to quickly provide the needed information to move on to the main story, your conclusion has a different purpose—to make a great last impression. So, if you think a slightly longer conclusion can impress a recruiter more, you should go for it.

For instance, if you’ve got interesting lessons learnt or incredible results, you can afford to make your conclusion slightly longer, at around 3–4 paragraphs. Generally, the longer your case study’s middle portion is, the longer you can make your conclusion. But just like any other part of your case study, include only the essential and remove the rest. Every word counts!

3 Things You Should Include in Your UX Case Study’s Conclusion

A great UX case study’s ending contains these 3 things:

The final product;

Results and impact of the final product; and

Reflections and lessons learnt.

1. Show the Final Product

If you haven’t already showcased your final product in the middle part of your UX case study, now is the time to show it. Your final product will differ from project to project. For instance, a design thinking project will likely have a high-fidelity prototype as the final product. In a user research project, however, the final deliverable might be a set of user personas or a research report that contains recommendations.

If your final product is visual in nature—for example, an app—show it in a visual way. Screenshots, videos and interactive embedded prototypes are great ways to impress a recruiter. At the same time, practice restraint so that you don’t dump 100 photos of your entire project. Use only the most impactful ones.

If you’ve revamped an existing design, then this is a great time to showcase a before-and-after comparison. Include some screenshots of the problems in the old design in your introduction—and show and point out where you’ve improved it in your conclusion.

Even if your role is specialized and you therefore didn’t contribute directly to the final design of a product, you can still show the final product. This helps recruiters understand how your work shaped the final results. For example, if you specialize in visual design and have created an icon library, feel free to show how the icons are used throughout the product. If you do so, remember to make it clear what you worked on and what your colleagues created.

2. Demonstrate the Impacts of Your Project

Results are a must-have in your case study’s conclusion. Recruiters hire you to bring value to their organization, so they want to see the impact your work has generated.

Show results that are linked to the problem statement you introduced at the beginning of your case study. Since your problem statement should involve a business need, your results should also be business-oriented . For instance, show how your work has improved conversion rates or decreased drop-off rates. If you’ve created an app, show the app download or user rating metrics.

We encourage you to show numerical results, because they easily show your impact on a business. However, you can also show qualitative results—for instance, you can quote positive feedback and anecdotes from users and stakeholders .

Product designer Simon Pan’s UX case study is a great example of how to show the business results of your project. In his case study on his work for the ridesharing app Uber, Simon clearly shows how his work positively impacted the business. Author / copyright holder: Simon Pan. Copyright terms and license: Fair use.

3. Reflect on What You’ve Learnt

It’s vital that you reflect on your work in your conclusion. That’s how you create a sense of resolution and end in a satisfying way.

Furthermore, recruiters like to see designers who reflect on what they’ve learnt. According to Anett Illés from the UX design portfolio site UX Folio:

“UX recruiters and UX leads search for problem solvers motivated to explore and learn new things. So don’t hide your thirst for knowledge. On the contrary, highlight it!”

—Anett Illés, UX Folio

If you’re stuck at coming up with reflections, here are some questions you can ask yourself:

What is your main challenge in the project, and how have you handled it? For example, it could be the first time you’ve ever led a project. Or the project could’ve required you to step out of your comfort zone. Ideally, you should include a challenge that you have overcome, although sometimes a failure can make for an effective reflection, too.

What are some lessons you’ve learnt through the project? We are bound to make mistakes in our projects—and while we shouldn’t dwell on them in our UX case studies , we can turn them into learning points. Demonstrate how you’ve grown through your project.

Has the project changed your outlook as a designer? For instance, you could’ve learnt that a designer’s job is not only to delight users but also to bring value to the business.

What are your next steps for the project? Remember that design is an iterative process, so there’s no clear end point. If you could, how would you continue your work and take your project to the next level?

Download Our Template to Guide You

We’ve created a PDF guide to help you write your UX case study’s conclusion. Download your copy now:

An Example of a UX Case Study Conclusion

Let’s end with a sample conclusion we’ve created. This hypothetical UX case study is a design thinking project where we redesigned the home page of an ecommerce site. In this case study, we’ll assume that we’ve already introduced the final product in our middle portion.

We start with a long, first draft of our conclusion. We’ve included headings so you can clearly see its different components:

Results and impact: Compared with the previous version of the home page, our newly designed home page increased the conversion rate by 20%. Our admin team also reported a marked drop in the number of enquiries about how the platform works, which demonstrates an increase in ease of understanding. Main challenge and lesson learnt: This was the first time I led a project. Although I was nervous at the beginning, I soon learnt to trust my team-mates. I also learnt that active communication and short daily stand-up meetings were key to ensuring the project’s success. Next steps: This home page redesign validated our hypothesis that the most effective value proposition is one that is centered around a person’s core motivation . I’m looking forward to applying the same approach to other key pages of the platform.

Now that we’ve got all the main points, we can focus on shortening it to fit 4–5 sentences. Don’t skip this step, because it will make your conclusion drastically better!

Here’s our shortened and final conclusion:

Our redesigned home page had a 20% higher conversion rate. We also received fewer enquiries about how the platform works, which shows the new design is easier to understand. This was the first time I led a project. While I was nervous initially, I learnt to trust my team-mates and that daily stand-up meetings were key to the project’s success. This project validated the value of using copy that is centered around a person’s core motivation, and I look forward to applying the same approach to the rest of the site.

If you’ve revamped an existing design, you should also point out the specific areas you’ve improved in the design.

The Take Away

A well-written conclusion to a UX case study ensures that a recruiter leaves with a great last impression. This is extremely valuable because we tend to remember the last parts of an experience best, due to what’s called the serial-position effect.

Your conclusion should be 4–5 sentences long, although a longer middle portion or more elaborate reflections and results can justify a lengthier conclusion. To create a satisfying end to your project’s story and deliver a great last impression, you should include the following in your UX case study’s conclusion:

The final product (if you’ve not already shown it in your case study’s middle portion);

Business-oriented results and impacts of your project; and

Reflections on your work.

References and Where to Learn More

Our UX case study writing guides take inspiration from Gustav Freytag’s 5-part story structure, also called Freytag’s Pyramid. The pyramid was first written in Freytag’s 1863 book Die Technik des Dramas , or “Technique of the Drama”.

Hermann Ebbinghaus first published about the serial-position effect in 1913 in his paper titled “On memory: A contribution to experimental psychology” .

You can check out Simon Pan’s UX design portfolio for inspiration:

Your use of English can make or break your UX case study. Check out our guide, which contains 8 tips to write effectively

Anett Illés writes about how to follow UX recruiters’ logic in your UX case study in her article in UX Folio

Hero image: Author / Copyright holder: Matt Botsford. Copyright terms and license: Unsplash License.

Information Visualization

Get Weekly Design Insights

What you should read next, the top 6 insights from our agile methods for ux design course.

• 3 hours ago

Mobile UI Design: Top Insights from the IxDF Course

How to Create Effective Journey Maps: Learnings from the IxDF Course

Master Mobile Experiences: 5 Key Discoveries from the IxDF Course

Personas for Mobile UX Design

• 2 weeks ago

Interaction Design Foundation Reviews: Answers to frequently asked questions by members

• 3 weeks ago

10 UI Designer Portfolio Examples

What Tech Job is Right for Me? A Comprehensive Guide to Navigating Your Career Path

How to Succeed as a Designer on Agile Teams: Embrace Imperfection

• 4 weeks ago

Your Guide to Hamburger Menus

Open Access—Link to us!

We believe in Open Access and the  democratization of knowledge . Unfortunately, world-class educational materials such as this page are normally hidden behind paywalls or in expensive textbooks.

If you want this to change , cite this article , link to us, or join us to help us democratize design knowledge !

Privacy Settings

Our digital services use necessary tracking technologies, including third-party cookies, for security, functionality, and to uphold user rights. Optional cookies offer enhanced features, and analytics.

Experience the full potential of our site that remembers your preferences and supports secure sign-in.

Governs the storage of data necessary for maintaining website security, user authentication, and fraud prevention mechanisms.

Enhanced Functionality

Saves your settings and preferences, like your location, for a more personalized experience.

Referral Program

We use cookies to enable our referral program, giving you and your friends discounts.

Error Reporting

We share user ID with Bugsnag and NewRelic to help us track errors and fix issues.

Optimize your experience by allowing us to monitor site usage. You’ll enjoy a smoother, more personalized journey without compromising your privacy.

Analytics Storage

Collects anonymous data on how you navigate and interact, helping us make informed improvements.

Differentiates real visitors from automated bots, ensuring accurate usage data and improving your website experience.

Lets us tailor your digital ads to match your interests, making them more relevant and useful to you.

Advertising Storage

Stores information for better-targeted advertising, enhancing your online ad experience.

Personalization Storage

Permits storing data to personalize content and ads across Google services based on user behavior, enhancing overall user experience.

Advertising Personalization

Allows for content and ad personalization across Google services based on user behavior. This consent enhances user experiences.

Enables personalizing ads based on user data and interactions, allowing for more relevant advertising experiences across Google services.

Receive more relevant advertisements by sharing your interests and behavior with our trusted advertising partners.

Enables better ad targeting and measurement on Meta platforms, making ads you see more relevant.

Allows for improved ad effectiveness and measurement through Meta’s Conversions API, ensuring privacy-compliant data sharing.

LinkedIn Insights

Tracks conversions, retargeting, and web analytics for LinkedIn ad campaigns, enhancing ad relevance and performance.

LinkedIn CAPI

Enhances LinkedIn advertising through server-side event tracking, offering more accurate measurement and personalization.

Google Ads Tag

Tracks ad performance and user engagement, helping deliver ads that are most useful to you.

Share Knowledge, Get Respect!

or copy link

Cite according to academic standards

Simply copy and paste the text below into your bibliographic reference list, onto your blog, or anywhere else. You can also just hyperlink to this article.

New to UX Design? We’re giving you a free ebook!

Download our free ebook The Basics of User Experience Design to learn about core concepts of UX design.

In 9 chapters, we’ll cover: conducting user interviews, design thinking, interaction design, mobile UX design, usability, UX research, and many more!

New to UX Design? We’re Giving You a Free ebook!

Get science-backed answers as you write with Paperpal's Research feature

How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

Align your conclusion’s tone with the rest of your research paper. Start Writing with Paperpal Now!  

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Write your research paper conclusion 2x faster with Paperpal. Try it now!

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• 5 Reasons for Rejection After Peer Review
• Ethical Research Practices For Research with Human Subjects

7 Ways to Improve Your Academic Writing Process

• Paraphrasing in Academic Writing: Answering Top Author Queries

Preflight For Editorial Desk: The Perfect Hybrid (AI + Human) Assistance Against Compromised Manuscripts

You may also like, measuring academic success: definition & strategies for excellence, phd qualifying exam: tips for success , ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., what are journal guidelines on using generative ai..., quillbot review: features, pricing, and free alternatives, what is an academic paper types and elements , should you use ai tools like chatgpt for..., 9 steps to publish a research paper, what are the different types of research papers.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2023 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.

• PLOS Biology
• PLOS Climate
• PLOS Complex Systems
• PLOS Computational Biology
• PLOS Digital Health
• PLOS Genetics
• PLOS Global Public Health
• PLOS Medicine
• PLOS Mental Health
• PLOS Neglected Tropical Diseases
• PLOS Pathogens
• PLOS Sustainability and Transformation
• PLOS Collections
• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

Business growth

Marketing tips

16 case study examples (+ 3 templates to make your own)

I like to think of case studies as a business's version of a resume. It highlights what the business can do, lends credibility to its offer, and contains only the positive bullet points that paint it in the best light possible.

Imagine if the guy running your favorite taco truck followed you home so that he could "really dig into how that burrito changed your life." I see the value in the practice. People naturally prefer a tried-and-true burrito just as they prefer tried-and-true products or services.

To help you showcase your success and flesh out your burrito questionnaire, I've put together some case study examples and key takeaways.

What is a case study?

A case study is an in-depth analysis of how your business, product, or service has helped past clients. It can be a document, a webpage, or a slide deck that showcases measurable, real-life results.

For example, if you're a SaaS company, you can analyze your customers' results after a few months of using your product to measure its effectiveness. You can then turn this analysis into a case study that further proves to potential customers what your product can do and how it can help them overcome their challenges.

It changes the narrative from "I promise that we can do X and Y for you" to "Here's what we've done for businesses like yours, and we can do it for you, too."

16 case study examples 

While most case studies follow the same structure, quite a few try to break the mold and create something unique. Some businesses lean heavily on design and presentation, while others pursue a detailed, stat-oriented approach. Some businesses try to mix both.

There's no set formula to follow, but I've found that the best case studies utilize impactful design to engage readers and leverage statistics and case details to drive the point home. A case study typically highlights the companies, the challenges, the solution, and the results. The examples below will help inspire you to do it, too.

1. .css-1l9i3yq-Link[class][class][class][class][class]{all:unset;box-sizing:border-box;-webkit-text-fill-color:currentColor;cursor:pointer;}.css-1l9i3yq-Link[class][class][class][class][class]{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='ocean']{color:#3d4592;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='ocean']:hover{color:#2b2358;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='ocean']:focus{color:#3d4592;outline-color:#3d4592;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='white']{color:#fffdf9;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='white']:hover{color:#a8a5a0;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='white']:focus{color:#fffdf9;outline-color:#fffdf9;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='primary']{color:#3d4592;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='primary']:hover{color:#2b2358;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='primary']:focus{color:#3d4592;outline-color:#3d4592;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='secondary']{color:#fffdf9;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='secondary']:hover{color:#a8a5a0;}.css-1l9i3yq-Link[class][class][class][class][class][data-color='secondary']:focus{color:#fffdf9;outline-color:#fffdf9;}.css-1l9i3yq-Link[class][class][class][class][class][data-weight='inherit']{font-weight:inherit;}.css-1l9i3yq-Link[class][class][class][class][class][data-weight='normal']{font-weight:400;}.css-1l9i3yq-Link[class][class][class][class][class][data-weight='bold']{font-weight:700;} Volcanica Coffee and AdRoll

People love a good farm-to-table coffee story, and boy am I one of them. But I've shared this case study with you for more reasons than my love of coffee. I enjoyed this study because it was written as though it was a letter.

In this case study, the founder of Volcanica Coffee talks about the journey from founding the company to personally struggling with learning and applying digital marketing to finding and enlisting AdRoll's services.

It felt more authentic, less about AdRoll showcasing their worth and more like a testimonial from a grateful and appreciative client. After the story, the case study wraps up with successes, milestones, and achievements. Note that quite a few percentages are prominently displayed at the top, providing supporting evidence that backs up an inspiring story.

Takeaway: Highlight your goals and measurable results to draw the reader in and provide concise, easily digestible information.

2. Taylor Guitars and Airtable

This Airtable case study on Taylor Guitars comes as close as one can to an optimal structure. It features a video that represents the artistic nature of the client, highlighting key achievements and dissecting each element of Airtable's influence.

It also supplements each section with a testimonial or quote from the client, using their insights as a catalyst for the case study's narrative. For example, the case study quotes the social media manager and project manager's insights regarding team-wide communication and access before explaining in greater detail.

Takeaway: Highlight pain points your business solves for its client, and explore that influence in greater detail.

3. EndeavourX and Figma

My favorite part of Figma's case study is highlighting why EndeavourX chose its solution. You'll notice an entire section on what Figma does for teams and then specifically for EndeavourX.

It also places a heavy emphasis on numbers and stats. The study, as brief as it is, still manages to pack in a lot of compelling statistics about what's possible with Figma.

Takeaway: Showcase the "how" and "why" of your product's differentiators and how they benefit your customers.

4. ActiveCampaign and Zapier

Zapier's case study leans heavily on design, using graphics to present statistics and goals in a manner that not only remains consistent with the branding but also actively pushes it forward, drawing users' eyes to the information most important to them. 

The graphics, emphasis on branding elements, and cause/effect style tell the story without requiring long, drawn-out copy that risks boring readers. Instead, the cause and effect are concisely portrayed alongside the client company's information for a brief and easily scannable case study.

Takeaway: Lean on design to call attention to the most important elements of your case study, and make sure it stays consistent with your branding.

5. Ironclad and OpenAI

In true OpenAI fashion, this case study is a block of text. There's a distinct lack of imagery, but the study features a narrated video walking readers through the product.

The lack of imagery and color may not be the most inviting, but utilizing video format is commendable. It helps thoroughly communicate how OpenAI supported Ironclad in a way that allows the user to sit back, relax, listen, and be impressed. 

Takeaway: Get creative with the media you implement in your case study. Videos can be a very powerful addition when a case study requires more detailed storytelling.

6. Shopify and GitHub

GitHub's case study on Shopify is a light read. It addresses client pain points and discusses the different aspects its product considers and improves for clients. It touches on workflow issues, internal systems, automation, and security. It does a great job of representing what one company can do with GitHub.

To drive the point home, the case study features colorful quote callouts from the Shopify team, sharing their insights and perspectives on the partnership, the key issues, and how they were addressed.

Takeaway: Leverage quotes to boost the authoritativeness and trustworthiness of your case study. 

7 . Audible and Contentful

Contentful's case study on Audible features almost every element a case study should. It includes not one but two videos and clearly outlines the challenge, solution, and outcome before diving deeper into what Contentful did for Audible. The language is simple, and the writing is heavy with quotes and personal insights.

This case study is a uniquely original experience. The fact that the companies in question are perhaps two of the most creative brands out there may be the reason. I expected nothing short of a detailed analysis, a compelling story, and video content. 

Takeaway: Inject some brand voice into the case study, and create assets that tell the story for you.

8 . Zoom and Asana

Asana's case study on Zoom is longer than the average piece and features detailed data on Zoom's growth since 2020. Instead of relying on imagery and graphics, it features several quotes and testimonials. 

It's designed to be direct, informative, and promotional. At some point, the case study reads more like a feature list. There were a few sections that felt a tad too promotional for my liking, but to each their own burrito.

Takeaway: Maintain a balance between promotional and informative. You want to showcase the high-level goals your product helped achieve without losing the reader.

9 . Hickies and Mailchimp

I've always been a fan of Mailchimp's comic-like branding, and this case study does an excellent job of sticking to their tradition of making information easy to understand, casual, and inviting.

It features a short video that briefly covers Hickies as a company and Mailchimp's efforts to serve its needs for customer relationships and education processes. Overall, this case study is a concise overview of the partnership that manages to convey success data and tell a story at the same time. What sets it apart is that it does so in a uniquely colorful and brand-consistent manner.

Takeaway: Be concise to provide as much value in as little text as possible.

10. NVIDIA and Workday

The gaming industry is notoriously difficult to recruit for, as it requires a very specific set of skills and experience. This case study focuses on how Workday was able to help fill that recruitment gap for NVIDIA, one of the biggest names in the gaming world.

Though it doesn't feature videos or graphics, this case study stood out to me in how it structures information like "key products used" to give readers insight into which tools helped achieve these results.

Takeaway: If your company offers multiple products or services, outline exactly which ones were involved in your case study, so readers can assess each tool.

11. KFC and Contentful

I'm personally not a big KFC fan, but that's only because I refuse to eat out of a bucket. My aversion to the bucket format aside, Contentful follows its consistent case study format in this one, outlining challenges, solutions, and outcomes before diving into the nitty-gritty details of the project.

Say what you will about KFC, but their primary product (chicken) does present a unique opportunity for wordplay like "Continuing to march to the beat of a digital-first drum(stick)" or "Delivering deep-fried goodness to every channel."

Takeaway: Inject humor into your case study if there's room for it and if it fits your brand. 

12. Intuit and Twilio

Twilio does an excellent job of delivering achievements at the very beginning of the case study and going into detail in this two-minute read. While there aren't many graphics, the way quotes from the Intuit team are implemented adds a certain flair to the study and breaks up the sections nicely.

It's simple, concise, and manages to fit a lot of information in easily digestible sections.

Takeaway: Make sure each section is long enough to inform but brief enough to avoid boring readers. Break down information for each section, and don't go into so much detail that you lose the reader halfway through.

13. Spotify and Salesforce

Salesforce created a video that accurately summarizes the key points of the case study. Beyond that, the page itself is very light on content, and sections are as short as one paragraph.

I especially like how information is broken down into "What you need to know," "Why it matters," and "What the difference looks like." I'm not ashamed of being spoon-fed information. When it's structured so well and so simply, it makes for an entertaining read.

Takeaway: Invest in videos that capture and promote your partnership with your case study subject. Video content plays a promotional role that extends beyond the case study in social media and marketing initiatives .

14. Benchling and Airtable

Benchling is an impressive entity in its own right. Biotech R&D and health care nuances go right over my head. But the research and digging I've been doing in the name of these burritos (case studies) revealed that these products are immensely complex. 

And that's precisely why this case study deserves a read—it succeeds at explaining a complex project that readers outside the industry wouldn't know much about.

Takeaway: Simplify complex information, and walk readers through the company's operations and how your business helped streamline them.

15. Chipotle and Hubble

The concision of this case study is refreshing. It features two sections—the challenge and the solution—all in 316 words. This goes to show that your case study doesn't necessarily need to be a four-figure investment with video shoots and studio time. 

Sometimes, the message is simple and short enough to convey in a handful of paragraphs.

Takeaway: Consider what you should include instead of what you can include. Assess the time, resources, and effort you're able and willing to invest in a case study, and choose which elements you want to include from there.

16. Hudl and Zapier

I may be biased, but I'm a big fan of seeing metrics and achievements represented in branded graphics. It can be a jarring experience to navigate a website, then visit a case study page and feel as though you've gone to a completely different website.

The Zapier format provides nuggets of high-level insights, milestones, and achievements, as well as the challenge, solution, and results. My favorite part of this case study is how it's supplemented with a blog post detailing how Hudl uses Zapier automation to build a seamless user experience.

The case study is essentially the summary, and the blog article is the detailed analysis that provides context beyond X achievement or Y goal.

Takeaway: Keep your case study concise and informative. Create other resources to provide context under your blog, media or press, and product pages.

3 case study templates

Now that you've had your fill of case studies (if that's possible), I've got just what you need: an infinite number of case studies, which you can create yourself with these case study templates.

Case study template 1

If you've got a quick hit of stats you want to show off, try this template. The opening section gives space for a short summary and three visually appealing stats you can highlight, followed by a headline and body where you can break the case study down more thoroughly. This one's pretty simple, with only sections for solutions and results, but you can easily continue the formatting to add more sections as needed.

Case study template 2

For a case study template with a little more detail, use this one. Opening with a striking cover page for a quick overview, this one goes on to include context, stakeholders, challenges, multiple quote callouts, and quick-hit stats. 

Case study template 3

Whether you want a little structural variation or just like a nice dark green, this template has similar components to the last template but is designed to help tell a story. Move from the client overview through a description of your company before getting to the details of how you fixed said company's problems.

Tips for writing a case study

Examples are all well and good, but you don't learn how to make a burrito just by watching tutorials on YouTube without knowing what any of the ingredients are. You could , but it probably wouldn't be all that good.

Writing a good case study comes down to a mix of creativity, branding, and the capacity to invest in the project. With those details in mind, here are some case study tips to follow:

Have an objective: Define your objective by identifying the challenge, solution, and results. Assess your work with the client and focus on the most prominent wins. You're speaking to multiple businesses and industries through the case study, so make sure you know what you want to say to them.

Focus on persuasive data: Growth percentages and measurable results are your best friends. Extract your most compelling data and highlight it in your case study.

Use eye-grabbing graphics: Branded design goes a long way in accurately representing your brand and retaining readers as they review the study. Leverage unique and eye-catching graphics to keep readers engaged. 

Simplify data presentation: Some industries are more complex than others, and sometimes, data can be difficult to understand at a glance. Make sure you present your data in the simplest way possible. Make it concise, informative, and easy to understand.

Use automation to drive results for your case study

A case study example is a source of inspiration you can leverage to determine how to best position your brand's work. Find your unique angle, and refine it over time to help your business stand out. Ask anyone: the best burrito in town doesn't just appear at the number one spot. They find their angle (usually the house sauce) and leverage it to stand out.

In fact, with the right technology, it can be refined to work better . Explore how Zapier's automation features can help drive results for your case study by making your case study a part of a developed workflow that creates a user journey through your website, your case studies, and into the pipeline.

Case study FAQ

Got your case study template? Great—it's time to gather the team for an awkward semi-vague data collection task. While you do that, here are some case study quick answers for you to skim through while you contemplate what to call your team meeting.

What is an example of a case study?

An example of a case study is when a software company analyzes its results from a client project and creates a webpage, presentation, or document that focuses on high-level results, challenges, and solutions in an attempt to showcase effectiveness and promote the software.

How do you write a case study?

To write a good case study, you should have an objective, identify persuasive and compelling data, leverage graphics, and simplify data. Case studies typically include an analysis of the challenge, solution, and results of the partnership.

What is the format of a case study?

While case studies don't have a set format, they're often portrayed as reports or essays that inform readers about the partnership and its results. 

Related reading:

How Hudl uses automation to create a seamless user experience

How to make your case studies high-stakes—and why it matters

How experts write case studies that convert, not bore

Get productivity tips delivered straight to your inbox

We’ll email you 1-3 times per week—and never share your information.

Hachem Ramki

Hachem is a writer and digital marketer from Montreal. After graduating with a degree in English, Hachem spent seven years traveling around the world before moving to Canada. When he's not writing, he enjoys Basketball, Dungeons and Dragons, and playing music for friends and family.

• Content marketing

Related articles

10 examples of ethos in advertising to inspire your next campaign

10 examples of ethos in advertising to...

17 testimonial advertising examples to inspire your next campaign

17 testimonial advertising examples to...

B2B email marketing: Proven strategies and examples

B2B email marketing: Proven strategies and...

10 social media advertising examples to inspire your next campaign

10 social media advertising examples to...

Improve your productivity automatically. Use Zapier to get your apps working together.

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

• << Previous: Reviewing Collected Works
• Next: Writing a Case Study >>
• Last Updated: Mar 6, 2024 1:00 PM
• URL: https://libguides.usc.edu/writingguide/assignments

Writing A Case Study

Case Study Examples

Brilliant Case Study Examples and Templates For Your Help

15 min read

People also read

A Complete Case Study Writing Guide With Examples

Simple Case Study Format for Students to Follow

Understand the Types of Case Study Here

It’s no surprise that writing a case study is one of the most challenging academic tasks for students. You’re definitely not alone here!

Most people don't realize that there are specific guidelines to follow when writing a case study. If you don't know where to start, it's easy to get overwhelmed and give up before you even begin.

Don't worry! Let us help you out!

We've collected over 25 free case study examples with solutions just for you. These samples with solutions will help you win over your panel and score high marks on your case studies.

So, what are you waiting for? Let's dive in and learn the secrets to writing a successful case study.

• 1. An Overview of Case Studies
• 2. Case Study Examples for Students
• 3. Business Case Study Examples
• 4. Medical Case Study Examples
• 5. Psychology Case Study Examples 
• 6. Sales Case Study Examples
• 7. Interview Case Study Examples
• 8. Marketing Case Study Examples
• 9. Tips to Write a Good Case Study

An Overview of Case Studies

A case study is a research method used to study a particular individual, group, or situation in depth. It involves analyzing and interpreting data from a variety of sources to gain insight into the subject being studied. 

Case studies are often used in psychology, business, and education to explore complicated problems and find solutions. They usually have detailed descriptions of the subject, background info, and an analysis of the main issues.

The goal of a case study is to provide a comprehensive understanding of the subject. Typically, case studies can be divided into three parts, challenges, solutions, and results. 

Here is a case study sample PDF so you can have a clearer understanding of what a case study actually is:

Case Study Sample PDF

How to Write a Case Study Examples

Learn how to write a case study with the help of our comprehensive case study guide.

Case Study Examples for Students

Quite often, students are asked to present case studies in their academic journeys. The reason instructors assign case studies is for students to sharpen their critical analysis skills, understand how companies make profits, etc.

Below are some case study examples in research, suitable for students:

Case Study Example in Software Engineering

Qualitative Research Case Study Sample

Software Quality Assurance Case Study

Social Work Case Study Example

Ethical Case Study

Case Study Example PDF

These examples can guide you on how to structure and format your own case studies.

Struggling with formatting your case study? Check this case study format guide and perfect your document’s structure today.

Business Case Study Examples

A business case study examines a business’s specific challenge or goal and how it should be solved. Business case studies usually focus on several details related to the initial challenge and proposed solution. 

To help you out, here are some samples so you can create case studies that are related to businesses: 

Here are some more business case study examples:

Business Case Studies PDF

Business Case Studies Example

Typically, a business case study discovers one of your customer's stories and how you solved a problem for them. It allows your prospects to see how your solutions address their needs. 

Medical Case Study Examples

Medical case studies are an essential part of medical education. They help students to understand how to diagnose and treat patients. 

Here are some medical case study examples to help you.

Medical Case Study Example

Nursing Case Study Example

Want to understand the various types of case studies? Check out our types of case study blog to select the perfect type.

Psychology Case Study Examples 

Case studies are a great way of investigating individuals with psychological abnormalities. This is why it is a very common assignment in psychology courses. 

By examining all the aspects of your subject’s life, you discover the possible causes of exhibiting such behavior. 

For your help, here are some interesting psychology case study examples:

Psychology Case Study Example

Mental Health Case Study Example

Sales Case Study Examples

Case studies are important tools for sales teams’ performance improvement. By examining sales successes, teams can gain insights into effective strategies and create action plans to employ similar tactics.

By researching case studies of successful sales campaigns, sales teams can more accurately identify challenges and develop solutions.

Sales Case Study Example

Interview Case Study Examples

Interview case studies provide businesses with invaluable information. This data allows them to make informed decisions related to certain markets or subjects.

Interview Case Study Example

Marketing Case Study Examples

Marketing case studies are real-life stories that showcase how a business solves a problem. They typically discuss how a business achieves a goal using a specific marketing strategy or tactic.

They typically describe a challenge faced by a business, the solution implemented, and the results achieved.

This is a short sample marketing case study for you to get an idea of what an actual marketing case study looks like.

 Here are some more popular marketing studies that show how companies use case studies as a means of marketing and promotion:

“Chevrolet Discover the Unexpected” by Carol H. Williams

This case study explores Chevrolet's “ DTU Journalism Fellows ” program. The case study uses the initials “DTU” to generate interest and encourage readers to learn more. 

Multiple types of media, such as images and videos, are used to explain the challenges faced. The case study concludes with an overview of the achievements that were met.

Key points from the case study include:

• Using a well-known brand name in the title can create interest.
• Combining different media types, such as headings, images, and videos, can help engage readers and make the content more memorable.
• Providing a summary of the key achievements at the end of the case study can help readers better understand the project's impact.

“The Met” by Fantasy

“ The Met ” by Fantasy is a fictional redesign of the Metropolitan Museum of Art in New York City, created by the design studio Fantasy. The case study clearly and simply showcases the museum's website redesign.

The Met emphasizes the website’s features and interface by showcasing each section of the interface individually, allowing the readers to concentrate on the significant elements.

For those who prefer text, each feature includes an objective description. The case study also includes a “Contact Us” call-to-action at the bottom of the page, inviting visitors to contact the company.

Key points from this “The Met” include:

• Keeping the case study simple and clean can help readers focus on the most important aspects.
• Presenting the features and solutions with a visual showcase can be more effective than writing a lot of text.
• Including a clear call-to-action at the end of the case study can encourage visitors to contact the company for more information.

“Better Experiences for All” by Herman Miller

Herman Miller's minimalist approach to furniture design translates to their case study, “ Better Experiences for All ”, for a Dubai hospital. The page features a captivating video with closed-captioning and expandable text for accessibility.

The case study presents a wealth of information in a concise format, enabling users to grasp the complexities of the strategy with ease. It concludes with a client testimonial and a list of furniture items purchased from the brand.

Key points from the “Better Experiences” include:

• Make sure your case study is user-friendly by including accessibility features like closed captioning and expandable text.
• Include a list of products that were used in the project to guide potential customers.

“NetApp” by Evisort 

Evisort's case study on “ NetApp ” stands out for its informative and compelling approach. The study begins with a client-centric overview of NetApp, strategically directing attention to the client rather than the company or team involved.

The case study incorporates client quotes and explores NetApp’s challenges during COVID-19. Evisort showcases its value as a client partner by showing how its services supported NetApp through difficult times. 

• Provide an overview of the company in the client’s words, and put focus on the customer. 
• Highlight how your services can help clients during challenging times.
• Make your case study accessible by providing it in various formats.

“Red Sox Season Campaign,” by CTP Boston

The “ Red Sox Season Campaign ” showcases a perfect blend of different media, such as video, text, and images. Upon visiting the page, the video plays automatically, there are videos of Red Sox players, their images, and print ads that can be enlarged with a click.

The page features an intuitive design and invites viewers to appreciate CTP's well-rounded campaign for Boston's beloved baseball team. There’s also a CTA that prompts viewers to learn how CTP can create a similar campaign for their brand.

Some key points to take away from the “Red Sox Season Campaign”: 

• Including a variety of media such as video, images, and text can make your case study more engaging and compelling.
• Include a call-to-action at the end of your study that encourages viewers to take the next step towards becoming a customer or prospect.

“Airbnb + Zendesk” by Zendesk

The case study by Zendesk, titled “ Airbnb + Zendesk : Building a powerful solution together,” showcases a true partnership between Airbnb and Zendesk. 

The article begins with an intriguing opening statement, “Halfway around the globe is a place to stay with your name on it. At least for a weekend,” and uses stunning images of beautiful Airbnb locations to captivate readers.

Instead of solely highlighting Zendesk's product, the case study is crafted to tell a good story and highlight Airbnb's service in detail. This strategy makes the case study more authentic and relatable.

Some key points to take away from this case study are:

• Use client's offerings' images rather than just screenshots of your own product or service.
• To begin the case study, it is recommended to include a distinct CTA. For instance, Zendesk presents two alternatives, namely to initiate a trial or seek a solution.

“Influencer Marketing” by Trend and WarbyParker

The case study "Influencer Marketing" by Trend and Warby Parker highlights the potential of influencer content marketing, even when working with a limited budget. 

The “Wearing Warby” campaign involved influencers wearing Warby Parker glasses during their daily activities, providing a glimpse of the brand's products in use. 

This strategy enhanced the brand's relatability with influencers' followers. While not detailing specific tactics, the case study effectively illustrates the impact of third-person case studies in showcasing campaign results.

Key points to take away from this case study are:

• Influencer marketing can be effective even with a limited budget.
• Showcasing products being used in everyday life can make a brand more approachable and relatable.
• Third-person case studies can be useful in highlighting the success of a campaign.

Marketing Case Study Example

Marketing Case Study Template

Now that you have read multiple case study examples, hop on to our tips.

Tips to Write a Good Case Study

Here are some note-worthy tips to craft a winning case study 

• Define the purpose of the case study This will help you to focus on the most important aspects of the case. The case study objective helps to ensure that your finished product is concise and to the point.
• Choose a real-life example. One of the best ways to write a successful case study is to choose a real-life example. This will give your readers a chance to see how the concepts apply in a real-world setting.
• Keep it brief. This means that you should only include information that is directly relevant to your topic and avoid adding unnecessary details.
• Use strong evidence. To make your case study convincing, you will need to use strong evidence. This can include statistics, data from research studies, or quotes from experts in the field.
• Edit and proofread your work. Before you submit your case study, be sure to edit and proofread your work carefully. This will help to ensure that there are no errors and that your paper is clear and concise.

There you go!

We’re sure that now you have secrets to writing a great case study at your fingertips! This blog teaches the key guidelines of various case studies with samples. So grab your pen and start crafting a winning case study right away!

Having said that, we do understand that some of you might be having a hard time writing compelling case studies.

But worry not! Our expert case study writing service is here to take all your case-writing blues away! 

With 100% thorough research guaranteed, our online essay service can craft an amazing case study within 24 hours! 

So why delay? Let us help you shine in the eyes of your instructor!

Write Essay Within 60 Seconds!

Dr. Barbara is a highly experienced writer and author who holds a Ph.D. degree in public health from an Ivy League school. She has worked in the medical field for many years, conducting extensive research on various health topics. Her writing has been featured in several top-tier publications.

Paper Due? Why Suffer? That’s our Job!

Keep reading

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Share Podcast

Lessons from Amazon’s Early Growth Strategy

If you’re interested in strategies for scaling start-ups, this episode is for you.

• Apple Podcasts

So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention. Gupta has spent years studying Amazon’s strategy and its founder and former CEO Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users, instead of having a narrow focus.

He argues that some of Amazon’s simplest business strategies — like their obsession with customers and insistence on long-term thinking — are approaches that companies, big and small, can emulate.

Key episode topics include: strategy, innovation, leadership, scaling, Jeff Bezos, long-term thinking, customer focus.

HBR On Strategy curates the best case studies and conversations with the world’s top business and management experts, to help you unlock new ways of doing business. New episodes every week.

• Listen to the full HBR IdeaCast episode: How Jeff Bezos Built One of the World’s Most Valuable Companies (2020)
• Find more episodes of HBR IdeaCast
• Discover 100 years of Harvard Business Review articles, case studies, podcasts, and more at HBR.org .

HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business.

So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention.

Gupta has spent years studying Amazon’s strategy and its founder and former CEO, Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users instead of focusing more narrowly.

And he argues that some of their simplest business strategies – like their obsession with the customer and insistence on long-term thinking – are approaches that companies, big and small, should emulate.

If you’re interested in innovation strategy, this episode is for you. It originally aired on HBR IdeaCast in November 2020. Here it is.

ALISON BEARD:  Welcome to the HBR IdeaCast from Harvard Business Review.  I’m Alison Beard.

If you had to name the most successful business leader alive today, who would you say?  I can’t hear you from my basement podcasting room, but I would bet that for many of you, the answer is Jeff Bezos, CEO of Amazon.  This is a man who over the past 25 years turned his online bookstore startup into a diversified company currently valued at $1.6 trillion.

Amazon is a digital retailing juggernaut, it’s also a web services provider, media producer, and manufacturer of personal technology devices like Kindle and Echo.  Oh, and Bezos also owns the Washington Post and Blue Origin, a space exploration company.  Forbes tells us he is the richest person in the world.

How did he accomplish so much?  How did he change the business landscape?  What mistakes has he made along the way?  A new collection of Bezos’s own writing, which full disclosure, my colleagues at Harvard Business Review Press have published, offer some insights.  Here’s a clip from one speech that’s included.  The book is called Invent and Wander.

And our guest today, who has spent years studying both Amazon and Bezos, is here to talk with me about some of the key themes in it, including the broad drivers of both the company and the CEO’s success.  Sunil Gupta is a professor of business administration at Harvard Business School and cochair of its executive program, and cochair of its executive program on driving digital strategy, which is also the title of his book.  Sunil, thanks so much for being on the show.

SUNIL GUPTA:  Thank you for having me, Alison.

ALISON BEARD:  So Invent and Wander.  I get that Bezos is inventive.  You know, he created a new way for us to buy things – everything.  How is he also a wonderer?

SUNIL GUPTA:  So he’s full of experiments.  His company and his whole style is known for experimentation, and he says that in so many words that if you want big winners, then you have to be willing to have many failures.  And the argument is, one big winner will take care of a thousand failed experiments.  So I think that’s the wandering part.  But also his experiments are not aimless.  There is a certain thought and process behind what experiments to do and why they will connect to the old, old picture of what Amazon is today.

ALISON BEARD:  And your expertise is in digital strategy.  How does he break the traditional rules of strategy?

SUNIL GUPTA:  So for the longest time the way, at least I was taught in my MBA program and the way we teach to our MBA students and executives, is strategy is about focus.  But if you look at Amazon, Amazon certainly doesn’t look like it’s focusing on anything, so obviously Jeff Bezos missed that class, otherwise it’s a very, very different thing.

And then you’d say, why is it that so called lack of focus strategy seems to be working for Amazon?  And I think the fundamental underlying principle that he’s guiding his whole discussion of strategy is, he’s changed the rules of strategy.  So the old rules of strategy were, the way you gained competitive advantage is by being better or cheaper.  So if I am selling you a car, my car is better of cheaper.  But the inherent assumption in that strategy statement is, I’m selling one product to one customer.  And what Amazon is basically arguing is, the digital economy is all about connection.  We have got to connect products and connect customers.  Let me explain why that is so powerful.

So connecting products, here the idea is, I can sell you, this is a classic razor and blade strategy.  I can sell you a razor cheap in order to make money on the blade.  So I can sell you Kindle cheap in order to make money on the ebooks.  Now, at some level you might say, hey, razor and blade have been around forever.  What’s so unique today?  I think unique today is razor could be in one industry and blades could be in completely different industrys.

So for example, if you look at Amazon’s portfolio of businesses, you sort of say, not only Amazon is an e-commerce player, but also is making movies and TV shows, its own studio.  Well, why does it make sense for an e-commerce player, an online retailer to compete with Hollywood.  Well, Walmart doesn’t make movies.  Macy’s doesn’t make movies?  So why does it make sense for Amazon to make movies?

And I think once you dig into it, the answer becomes clear that the purpose of the movies is to keep and gain the Prime customers. Two day free shipping is fine, but if  you ask me to pay $99 or $119 for two day free shipping, I might start doing the math in my head, and say, OK, how many packages do I expect to get next year?  And is the Prime membership worth it or not?

But once you throw in, in addition to the two-day free shipping, you throw in some TV shows and movies that are uniquely found only on Amazon, I can’t do this math.  And why is Prime customers important to Amazon?  Because Prime customers are more loyal.  They buy three or four times more than the non-Prime customers, and they’re also less price sensitive.

And in fact, Jeff Bezos has said publicly that every time we win a Golden Globe Award for one of our shows, we sell more shoes.  So this is, and he said it in your book, Invent and Wander, also, that we might be the only company in the world which has figured out how winning Golden Globe Awards can actually translate into selling more products on the online commerce.

So this is a great example of the razor being in a very different industry and blade being in another industry.  Take another example.  Amazon has a lending business where they give loans to small and medium enterprises. If Amazon decides to compete with banks tomorrow, Amazon can decide to offer loans to the small merchants at such a low price that banks would never be able to compete.  And why would Amazon be able to do that?  Because Amazon can say, hey, I’m not going to make money on loans, as much money on loans, but I’ll make more money when these businesses, small businesses grow and do more transactions on my marketplace platform.  And I get more commissions.  So again, loan can become my razor in order to help the merchants grow and make money on the transaction and the commission that I get from that.  The moment I make somebody else’s, in this case the banks, core business my razor, they will make a very hard time competing.  So I think that’s the key change, the fundamental rules of strategy and competition in that direction.

The second part of connection is connecting customers, and this is the classic network effect.  So marketplace is a great example of network effects.  The more buyers I have, the more sellers I have.  The more sellers I have, the sellers I have, the more buyers I get, because the buyers can find all the items.  And that becomes flywheel effect, and it becomes a situation where it’s very hard for a new player to complete with Amazon.

ALISON BEARD:  In this diversification that Amazon has done, how have they managed to be good at all of those things?  Because they’re not focused.  You know, they’re not concentrated on an area of specific expertise.  So how have they succeeded when other companies might have failed because they lacked that expertise, or they were spreading themselves too thin?

SUNIL GUPTA:  So I think it depends on how you define focus.  Most of us, when we define focus, we sort of define focus by traditional industry boundaries, that I’m an online retailer, therefore going into some other business is lack of focus.  The way Amazon thinks about is focus on capabilities.

So if you look at it from that point of view, I would argue that Amazon had three fundamental core capabilities.  Number one, it’s highly customer focused, not only in its culture, but also in its capability in terms of how it can actually handle data and leverage data to get customer insight.  The second core capability of Amazon is logistics.  So it’s now a world class logistics player.  It uses really frontier technology, whether it’s key word, robotics, computer vision, in its warehouse to make it much more efficient.

And the third part of Amazon’s skill or the capability is its technology.  And a good example of that is Amazon Web Services, or AWS.  And I think if you look at these three core capabilities, customer focus and the data insight that it gets from that, the logistics capability, and the technology, everything that Amazon is doing is some way or the other connected to it.  In that sense, Amazon, and there’s no lack of focus, in my judgment on Amazon.

Now, if he starts doing, starts making cream cheese tomorrow or starts making airplane engines, then I would say, yes, it’s got a lack of focus.  But one of the other things that Jeff Bezos has said again and again is this notion of work backwards and scale forward.  And what that means is, because you’re customer obsessed, you sort of find ways to satisfy customers, and if that means developing new skills that we don’t have because we are working backwards from what the customer needs are, then we’ll build those skills.

So a good example of that is, when Amazon started building Kindle, Amazon was never in the hardware business.  It didn’t know how to build hardware.  But Bezos realized that as the industry moved, people are beginning to read more and more online, rather, or at least on their devices, rather than the physical paper copy of a book.  So as a result, he says, how do we make it easier for consumers to read it on an electronic version?  And they’re spending three years learning about this capability of hardware manufacturing.  And by the way, Kindle came out long before iPad came out.  And of course, that capability now has helped them launch Echo and many other devices.

ALISON BEARD:  Right.  So it’s the focus on the customer, plus a willingness to go outside your comfort zone, the wander part.

SUNIL GUPTA:  Exactly.

ALISON BEARD:  Yeah.  How would you describe Bezos’s leadership style?

SUNIL GUPTA:  So I think there are at least three parts to it.  One is, he said right from day one that he wants to be a long-term focus.  The second thing is being customer obsessed.  And many times he has said that he can imagine, in the meetings he wants people to imagine an empty chair.  That is basically for the customer. And he says, we are not competitor focused.  We are not product focused.  We are not technology focused.  We are customer focused.  And the third is, willingness to experiment.  And fail, and build that culture in the company that it’s OK to fail.

ALISON BEARD:  What about personally, though?  Is he a hard charger?  Is he an active listener?  What’s it like to be in a room with him?

SUNIL GUPTA:  Oh, he’s certainly a hard charger.  I mean, he’s also the kind of guy, when he hires people, he says, you can work long, hard, or smart.  But at Amazon, you can choose two out of three.  And I think this is similar to many other leaders.  If you look at Steve Jobs, he was also a very hard charging guy.  And I think some people find it exhilarating to work with these kind of leaders.  Some find it very tough.

ALISON BEARD:  Do you think that he communicates differently from other successful CEOs?

SUNIL GUPTA:  So the communication style that he has built in the company is the very famous now, there’s no PowerPoints.  So it’s a very thoughtful discussion.  You write six-page memos, which everybody, when their meeting starts, everybody sits down and actually reads the memo.

In fact, this was a very interesting experience that I had.  One of my students, who was in the executive program, works at Amazon in Germany.  And he is, he was at that point in time thinking of moving to another company and becoming a CEO of that company.  So he said, can I talk to you about this change of career path that I’m thinking about?  I said, sure.  So we set up a time, and five minutes before our call, he sends me an email with a six-page memo.  And I said, well, shouldn’t he have sent this to me before, so I could at least look at it?  He says, no, that’s the Amazon style.  We’ll sit in silence and read it together.  And so I read it together, because then you’re completely focused on it.  And then we can have a conversation.  But this discipline of writing a six-page memo, it’s a very, very unique experience, because you actually have to think through all your arguments.

ALISON BEARD:  You also mentioned the long term focus, and that really stood out for me, too, this idea that he is not at all thinking of next year.  He’s thinking five years out, and sometimes even further.  But as a public company, how has Amazon been able to stick to that?  And is it replicable at other companies?

SUNIL GUPTA:  I think it is replicable.  It requires conviction, and it requires a way to articulate the vision to Wall Street that they can rally behind.  And it’s completely replicable.  There are other examples of companies who have followed a similar strategy.  I mean, Netflix is a good example.  Netflix hadn’t made money for a long period of time.  But they sold the vision of what the future will look like, and Wall Street bought that vision.

Mastercard is exactly the same thing.  Ajay Banga is giving three year guidance to Wall Street saying, this is my three-year plan, because things can change quarter to quarter.  I’m still responsible to tell you what we are doing this quarter, but my strategy will not be guided by what happens today.  It will be guided by the three-year plan that we have.

ALISON BEARD:  There are so many companies now that go public without turning any profit, whereas Amazon now is printing money, and thus able to reinvest and have this grand vision.  So at what point was Bezos able to say, right, we’re going to do it my way?

SUNIL GUPTA:  I think he said it right from day one, except that people probably didn’t believe it.  And in fact, one of the great examples of that was, when he was convinced about AWS, the Amazon Web Services, that was back in the early 2000s, when a majority of the Wall Street was not sure what Jeff Bezos was trying to do, because they say, hey, you are an online retailer.  You have no business being in web services.  That’s the business of IBM.  And that’s a B2B business.  You’re in a B2C business.  Why are you going in there?

And Bezos said, well, we have plenty of practice of being misunderstood.  And we will continue with our passion and vision, because we see the path.  And now he’s proven it again and again why his vision is correct, and I think that could give us more faith and conviction to the Wall Street investors.

SUNIL GUPTA:  Oh, absolutely.  And he’s one of the persons who has his opinion, and you always surround yourself with people better than you.

ALISON BEARD:  How has he managed to attract that talent when it is so fiercely competitive between Google, Facebook, all of these U.S. technology leaders?

SUNIL GUPTA:  So a couple of things I would say.  First of all, it’s always good fun to join a winning team.  And all of us want to join a winning team, so this certainly is on a trajectory which is phenomenal.  It’s like a rocket ship that is taking off and has been taking off for the last 25 years.  So I think that’s certainly attractive to many people, and certainly many hard charging people who want to be on a winning team.

And a second thing is, Amazon’s culture of experimentation and innovation.  That is energizing to a lot of people.  It’s not a bureaucracy where you get bogged down by the processes.  So the two type of decisions that we talked about, he gives you enough leeway to try different things, and is willing to invest hundreds of millions of dollars into things that may or may not succeed in the future.  And I think that’s very liberating to people who are willing to take on the ownership and build something.

ALISON BEARD:  But don’t all of the tech companies offer that?

SUNIL GUPTA:  They do, but if you think about many other tech companies, they’re much more narrow in focus.  So Facebook is primarily in social media.  Google is primarily in search advertising.  Yes, you have GoogleX, but that’s still a small part of what Google does.  Whereas if you ask yourself what business is Amazon in, there are much broader expansive areas that Amazon has gone into.  So I think the limits, I mean, Amazon does not have that many limits or boundaries as compared to many other businesses in Silicon Valley.

ALISON BEARD:  So let’s talk a little bit about Bezos’s acquisition strategy.  I think the most prominent is probably Whole Foods, but there are many others.  How does he think about the companies that he wants to bring in as opposed to grow organically?

SUNIL GUPTA:  So some acquisitions are areas where he thinks that he can actually benefit and accelerate the vision that he already has.  So for example, the acquisition of Kiva was to improve the efficiency and effectiveness of the systems that he already put in place in his warehouse.  And logistics and warehouse is a key component or key part of Amazon’s business, and he saw that Kiva already was ahead of the curve in technology that he probably wanted to have that in his own company.  So that was obvious acquisition, because that fits in the existing business.

Whole Foods is kind of a slightly different story, in my judgment, because I some ways, you can argue, why is Amazon, an online player, buying an offline retail store, Whole Foods?  And in fact, they bought it at 27% premium.  So that doesn’t make sense for an online retailer commerce to go to offline channels.  And I think, in fact, part of the reason in my judgment is, it’s not just Whole Foods, but it’s about the food business, per se.  And why is Amazon so interested in food?  In fact, Amazon has been trying this food business, online food delivery for a long period of time without much success.  And Whole Foods was one, another way to try and get access to that particular business.  And why is that so important to Amazon, even though you could argue, food is a low margin business?

And I would say, part of the reason is, food is something, grocery is something that you buy every week, perhaps twice a week.  And if I, as Amazon, can convince you to buy grocery online from Amazon, then I’m creating a habit for you to come onto Amazon every week, perhaps twice a week.  And once you are on Amazon, you will end up buying other products on Amazon.  Whereas if you are buying electronics, you may not come to Amazon every day.

So this is a habit creation activity, and again, it may not be a very high margin activity to sell you food.  But I’ve created a habit, just like Prime.  I’ve created a loyal customer where you think of nothing else but Amazon for your daily needs, and therefore you end up buying other things.

ALISON BEARD:  And Amazon isn’t without controversy.  You know, and we should talk about that, too.  First, there are questions about its treatment of warehouse employees, particularly during COVID.  And Bezos, as you said, has always been relentlessly focused on the customer.  But is Amazon employee centric, too?

SUNIL GUPTA:  So I think there is definitely some areas of concern, and you rightly said there is a significant concern about the, during the COVID, workers were complaining about safety, the right kind of equipment.  But even before COVID, there were a lot of concerns about whether the workers are being pushed too hard.  They barely have any breaks.  And they’re constantly on the go, because speed and efficiency become that much more important to make sure customers always get what they are promised.  And in fact, more than promised.

Clearly Amazon either hasn’t done a good job, or hasn’t at least done the public relations part of it that they have done a good job.  Now, if you ask Jeff Bezos, he will claim that, no, actually, they have done things.  For example, they offer something called carrier choice, where they give 95% tuition to the employees to learn new skills, whether they’re relevant to Amazon or not.  Pretty much like what Starbucks does for its baristas, for college education and other things.  But I think more than just giving money or tuition, it requires a bit of empathy and sense that you care for your employees, and perhaps that needs, that’s something that Amazon needs to work on.

ALISON BEARD:  And another challenge is the criticism that it has decimated mom and pop shops.  Even when someone sells through Amazon, the company will then see that it’s a popular category and create it itself and start selling it itself.  There’s environmental concerns about the fact that packages are being driven from warehouses to front doors all over America.  And boxes and packaging.  So how has Bezos, how has the company dealt with all of that criticism?

SUNIL GUPTA:  They haven’t.  And I think those are absolutely valid concerns on both counts, that the small sellers who grow to become reasonably big are always under the radar, and there are certainly anecdotal evidence there, small sellers have complained that Amazon had decided to sell exactly the same item that they were so successful in selling, and becoming too big is actually not good on Amazon, because Amazon can get into your business and wipe you away.  So that’s certainly a big concern, and I think that’s something that needs to be sorted out, and Amazon needs to clarify what its position on that area is, because it benefits from these small sellers on his platform.

And your second question about environmental issues is also absolutely on the money, because not only emission issues, but there’s so many boxes that pile in, certainly in my basement, from Amazon.  You sort of say, and it’s actually ironical that Millennials who are in love with Amazon are extremely environmentally friendly.  But at the same time, they would not hesitate to order something from Amazon and pile up all these boxes.  So I think Amazon needs to figure out a way to think about both those issues.

ALISON BEARD:  And at what point will it have to?  I mean, it seems to be rolling happily along.

SUNIL GUPTA:  Well, I think those issues are becoming bigger and bigger, and it’s certainly in the eye of the regulators, also, for some of these practices.  And not only because it’s too big, and there might be monopoly concerns, but these issues will become larger, and any time you become a large company, you become the center of attraction for broader issues than just providing shareholder value.

ALISON BEARD:  Yeah.  So those are weaknesses possibly for the company.  What are some of Bezos’s personal weaknesses that you’ve seen in studying him and the company?

SUNIL GUPTA:  So I think one thing that stands out to me, and at least in the public forums, I have not seen any empathy.  And it’s, I mean, we talk about that the leaders have, should have three qualities.  They should be competent.  They should have a good character.  And they should have compassion.  So he’s certainly very competent.  I mean, he’s brilliant in many aspects, right, from the computer vision and AI and machine learning, to the nuances of data analytics, to the Hollywood production, etc.  He also seems to have good character, at least I have not heard any personal scandals, apart from his other issues in his personal life, perhaps.

Those characteristics of competence and character make people respect you.  What makes people love you is when you show compassion, and at least I haven’t seen compassion or empathy that comes out of him.  I mean, he certainly comes across as a very hard charging, driven person, which probably is good for business.  But the question of empathy is perhaps something lacking right now.

ALISON BEARD:  Yeah.  The other issue is his just enormous wealth.  He did invent this colossally valuable company, but should anyone really be that rich?

SUNIL GUPTA:  Well, I guess that’s, you can say that’s the good or the bad thing about capitalism.  But I think, and again, my personal view is there’s nothing wrong in becoming rich, if you have been successful and done it with hard work and ingenuity.  But how you use your wealth is something that perhaps will define Jeff Bezos going forward.  I think Bill Gates is a great example how he actually has used his wealth and his influence and his expertise and his brilliance into some certain thing that actually is great for humanity.

Now, whether Jeff Bezos does that down the road, I don’t know, whether his space exploration provides that sort of outlet which is both his passion as well as good for humanity, I don’t know.  But at some point in time, I think it’s the responsibility of these leaders to sort of say, my goal is not simply to make money and make my shareholders rich, but also help humanity and help society.

ALISON BEARD:  If you’re talking to someone who’s running a startup, or even a manager of a team at a traditional company, what is the key lesson that you would say, this is what you can learn from Jeff Bezos?  This is what you can put to work in your own profession?

SUNIL GUPTA:  So I would say two things that at least I would take away if I were doing a startup.  One is customer obsession.  Now, every company says that, but honestly, not every company does it, because if you go to the management meetings, if you go to the quarterly meetings, you suddenly go focus on financials and competition and product.  But there’s rarely any conversation on customers.  And I think, as I mentioned earlier, that Jeff Bezos always tells his employee to think of the imaginary chair in which a customer is sitting, because that’s the person that we need to focus on.  Howard Shultz does the same thing at Starbucks, and that’s why Starbucks is so customer focused.

So I think that’s the first part.  And the argument that Bezos gives is, customers are never satisfied.  And that pushes us to innovate and move forward, so we need to innovate even before the rest of the world even sees that, because customers are the first ones to see what is missing in the offering that you have.

And the second I would say that I would take away from Jeff Bezos is the conviction and passion with what you do.  And many times that goes against the conventional wisdom.  And the Amazon Web Services is a great example of that.  The whole world, including the Wall Street Journal and the Wall Street analysts were saying, this is none of Amazon’s business to do web services.  But he was convinced that this is the right thing to do, and he went and did that.

And part of that conviction may come from experiments.  Part of that conviction comes from connecting the dots that he could see that many other people didn’t see.  I mean, that’s why he went, left his job, and went to Seattle to do the online bookstore, because he could see the macro trends as to what the Internet is likely to do.  So, I think that’s the vision that he had.  And once you have the conviction, then you follow your passion.

ALISON BEARD: Sunil, thanks so much for coming on the show.

SUNIL GUPTA:  Thank you for having me. Alison.

HANNAH BATES: That was Harvard Business School professor Sunil Gupta, in conversation with Alison Beard on the HBR IdeaCast .

We’ll be back next Wednesday with another hand-picked conversation about business strategy from Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

And when you’re ready for more podcasts, articles, case studies, books, and videos with the world’s top business and management experts, find it all at HBR.org.

This episode was produced by Mary Dooe, Anne Saini, and me, Hannah Bates. Ian Fox is our editor. And special thanks to Maureen Hoch, Nicole Smith, Erica Truxler, Ramsey Khabbaz, Anne Bartholomew, and you – our listener. See you next week.

• Subscribe On:

Latest in this series

This article is about strategy, partner center.

• Open access
• Published: 17 April 2024

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

• Weiwei Zhang 1 &
• Xinchun Li 1  

BMC Health Services Research volume  24 , Article number:  477 ( 2024 ) Cite this article

246 Accesses

1 Altmetric

Metrics details

Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.

This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.

A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.

Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.

The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Peer Review reports

Introduction

The outbreak of severe acute respiratory syndrome (SARS) in 2003 was the first global public health emergency of the 21st century. From SARS to the coronavirus disease (COVID-19) pandemic at the end of 2019, followed shortly by the monkeypox epidemic of 2022, the global community has witnessed eight major public health events within the span of only 20 years [ 1 ]. These events are all characterized by high infection and fatality rates. For example, the number of confirmed COVID-19 cases worldwide is over 700 million, and the number of deaths has exceeded 7 million [ 2 ]. Every major public health emergency typically consists of four stages: incubation, outbreak, peak, and decline. During the outbreak and transmission, surges in the number of infected individuals and the number of critically ill patients led to a corresponding increase in the urgent demand for intensive care unit (ICU) medical resources. ICU healthcare resources provide material security for rescue work during major public health events as they allow critically ill patients to be treated, which decreases the case fatality rate and facilitates the prevention and control of epidemics. Nevertheless, in actual cases of prevention and control, the surge in patients has often led to shortages of ICU healthcare resources and a short-term mismatch of supply and demand, which are problems that have occurred several times in different regions. These issues can drastically impact anti-epidemic frontline healthcare workers and the treatment outcomes of infected patients. According to COVID-19 data from recent years, many infected individuals take about two weeks to progress from mild to severe disease. As the peak of severe cases tends to lag behind that of infected cases, predicting the changes in the number of new infections can serve as a valuable reference for healthcare institutions in forecasting the demand for ICU healthcare resources. The accurate forecasting of the demand for ICU healthcare resources can facilitate the rational resource allocation of hospitals under changes in demand patterns, which is crucial for improving the provision of critical care and rescue efficiency. Therefore, in this study, we combined a support vector regression (SVR) prediction model optimized by a genetic algorithm (GA) with bidirectional long-short-term memory (BILSTM), with the aim of enhancing the dynamic and accurate prediction of the number of current confirmed cases. Based on this, we forecasted the demand for ICU healthcare resources, which in turn may enable more efficient resource deployment during severe epidemic outbreaks and improve the precise supply of ICU healthcare resources.

Research on the demand forecasting of emergency materials generally employs quantitative methods, and traditional approaches mainly include linear regression and GM (1,1). Linear regression involves the use of regression equations to make predictions based on data. Sui et al. proposed a method based on multiple regression that aimed to predict the demand for emergency supplies in the power grid system following natural disasters [ 3 ]. Historical data was used to obtain the impact coefficient of each factor on emergency resource forecasting, enabling the quick calculation of the demand for each emergency resource during a given type of disaster. However, to ensure prediction accuracy, regression analysis needs to be supported by data from a large sample size. Other researchers have carried out demand forecasting for emergency supplies from the perspective of grey prediction models. Li et al. calculated the development coefficient and grey action of the grey GM (1,1) model using the particle swarm optimization algorithm to minimize the relative errors between the real and predicted values [ 4 ]. Although these studies have improved the prediction accuracy of grey models, they mainly involve pre-processing the initial data series without considering the issue of the excessively fast increase in predicted values by traditional grey GM (1,1) models. In emergency situations, the excessively fast increase in predicted values compared to real values will result in the consumption of a large number of unnecessary resources, thereby decreasing efficiency and increasing costs. As traditional demand forecasting models for emergency supplies have relatively poor perfect order rates in demand analysis, which result in low prediction accuracy, they are not mainstream.

At present, dynamic models of infectious diseases and demand forecasting models based on machine learning are at the cutting edge of research. With regard to the dynamic models of infectious diseases, susceptible infected recovered model (SIR) is a classic mathematical model employed by researchers [ 5 , 6 , 7 ]. After many years of development, the SIR model has been expanded into various forms within the field of disease transmission, including susceptible exposed infected recovered model (SEIR) and susceptible exposed infected recovered dead model (SEIRD) [ 8 , 9 ]. Nevertheless, with the outbreak of COVID-19, dynamic models of infectious diseases have once again come under the spotlight, with researchers combining individual and group variables and accounting for different factors to improve the initial models and reflect the state of COVID-19 [ 10 , 11 , 12 , 13 ]. Based on the first round of epidemic data from Wuhan, Li et al. predicted the time-delay distributions, epidemic doubling time, and basic reproductive number [ 14 ]. Upon discovering the presence of asymptomatic COVID-19 infections, researchers began constructing different SEIR models that considered the infectivity of various viral incubation periods, yielding their respective predictions of the inflection point. Based on this, Anggriani et al. further considered the impact of the status of infected individuals and established a transmission model with seven compartments [ 15 ]. Efimov et al. set the model parameters for separating the recovered and the dead as uncertain and applied the improved SEIR model to analyze the transmission trend of the pandemic [ 16 ]. In addition to analyzing the transmission characteristics of normal COVID-19 infection to predict the status of the epidemic, many researchers have also used infectious disease models to evaluate the effects of various epidemic preventive measures. Lin et al. applied an SEIR model that considered individual behavioral responses, government restrictions on public gatherings, pet-related transmission, and short-term population movements [ 17 ]. Cao et al. considered the containment effect of isolation measures on the pandemic and solved the model using Euler’s numerical method [ 18 ]. Reiner et al. employed an improved SEIR model to study the impact of non-pharmaceutical interventions implemented by the government (e.g., restricting population movement, enhancing disease testing, and increasing mask use) on disease transmission and evaluated the effectiveness of social distancing and the closure of public spaces [ 19 ]. These studies have mainly focused on modeling the COVID-19 pandemic to perform dynamic forecasting and analyze the effectiveness of control measures during the epidemic. Infectious disease dynamics offer good predictions for the early transmission trends of epidemics. However, this approach is unable to accurately estimate the spread of the virus in open-flow environments. Furthermore, it is also impossible to set hypothetical parameters, such as disease transmissibility and the recovery probability constant, that are consistent with the conditions in reality. Hence, with the increase in COVID-19 data, this approach has become inadequate for the accurate long-term analysis of epidemic trends.

Machine learning has shown significant advantages in this regard [ 20 , 21 ]. Some researchers have adopted the classic case-based reasoning approach in machine learning to make predictions. However, it is not feasible to find historical cases that fully match the current emergency event, so this approach has limited operability. Other researchers have also employed neural network training in machine learning to make predictions. For example, Hamou et al. predicted the number of injuries and deaths, which in turn were used to forecast the demand for emergency supplies [ 22 ]. However, this approach requires a large initial dataset and a high number of training epochs, while uncertainty due to large changes in intelligence information can lead to significant errors in data prediction [ 23 , 24 , 25 ]. To address these problems, researchers have conducted investigations that account (to varying degrees) for data characterized by time-series and non-linearity and have employed time-series models with good non-linear fitting [ 26 , 27 , 28 ]. The use of LSTM to explore relationships within the data can improve the accuracy of predicting COVID-19 to some extent. However, there are two problems with this approach. First, LSTM neural networks require extremely large datasets, and each wave of the epidemic development cycle would be insufficient to support a dataset suitable for LSTM. Second, neural networks involve a large number of parameters and highly complex models and, hence, are susceptible to overfitting, which can prevent them from achieving their true and expected advantages in prediction.

Overall, Our study differs from other papers in the following three ways. First, the research object of this paper focuses on the specific point of ICU healthcare resource demand prediction, aiming to improve the rate of critical care patient treatment. However, past research on public health emergencies has focused more on resource prediction , such as N95 masks, vaccines, and generalized medical supplies during the epidemic , to mitigate the impact of rapid transmission and high morbidity rates. This has led to less attention being paid to the reality of the surge in critically ill patients due to their high rates of severe illness and mortality.

Second, the idea of this paper is to further forecast resource needs based on the projected number of people with confirmed diagnoses, which is more applicable to healthcare organizations than most other papers that only predict the number of people involved. However, in terms of the methodology for projecting the number of people, this paper adopts a combined prediction method that combines regression algorithms and recurrent neural networks to propose a BILSTM-GASVR prediction model for the number of confirmed diagnoses. It capitalizes on both the suitability of SVR for small samples and non-linear prediction as well as the learning and memory abilities of BILSTM in processing time-series data. On the basis of the prediction model for the number of infected cases, by considering the characteristics of ICU healthcare resources, we constructed a demand forecasting model of emergency healthcare supplies. Past public health emergencies are more likely to use infectious disease models or a single prediction model in deep learning. some of the articles, although using a combination of prediction, but also more for the same method domain combination, such as CNN-LSTM, GRU-LSTM, etc., which are all recurrent neural networks.

Third, in terms of specific categorization of resources to be forecasted, considering the specificity of ICU medical resources, we introduce human resource prediction on the basis of previous studies focusing on material security, and classified ICU medical resources into three categories: ICU human resources, drugs and medical equipment. The purpose of this classification is to match the real-life prediction scenarios of public health emergencies and improve the demand forecasting performance for local ICU healthcare resources. Thus, it is easy for healthcare institutions to grasp the overall development of events, optimizing decision-making, and reducing the risk of healthcare systems collapsing during the outbreak stage.

In this section, we accomplish the following two tasks. Firstly, we introduce the idea of predicting the number of infected cases and show the principle of the relevant models. Secondly, based on the number of infected cases, ICU healthcare resources are divided into two categories (healthcare workers and healthcare supplies), and their respective demand forecasting models are constructed.

Prediction model for the number of infected cases

Gasvr model.

Support vector machine (SVM) is a machine-learning language for classification developed by Vapnik [ 29 ]. Suppose there are two categories of samples: H1 and H2. If hyperplane H is able to correctly classify the samples into these two categories and maximize the margin between the two categories, it is known as the optimal separating hyperplane (OSH). The sample vectors closest to the OSH in H1 and H2 are known as the support vectors. To apply SVM to prediction, it is essential to perform regression fitting. By introducing the \(\varepsilon\) -insensitive loss function, SVM can be converted to a support vector regression machine, where the role of the OSH is to minimize the error of all samples from this plane. SVR has a theoretical basis in statistical learning and relatively high learning performance, making it suitable for performing predictions in small-sample, non-linear, and multi-dimensional fields [ 30 , 31 ].

Assume the training sample set containing \(l\) training samples is given by \(\{({x}_{i},{y}_{i}),i=\mathrm{1,2},...,l\}\) , where \({x}_{i}=[{x}_{i}^{1},{x}_{i}^{2},...,{x}_{i}^{d}{]}^{\rm T}\) and \({y}_{i}\in R\) are the corresponding output values.

Let the regression function be \(f(x)=w\Phi (x)+b\) , where \(\phi (x)\) is the non-linear mapping function. The linear \(\varepsilon\) -insensitive loss function is defined as shown in formula ( 1 ).

Among the rest, \(f(x)\) is the predicted value returned by the regression function, and \(y\) is the corresponding real value. If the error between \(f(x)\) and \(y\) is ≤ \(\varepsilon\) , the loss is 0; otherwise, the loss is \(\left|y-f(x)\right|-\varepsilon\) .

The slack variables \({\xi }_{i}\) and \({\xi }_{i}^{*}\) are introduced, and \(w\) , \(b\) are solved using the following equation as shown in formula ( 2 ).

Among the rest, \(C\) is the penalty factor, with larger values indicating a greater penalty for errors > \(\varepsilon\) ; \(\varepsilon\) is defined as the error requirement, with smaller values indicating a smaller error of the regression function.

The Lagrange function is introduced to solve the above function and transformed into the dual form to give the formula ( 3 ).

Among the rest, \(K({x}_{i},{x}_{j})=\Phi ({x}_{i})\Phi ({x}_{j})\) is the kernel function. The kernel function determines the structure of high-dimensional feature space and the complexity of the final solution. The Gaussian kernel is selected for this study with the function \(K({x}_{i},{x}_{j})=\mathit{exp}(-\frac{\Vert {x}_{i}-{x}_{j}\Vert }{2{\sigma }^{2}})\) .

Let the optimal solution be \(a=[{a}_{1},{a}_{2},...,{a}_{l}]\) and \({a}^{*}=[{a}_{1}^{*},{a}_{2}^{*},...,{a}_{l}]\) to give the formula ( 4 ) and formula ( 5 ).

Among the rest, \({N}_{nsv}\) is the number of support vectors.

In sum, the regression function is as shown in formula ( 6 ).

when some of the parameters are not 0, the corresponding samples are the support vectors in the problem. This is the principle of SVR. The values of the three unknown parameters (penalty factor C, ε -insensitive loss function, and kernel function coefficient \(\sigma )\) , can directly impact the model effect. The penalty factor C affects the degree of function fitting through the selection of outliers in the sample by the function. Thus, excessively large values lead to better fit but poorer generalization, and vice versa. The ε value in the ε-insensitive loss function determines the accuracy of the model by affecting the width of support vector selection. Thus, excessively large values lead to lower accuracy that does not meet the requirements and excessively small values are overly complex and increase the difficulty. The kernel function coefficient \(\sigma\) determines the distribution and range of the training sample by controlling the size of inner product scaling in high-dimensional space, which can affect overfitting.

Therefore, we introduce other algorithms for optimization of the three parameters in SVR. Currently the commonly used algorithms are 32and some heuristic algorithms. Although the grid search method is able to find the highest classification accuracy, which is the global optimal solution. However, sometimes it can be time-consuming to find the optimal parameters for larger scales. If a heuristic algorithm is used, we could find the global optimal solution without having to trace over all the parameter points in the grid. And GA is one of the most commonly used heuristic algorithms, compared to other heuristic algorithms, it has the advantages of strong global search, generalizability, and broader blending with other algorithms.

Given these factors, we employ a GA to encode and optimize the relevant parameters of the model. The inputs are the experimental training dataset, the Gaussian kernel function expression, the maximum number of generations taken by the GA, the accuracy range of the optimized parameters, the GA population size, the fitness function, the probability of crossover, and the probability of mutation. The outputs are the optimal penalty factor C, ε-insensitive loss function parameter \(\varepsilon ,\) and optimal Gaussian kernel parameter \(\sigma\) of SVR, thus achieving the optimization of SVR. The basic steps involved in GA optimization are described in detail below, and the model prediction process is shown in Fig. 1 .

Prediction process of the GASVR model

Population initialization

The three parameters are encoded using binary arrays composed of 0–1 bit-strings. Each parameter consisted of six bits, and the initial population is randomly generated. The population size is set at 60, and the number of iterations is 200.

Fitness calculation

In the same dataset, the K-fold cross-validation technique is used to test each individual in the population, with K = 5. K-fold cross validation effectively avoids the occurrence of model over-learning and under-learning. For the judgment of the individual, this paper evaluates it in terms of fitness calculations. Therefore, combining the two enables the effective optimization of the model’s selected parameters and improves the accuracy of regression prediction.

Fitness is calculated using the mean error method, with smaller mean errors indicating better fitness. The fitness function is shown in formula ( 7 ) [ 32 ].

The individual’s genotype is decoded and mapped to the corresponding parameter value, which is substituted into the SVR model for training. The parameter optimization range is 0.01 ≤ C ≤ 100, 0.1 ≤ \(\sigma\) ≤ 20, and 0.001 ≤ ε ≤ 1.

Selection: The selection operator is performed using the roulette wheel method.

Crossover: The multi-point crossover operator, in which two chromosomes are selected and multiple crossover points are randomly chosen for swapping, is employed. The crossover probability is set at 0.9.

Mutation: The inversion mutation operator, in which two points are randomly selected and the gene values between them are reinserted to the original position in reverse order, is employed. The mutation probability is set at 0.09.

Decoding: The bit strings are converted to parameter sets.

The parameter settings of the GASVR model built in this paper are shown in Table 1 .

BILSTM model

The LSTM model is a special recurrent neural network algorithm that can remember the long-term dependencies of data series and has an excellent capacity for self-learning and non-linear fitting. LSTM automatically connects hidden layers across time points, such that the output of one time point can arbitrarily enter the output terminal or the hidden layer of the next time point. Therefore, it is suitable for the sample prediction of time-series data and can predict future data based on stored data. Details of the model are shown in Fig. 2 .

Schematic diagram of the LSTM model

LSTM consists of a forget gate, an input gate, and an output gate.

The forget gate combines the previous and current time steps to give the output of the sigmoid activation function. Its role is to screen the information from the previous state and identify useful information that truly impacts the subsequent time step. The equation for the forget gate is shown in formula ( 8 ).

Among the number, \(W_{f}\) is the weight of the forget gate, \({b}_{f}\) is the bias, \(\sigma\) is the sigmoid activation function, \({f}_{t}\) is the output of the sigmoid activation function, \(t-1\) is the previous time step, \(t\) is the current time step, and \({x}_{t}\) is the input time-series data at time step \(t\) .

The input gate is composed of the output of the sigmoid and tanh activation functions, and its role is to control the ratio of input information entering the information of a given time step. The equation for the input gate is shown in formula ( 9 ).

Among the number, \({W}_{i}\) is the output weight of the input gate, \({i}_{t}\) is the output of the sigmoid activation function, \({b}_{i}\) and \({b}_{C}\) are the biases of the input gate, and \({W}_{C}\) is the output of the tanh activation function.

The role of the output gate is to control the amount of information output at the current state, and its equation is shown in formula ( 10 ).

Among the number, \({W}_{o}\) is the weight of \({o}_{t}\) , and \({b}_{o}\) is the bias of the output gate.

The values of the above activation functions \(\sigma\) and tanh are generally shown in formulas ( 11 ) and ( 12 ).

\({C}_{t}\) is the data state of the current time step, and its value is determined by the input information of the current state and the information of the previous state. It is shown in formula ( 13 ).

Among the number, \(\widetilde{{C}_{t}}=\mathit{tan}h({W}_{c}[{h}_{t-1},{x}_{t}]+{b}_{c})\) .

\({h}_{t}\) is the state information of the hidden layer at the current time step, \({h}_{t}={o}_{t}\times \mathit{tan}h({c}_{t})\) .Each time step \({T}_{n}\) has a corresponding state \({C}_{t}\) . By undergoing the training process, the model can learn how to modify state \({C}_{t}\) through the forget, output, and input gates. Therefore, this state is consistently passed on, implying that important distant information will neither be forgotten nor significantly affected by unimportant information.

The above describes the principle of LSTM, which involves forward processing when applied. BILSTM consists of two LSTM networks, one of which processes the input sequence in the forward direction (i.e., the original order), while the other inputs the time series in the backward direction into the LSTM model. After processing both LSTM networks, the outputs are combined, which eventually gives the output results of the BILSTM model. Details of the model are presented in Fig. 3 .

Schematic diagram of the BILSTM model

Compared to LSTM, BILSTM can achieve bidirectional information extraction of the time-series and connect the two LSTM layers onto the same output layer. Therefore, in theory, its predictive performance should be superior to that of LSTM. In BILSTM, the equations of the forward hidden layer( \(\overrightarrow{{h}_{t}}\) ) , backward hidden layer( \(\overleftarrow{{h}_{t}}\) ) , and output layer( \({o}_{t}\) ) are shown in formulas ( 14 ) , ( 15 ) and ( 16 ).

The parameter settings of the BILSTM model built in this paper are shown in Table 2 .

Informer model

The Informer model follows the compiler-interpreter architecture in the Transformer model, and based on this, structural optimizations have been made to reduce the computational time complexity of the algorithm and to optimize the output form of the interpreter. The two optimization methods are described in detail next.

With large amounts of input data, neural network models can have difficulty capturing long-term interdependencies in sequences, which can produce gradient explosions or gradient vanishing and affect the model's prediction accuracy. Informer model solves the existential gradient problem by using a ProbSparse Self-attention mechanism to make more efficient than conventional self-attention.

The value of Transformer self-attention is shown in formula ( 17 ).

Among them, \(Q\in {R}^{{L}_{Q}\times d}\) is the query matrix, \(K\in {R}^{{L}_{K}\times d}\) is the key matrix, and \(V\in {R}^{{L}_{V}\times d}\) is the value matrix, which are obtained by multiplying the input matrix X with the corresponding weight matrices \({W}^{Q}\) , \({W}^{K}\) , \({W}^{V}\) respectively, and d is the dimensionality of Q, K, and V. Let \({q}_{i}\) , \({k}_{i}\) , \(v_{i}\) represent the ith row in the Q, K, V matrices respectively, then the ith attention coefficient is shown in formula ( 18 ) as follows.

Therein, \(p({k}_{j}|{q}_{i})\) denotes the traditional Transformer's probability distribution formula, and \(k({q}_{i},{K}_{l})\) denotes the asymmetric exponential sum function. Firstly, q=1 is assumed, which implies that the value of each moment is equally important; secondly, the difference between the observed distribution and the assumed one is evaluated by the KL scatter, if the value of KL is bigger, the bigger the difference with the assumed distribution, which represents the more important this moment is. Then through inequality \(ln{L}_{k}\le M({q}_{i},K)\le {\mathit{max}}_{j}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}-\frac{1}{{L}_{k}}{\sum }_{j=1}^{{L}_{k}}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}+ln{L}_{k}\) , \(M({q}_{i},K)\) is transformed into \(\overline{M}({q}_{i},K)\) . According to the above steps, the ith sparsity evaluation formula is obtained as shown in formula ( 19 ) [ 33 ].

One of them, \(M({q}_{i},K)\) denotes the ith sparsity measure; \(\overline{M}({q}_{i},K)\) denotes the ith approximate sparsity measure; \({L}_{k}\) is the length of query vector. \(TOP-u\) quantities of \(\overline{M}\) are selected to form \(\overline{Q}\) , \(\overline{Q}\) is the first u sparse matrices, and the final sparse self-attention is shown in Formula ( 20 ). At this point, the time complexity is still \(O({n}^{2})\) , and to solve this problem, only l moments of M2 are computed to reduce the time complexity to \(O(L\cdot \mathit{ln}(L))\) .

Informer uses a generative decoder to obtain long sequence outputs.Informer uses the standard decoder architecture shown in Fig. 4 , in long time prediction, the input given to the decoder is shown in formula ( 21 ).

Informer uses a generative decoder to obtain long sequence outputs

Therein, \({X}_{de}^{t}\) denotes the input to the decoder; \({X}_{token}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}\) is the dimension of the encoder output, which is the starting token without using all the output dimensions; \({X}_{0}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}\) is the dimension of the target sequence, which is uniformly set to 0; and finally the splicing input is performed to the encoder for prediction.

The parameter settings of Informer model created in this paper are shown in Table 3 .

BILSTM-GASVR combined prediction model

SVR has demonstrated good performance in solving problems like finite samples and non-linearity. Compared to deep learning methods, it offers faster predictions and smaller empirical risks. BILSTM has the capacity for long-term memory, can effectively identify data periodicity and trends, and is suitable for the processing of time-series data. Hence, it can be used to identify the effect of time-series on the number of confirmed cases. Given the advantages of these two methods in different scenarios, we combined them to perform predictions using GASVR, followed by error repair using BILSTM. The basic steps for prediction based on the BILSTM-GASVR model are as follows:

Normalization is performed on the initial data.

The GASVR model is applied to perform training and parameter optimization of the data to obtain the predicted value \(\widehat{{y}_{i}}\) .

After outputting the predicted value of GASVR, the residual sequence between the predicted value and real data is extracted to obtain the error \({\gamma }_{i}\) (i.e., \({\gamma }_{i}={y}_{i}-\widehat{{y}_{i}}\) ).

The BILSTM model is applied to perform training of the error to improve prediction accuracy. The BILSTM model in this paper is a multiple input single output model. Its inputs are the true and predicted error values \({\gamma }_{i}\) and its output is the new error value \(\widehat{{\gamma }_{i}}\) predicted by BILSTM.

The final predicted value is the sum of the GASVR predicted value and the BILSTM residual predicted value (i.e., \({Y}_{i}=\widehat{{y}_{i}}+\widehat{{\gamma }_{i}}\) ).

The parameter settings of the BILSTM-GASVR model built in this paper are shown in Table 4 .

Model testing criteria

To test the effect of the model, the prediction results of the BILSTM-GASVR model are compared to those of GASVR, LSTM, BILSTM and Informer. The prediction error is mainly quantified using three indicators: mean squared error (MSE), root mean squared error (RMSE), and correlation coefficient ( \(R^{2}\) ). Their respective equations are shown in formulas ( 22 ), ( 23 ) and ( 24 ).

Demand forecasting model of ICU healthcare resources

ICU healthcare resources can be divided into human and material resources. Human resources refer specifically to the professional healthcare workers in the ICU. Material resources, which are combined with the actual consumption of medical supplies, can be divided into consumables and non-consumables. Consumables refer to the commonly used drugs in the ICU, which include drugs for treating cardiac insufficiency, vasodilators, anti-shock vasoactive drugs, analgesics, sedatives, muscle relaxants, anti-asthmatic drugs, and anticholinergics. Given that public health emergencies have a relatively high probability of affecting the respiratory system, we compiled a list of commonly used drugs for respiratory diseases in the ICU (Table 5 ).

Non-consumables refer to therapeutic medical equipment, including electrocardiogram machines, blood gas analyzers, electrolyte analyzers, bedside diagnostic ultrasound machines, central infusion workstations, non-invasive ventilators, invasive ventilators, airway clearance devices, defibrillators, monitoring devices, cardiopulmonary resuscitation devices, and bedside hemofiltration devices.

The demand forecasting model of ICU healthcare resources constructed in this study, as well as its relevant parameters and definitions, are described below. \({R}_{ij}^{n}\) is the forecasted demand for the \(i\) th category of resources on the \(n\) th day in region \(j\) . \({Y}_{j}^{n}\) is the predicted number of current confirmed cases on the \(n\) th day in region \(j\) . \({M}_{j}^{n}\) is the number of ICU healthcare workers on the \(n\) th day in region \(j\) , which is given by the following formula: number of healthcare workers the previous day + number of new recruits − reduction in number the previous day, where the reduction in number refers to the number of healthcare workers who are unable to work due to infection or overwork. In general, the number of ICU healthcare workers should not exceed 5% of the number of current confirmed cases (i.e., it takes the value range [0, \(Y_{j}^{n}\) ×5%]). \(U_{i}\) is the maximum working hours or duration of action of the \(i\) th resource category within one day. \({A}_{j}\) is the number of resources in the \(i\) th category allocated to patients (i.e., how many units of resources in the \(i\) th category is needed for a patient who need the \(i\) th unit of the given resource). \({\varphi }_{i}\) is the demand conversion coefficient (i.e., the proportion of the current number of confirmed cases who need to use the \(i\) th resource category). \({C}_{ij}^{n}\) is the available quantity of material resources of the \(i\) th category on the \(n\) th day in region \(j\) . At the start, this quantity is the initial reserve, and once the initial reserve is exhausted, it is the surplus from the previous day. The formula for this parameter is given as follows: available quantity from the previous day + replenishment on the previous day − quantity consumed on the previous day, where if \({C}_{ij}^{n}\) is a negative number, it indicates the amount of shortage for the given category of resources on the previous day.

In summary, the demand forecast for emergency medical supplies constructed in this study is shown in formula ( 25 ).

The number of confirmed cases based on data-driven prediction is introduced into the demand forecasting model for ICU resources to forecast the demand for the various categories of resources. In addition to the number of current confirmed cases, the main variables of the first demand forecasting model for human resources are the available quantity and maximum working hours. The main variable of the second demand forecasting model for consumable resources is the number of units consumed by the available quantity. The main variable of the third model for non-consumable resources is the allocated quantity. These three resource types can be predicted using the demand forecasting model constructed in this study.

Prediction of the number of current infected cases

The COVID-19 situation in Shanghai is selected for our experiment. A total of 978 entries of epidemic-related data in Shanghai between January 20, 2020, and September 24, 2022, are collected from the epidemic reporting platform. This dataset is distributed over a large range and belongs to a right-skewed leptokurtic distribution. The specific statistical description of data is shown in Table 6 . Part of the data is shown in Table 7 .

And we divided the data training set and test set in an approximate 8:2 ratio, namely, 798 days for training (January 20, 2020 to March 27, 2022) and 180 days for prediction (March 28, 2022 to September 24, 2022).

Due to the large difference in order of magnitude between the various input features, directly implementing training and model construction would lead to suboptimal model performance. Such effects are usually eliminated through normalization. In terms of interval selection, [0, 1] reflects the probability distribution of the sample, whereas [-1, 1] mostly reflects the state distribution or coordinate distribution of the sample. Therefore, [-1, 1] is selected for the normalization interval in this study, and the processing method is shown in formula ( 26 ).

Among the rest, \(X\) is the input sample, \({X}_{min}\) and \({X}_{max}\) are the minimum and maximum values of the input sample, and \({X}_{new}\) is the input feature after normalization.

In addition, we divide the data normalization into two parts, considering that the amount of data in the training set is much more than the test set in the real operating environment. In the first step, we normalize the training set data directly according to the above formula; in the second step, we normalize the test data set using the maximum and minimum values of the training data set.

The values of the preprocessed data are inserted into the GASVR, LSTM, Informer, BILSTM models and the BILSTM-GASVR model is constructed. Figures 5 , 6 , 7 , 8 and 9 show the prediction results. From Figs. 5 , 6 , and 7 , it can be seen that in terms of data accuracy, GASVR more closely matches the real number of infected people relative to BILSTM and LSTM. Especially in the most serious period of the epidemic in Shanghai (April 17, 2022 to April 30, 2022), the advantage of the accuracy of the predicted data of GASVR is even more obvious, which is due to the characteristics of GASVR for small samples and nonlinear prediction. However, in the overall trend of the epidemic, BILSTM and LSTM, which have the ability to learn and memorize to process time series data, are superior. It is clearly seen that in April 1, 2022-April 7, 2022 and May 10, 2022-May 15, 2022, there is a sudden and substantial increase in GASVR in these two time phases, and a sudden and substantial decrease in April 10, 2022-April 14, 2022. These errors also emphasize the stability of BILSTM and LSTM, which are more closely matched to the real epidemic development situation in the whole process of prediction, and the difference between BILSTM and LSTM prediction is that the former predicts data more accurately than the latter, which is focused on the early stage of prediction as well as the peak period of the epidemic. Informer is currently an advanced time series forecasting method. From Fig. 8 , it can be seen that the prediction data accuracy and the overall trend of the epidemic are better than the single prediction models of GASVR, LSTM and BILSTM. However, Informer is more suitable for long time series and more complex and large prediction problems, so the total sample size of less than one thousand cases is not in the comfort zone of Informer model. Figure 9 shows that the BILSTM-GASVR model constructed in this paper is more suitable for this smaller scale prediction problem, with the best prediction results, closest to the actual parameter (number of current confirmed cases), demonstrating small sample and time series advantages. In Short, the prediction effect of models is ranked as follows: BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM.

The prediction result of the GASVR model

The prediction result of the LSTM model

The prediction result of the BILSTM model

The prediction result of the Informer model

The prediction result of the BILSTM-GASVR model

The values of the three indicators (MSE, RMSE, and correlation coefficient \({R}^{2}\) ) for the five models are shown in Table 8 . MSE squares the error so that the larger the model error, the larger the value, which help capture the model's prediction error more sensitively. RMSE is MSE with a root sign added to it, which allows for a more intuitive representation of the order of magnitude difference from the true value. \({R}^{2}\) is a statistical indicator used to assess the overall goodness of fit of the model, which reflects the overall consistency of the predicted trend and does not specifically reflect the degree of data. The results in the Table 8 are consistent with the prediction results in the figure above, while the ranking of MSE, RMSE, and \({R}^{2}\) are also the same (i.e., BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM).

In addition, we analyze the five model prediction data using significance tests as a way of demonstrating whether the model used is truly superior to the other baseline models. The test dataset with kurtosis higher than 4 does not belong to the approximate normal distribution, so parametric tests are not used in this paper. Given that the datasets predicted by each of the five models are continuous and independent datasets, this paper uses the Kruskal-Wallis test, which is a nonparametric test. The test steps are as follows.

Determine hypotheses (H0, H1) and significance level ( \(\alpha\) ).

For each data set, all its sample data are combined and ranked from smallest to largest. Then find the number of data items ( \({n}_{i}\) ), rank sum ( \({R}_{i}\) ) and mean rank of each group of data respectively.

Based on the rank sum, the test statistic (H) is calculated for each data set in the Kruskal-Wallis test. The specific calculation is shown in formula ( 27 ).

According to the test statistic and degrees of freedom, find the corresponding p-value in the Kruskal-Wallis distribution table. Based on the P-value, determine whether the original hypothesis is valid.

In the significance test, we set the significance setting original hypothesis (H0) as there is no significant difference between the five data sets obtained from the five predictive models. We set the alternative hypothesis (H1) as there is a significant difference between the five data sets obtained from the five predictive models. At the same time, we choose the most commonly used significance level taken in the significance test, namely 0.05. In this paper, multiple comparisons and two-by-two comparisons of the five data sets obtained from the five predictive models are performed through the SPSS software. The results of the test show that in the multiple comparison session, P=0.001<0.05, so H0 is rejected, which means that the difference between the five groups of data is significant. In the two-by-two comparison session, BILSTM-GASVR is less than 0.05 from the other four prediction models. The specific order of differences is Informer < GASVR < BILSTM < LSTM, which means that the BILSTM-GASVR prediction model does get a statistically significant difference between the dataset and the other models.

In summary, combined prediction using the BILSTM-GASVR model is superior to the other four single models in various aspects in the case study analysis of Shanghai epidemic with a sample size of 978.

Demand forecasting of ICU healthcare resources

Combined with the predicted number of current infected cases, representatives are selected from the three categories of resources for forecasting. The demand for nurses is selected as the representative for the first category of resources.

In view of the fact that there are currently no specific medications that are especially effective for this public health emergency, many ICU treatment measures involved helping patients survive as their own immune systems eliminated the virus. This involved, for example, administering antibiotics when patients developed a secondary bacterial infection. glucocorticoids are used to temporarily suppress the immune system when their immune system attacked and damaged lung tissues causing patients to have difficulty breathing. extracorporeal membrane oxygenation (ECMO) is used for performing cardiopulmonary resuscitation when patients are suffering from cardiac arrest. In this study, we take dexamethasone injection (5 mg), a typical glucocorticoid drug, as the second category of ICU resources (i.e., drugs); and invasive ventilators as the third category of ICU resources (i.e., medical equipment).

During the actual epidemic in Shanghai, the municipal government organized nine critical care teams, which are stationed in eight municipally designated hospitals and are dedicated to the treatment of critically ill patients. In this study, the ICU nurses, dexamethasone injections, and invasive ventilators in Shanghai are selected as the prediction targets and introduced into their respective demand forecasting models. Forecasting of ICU healthcare resources is then performed for the period from March 28, 2022, to April 28, 2022, as an example. Part of the parameter settings for the three types of resources are shown in Tables 9 , 10 , and 11 , respectively.

Table 12 shows the forecasting results of the demand for ICU nurses, dexamethasone injections, and invasive ventilators during the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022.

For the first category (i.e., ICU nurses), human resource support is only needed near the peak period, but the supply could not be replenished immediately. In the early stages, Shanghai could only rely on the nurses’ perseverance, alleviating the shortage of human resources by reducing the number of shifts and increasing working hours. This situation persisted until about April 10 and is only resolved when nurses from other provinces and regions successively arrived in Shanghai.

The second category of ICU resources is drugs, which are rapidly consumed. The pre-event reserve of 30,000 dexamethasone injections could only be maintained for a short period and is fully consumed during the outbreak. Furthermore, daily replenishment is still needed, even when the epidemic has passed its peak and begun its decline.

The third category is invasive ventilators, which are non-consumables. Thus, the reserve lasted for a relatively long period of time in the early stages and did not require replenishment after its maximum usage during the peak period.

Demand forecasting models are constructed based on the classification of healthcare resources according to their respective features. We choose ICU nurses, dexamethasone injections, and invasive ventilators as examples, and then forecast demand for the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022. The main conclusions are as follows:

A long period of time is needed to train ICU healthcare workers who can independently be on duty, taking at least one year from graduation to entering the hospital, in addition to their requiring continuous learning, regular theoretical training, and the accumulation of clinical experience during this process. Therefore, for the first category of ICU healthcare resources, in the long term, healthcare institutions should place a greater emphasis on their talent reserves. Using China as an example, according to the third ICU census, the ratio of the number of ICU physicians to the number of beds is 0.62:1 and the ratio of the number of nurses to the number of beds is 1.96:1, which are far lower than those stipulated by China itself and those of developed countries. Therefore, a fundamental solution is to undertake proactive and systematic planning and construction to ensure the more effective deployment of human resources in the event of a severe outbreak. In the short term, healthcare institutions should focus on the emergency expansion capacity of their human resources. In case there are healthcare worker shortages during emergencies, the situation can be alleviated by summoning retired workers back to work and asking senior medical students from various universities to help in the hospitals to prevent the passive scenario of severely compressing the rest time of existing staff or waiting for external aid. However, it is worth noting that to ensure the effectiveness of such a strategy of using retired healthcare workers or senior students of university medical faculties, it is necessary for healthcare organizations to provide them with regular training in the norm, such as organizing 2-3 drills a year, to ensure the professionalism and proficiency of healthcare workers who are temporarily and suddenly put on the job. At the same time, it is also necessary to fully mobilize the will of individuals. Medical institutions can provide certain subsidies to retired health-care workers and award them with honorable titles. For senior university medical students, volunteer certificates are issued and priority is given to their internships, so that health-care workers can be motivated to self-realization through spiritual and material rewards.

Regarding the second category of ICU resources (i.e., drugs), healthcare institutions perform the subdivision of drug types and carry out dynamic physical preparations based on 15–20% of the service recipient population for clinically essential drugs. This will enable a combination of good preparedness during normal times and emergency situations. In addition, in-depth collaboration with corporations is needed to fully capitalize on their production capacity reserves. This helps medical institutions to be able to scientifically and rationally optimize the structure and quantity of their drug stockpiles to prevent themselves from being over-stressed. Yet the lower demand for medicines at the end of the epidemic led to the problem of excess inventory of enterprises at a certain point in time must be taken into account. So, the medical institutions should sign a strategic agreement on stockpiling with enterprises, take the initiative to bear the guaranteed acquisition measures, and consider the production costs of the cooperative enterprises. These measures are used to truly safeguard the enthusiasm of the cooperative enterprises to invest in the production capacity.

Regarding the third category of ICU resources (i.e., medical equipment), large-scale medical equipment cannot be rapidly mass-produced due to limitations in the capacity for emergency production and conversion of materials. In addition, the bulk procurement of high-end medical equipment is also relatively difficult in the short term. Therefore, it is more feasible for healthcare institutions to have physical reserves of medical equipment, such as invasive ventilators. However, the investment costs of medical equipment are relatively high. Ventilators, for example, cost up to USD $50,000, and subsequent maintenance costs are also relatively high. After all, according to the depreciable life of specialized hospital equipment, the ventilator, as a surgical emergency equipment, is depreciated over five years. And its depreciation rate is calculated at 20% annually for the first five years, which means a monthly depreciation of $835. Thus, the excessively low utilization rate of such equipment will also impact the hospital. Healthcare institutions should, therefore, conduct further investigations on the number of beds and the reserves of ancillary large-scale medical equipment to find a balance between capital investment and patient needs.

The limitations of this paper are reflected in the following three points. Firstly, in the prediction of the number of infections, the specific research object in this paper is COVID-19, and other public health events such as SARS, H1N1, and Ebola are not comparatively analyzed. The main reason for this is the issue of data accessibility, and it is easier for us to analyze events that have occurred in recent years. In addition, using the Shanghai epidemic as a specific case may be more representative of the epidemic situation in an international metropolis with high population density and mobility. Hence, it has certain regional limitations, and subsequent studies should expand the scope of the case study to reflect the characteristics of epidemic transmission in different types of urban areas and enhance the generalizability.

Secondly, the main emphasis of this study is on forecasting the demand for ICU healthcare resources across the entire region of the epidemic, with a greater focus on patient demand during public emergencies. Our aims are to help all local healthcare institutions more accurately identify changes in ICU healthcare resource demand during this local epidemic wave, gain a more accurate understanding of the treatment demands of critically ill patients, and carry out comprehensive, scientifically based decision-making. Therefore, future studies can examine individual healthcare institutions instead and incorporate the actual conditions of individual units to construct multi-objective models. In this way, medical institutions can further grasp the relationship between different resource inputs and the recovery rate of critically ill patients, and achieve the balance between economic and social benefits.

Finally, for the BILSTM-GASVR prediction method, in addition to the number of confirmed diagnoses predicted for an outbreak in a given region, other potential applications beyond this type of medium-sized dataset still require further experimentation. For example, whether the method is suitable for procurement planning of a certain supply in production management, forecasting of goods sales volume in marketing management, and other long-period, large-scale and other situations.

Within the context of major public health events, the fluctuations and uncertainties in the demand for ICU resources can lead to large errors between the healthcare supply and actual demand. Therefore, this study focuses on the question of forecasting the demand for ICU healthcare resources. Based on the number of current confirmed cases, we construct the BILSTM-GASVR model for predicting the number of patients. By comparing the three indicators (MSE, MAPE, and correlation coefficient \(R^{2}\) ) and the results of the BILSTM, LSTM, and GASVR models, we demonstrate that our model have a higher accuracy. Our findings can improve the timeliness and accuracy of predicting ICU healthcare resources and enhance the dynamics of demand forecasting. Hence, this study may serve as a reference for the scientific deployment of ICU resources in healthcare institutions during major public events.

Given the difficulty in data acquisition, only the Shanghai epidemic dataset is selected in this paper, which is one of the limitations mentioned in Part 4. Although the current experimental cases of papers in the same field do not fully conform to this paper, the results of the study cannot be directly compared. However, after studying the relevant reviews and the results of the latest papers, we realize that there is consistency in the prediction ideas and prediction methods [ 34 , 35 ]. Therefore, we summarize the similarities and differences between the results of the study and other research papers in epidemic forecasting as shown below.

Similarities: on the one hand, we all characterize trends in the spread of the epidemic and predict the number of infections over 14 days. On the other hand, we all select the current mainstream predictive models as the basis and combine or improve them. Moreover, we all use the same evaluation method (comparison of metrics such as MSE and realistic values) to evaluate the improvements against other popular predictive models.

Differences: on the one hand, other papers focus more on predictions at the point of the number of patients, such as hospitalization rate, number of infections, etc. This paper extends the prediction from the number of patients to the specific healthcare resources. This paper extends the prediction from the number of patients to specific healthcare resources. We have divided the medical resources and summarized the demand regularities of the three types of information in the epidemic, which provides the basis for decision-making on epidemic prevention to the government or medical institutions. On the other hand, in addition to the two assessment methods mentioned in the same point, this paper assesses the performance of the prediction methods with the help of significance tests, which is a statistical approach to data. This can make the practicality of the forecasting methodology more convincing.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Yuan, R., Yang, Y., Wang, X., Duo, J. & Li, J. Study on the forecasting and allocation of emergency medical material needs in the event of a major infectious disease outbreak. J Saf Environ. https://doi.org/10.13637/j.issn.1009-6094.2023.2448 .

Total epidemic data (global). 2024. Retrieved March 30, 2024, from: https://www.sy72.com/world/ .

Sui, K., Wang, Y., Wang, S., Chen, C., & Sun, X. A multiple regression analysis-based method for forecasting the demand of emergency materials for power grids. Electron Technol Softw Eng. 2016; (23), 195-197.

Li K, Liu L, Zhai J, Khoshgoftaar TM, Li T. The improved grey model based on particle swarm optimization algorithm for time series prediction. Eng Appl Artif Intell. 2016;55:285–91. https://doi.org/10.1016/j.engappai.2016.07.005 .

Article   Google Scholar  

Fan R, Wang Y, Luo M. SEIR-based COVID-19 transmission model and inflection point prediction analysis. J Univ Electron Sci Technol China. 2020;49(3):369–74.

Google Scholar  

Neves AGM, Guerrero G. Predicting the evolution of the COVID-19 epidemic with the A-SIR model: Lombardy, Italy and São Paulo state Brazil. Physica D. 2020;413:132693. https://doi.org/10.1016/j.physd.2020.132693 .

Article   PubMed   PubMed Central   Google Scholar  

Li R, Pei S, Chen B, Song Y, Zhang T, Wan Y, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020;368(6490):489–93. https://doi.org/10.1126/science.abb3221 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond. 1927;115(772):700–21. https://doi.org/10.1098/rspa.1927.0118 .

Hethcote HW. The mathematics of infectious diseases. Siam Rev. 2000;42(4):599–653. https://doi.org/10.1137/SIREAD000042000004000655000001 .

Estrada E. COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. Phys Rep. 2020;869:1–51. https://doi.org/10.1016/j.physrep.2020.07.005 .

Mizumoto K, Chowell G. Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020. Infect Dis Model. 2020;5:264–70. https://doi.org/10.1016/j.idm.2020.02.003 .

Delamater PL, Street EJ, Leslie TF, Yang YT, Jacobsen KH. Complexity of the Basic Reproduction Number. Emerg Infect Dis. 2019;25(1):1–4. https://doi.org/10.3201/eid2501.171901 .

Annas S, Isbar Pratama M, Rifandi M, Sanusi W, Side S. Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia. Chaos Solit Fract. 2020;139:110072. https://doi.org/10.1016/j.chaos.2020.110072 .

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199–209. https://doi.org/10.1056/NEJMoa2001316 .

Anggriani N, Ndii MZ, Amelia R, Suryaningrat W, Pratama MAA. A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity. Alexandria Eng J. 2022;61(1):113–24. https://doi.org/10.1016/j.aej.2021.04.104 .

Efimov D, Ushirobira R. On an interval prediction of COVID-19 development based on a SEIR epidemic model. Ann Rev Control. 2021;51:477–87. https://doi.org/10.1016/j.arcontrol.2021.01.006 .

Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, Wang MH. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis. 2020;93:211–16.

Chao L, Feng P, Shi P. Study on the epidemic development of COVID-19 in Hubei. J Zhejiang Univ (Med Sci). 2020;49(2):178–84.

Reiner RC, Barber RM, Collins JK, et al. Modeling COVID-19 scenarios for the United States. Nat Med. 2021;27(1):94–105. https://doi.org/10.1038/s41591-020-1132-9 .

Article   CAS   Google Scholar  

Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM GRU and Bi-LSTM. Chaos Solit Fract. 2020;140:110212. https://doi.org/10.1016/j.chaos.2020.110212 .

Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solit Fract. 2020;135:109864. https://doi.org/10.1016/j.chaos.2020.109864 .

Hamou AA, Azroul E, Hammouch Z, Alaoui AAL. A fractional multi-order model to predict the COVID-19 outbreak in Morocco. Appl Comput Math. 2021;20(1):177–203.

Zhou, L., Zhao, C., Liu, N., Yao, X., & Cheng, Z. Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach. Eng Appl Artif Intell. 2023; 122. https://doi.org/10.1016/j.engappai.2023.106157 .

Huang C, Chen Y, Ma Y, Kuo P. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. medRxiv. 2020;74822–34.

Gautam Y. Transfer Learning for COVID-19 cases and deaths forecast using LSTM network. Isa Trans. 2022;124:41–56. https://doi.org/10.1016/j.isatra.2020.12.057 .

Article   PubMed   Google Scholar  

Ghany KKA, Zawbaa HM, Sabri HM. COVID-19 prediction using LSTM algorithm: GCC case study. Inform Med Unlock. 2021;23:100566. https://doi.org/10.1016/j.imu.2021.100566 .

Devaraj J, Madurai Elavarasan R, Pugazhendhi R, Shafiullah GM, Ganesan S, Jeysree AK, Khan IA, Hossain E. Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant? Results Phys. 2021;21:103817. https://doi.org/10.1016/j.rinp.2021.103817 .

Arora P, Kumar H, Panigrahi BK. Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos Solit Fract. 2020;139:110017. https://doi.org/10.1016/j.chaos.2020.110017 .

Liu Q, Fung DLX, Lac L, Hu P. A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA. Front Public Health. 2021;9:741030. https://doi.org/10.3389/fpubh.2021.741030 .

Ribeiro MHDM, Da Silva RG, Mariani VC, Coelho LDS. Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil. Chaos Solit Fract. 2020;135:109853. https://doi.org/10.1016/j.chaos.2020.109853 .

Shoko C, Sigauke C. Short-term forecasting of COVID-19 using support vector regression: an application using Zimbabwean data. Am J Infect Control. 2023. https://doi.org/10.1016/j.ajic.2023.03.010 .

Feng T, Peng Y, Wang J. ISGS: A Combinatorial Model for Hysteresis Effects. Acta Electronica Sinica. 2023;09:2504–9.

Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI Conference Artif Intell. 2020;35(12):11106–15.

Rahimi I, Chen F, Gandomi AH. A review on COVID-19 forecasting models. Neural Comput Applic. 2023;35:23671–81. https://doi.org/10.1007/s00521-020-05626-8 .

Chen J, Creamer GG, Ning Y, Ben-Zvi T. Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis. Sustainability. 2023;15(22):15840. https://doi.org/10.3390/su152215840 .

Download references

Acknowledgements

We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.

No external funding received to conduct this study.

Author information

Authors and affiliations.

School of Logistics, Beijing Wuzi University, No.321, Fuhe Street, Tongzhou District, Beijing, 101149, China

Weiwei Zhang & Xinchun Li

You can also search for this author in PubMed   Google Scholar

Contributions

WWZ and XCL conceived the idea and conceptualised the study. XCL collected the data. WWZ analysed the data. WWZ and XCL drafted the manuscript, then WWZ and XCLreviewed the manuscript. WWZ and XCL read and approved the final draft.

Corresponding author

Correspondence to Xinchun Li .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zhang, W., Li, X. A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies. BMC Health Serv Res 24 , 477 (2024). https://doi.org/10.1186/s12913-024-10955-8

Download citation

Received : 21 September 2023

Accepted : 05 April 2024

Published : 17 April 2024

DOI : https://doi.org/10.1186/s12913-024-10955-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Public health emergency
• ICU healthcare resource demand
• Machine learning
• Combined prediction

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 30 April 2024

Developing more useful equity measurements for flood-risk management

• Adam B. Pollack   ORCID: orcid.org/0000-0001-6642-0591 1 ,
• Casey Helgeson   ORCID: orcid.org/0000-0001-5333-9954 2 , 3 ,
• Carolyn Kousky 4 &
• Klaus Keller   ORCID: orcid.org/0000-0002-5451-8687 1  

Nature Sustainability ( 2024 ) Cite this article

Metrics details

• Climate-change policy
• Natural hazards

Decision-makers increasingly invoke equity to motivate, design, implement and evaluate strategies for managing flood risks. Unfortunately, there is little guidance on how analysts can develop measurements that support these tasks. Here we analyse how equity can be defined and measured by surveying 167 peer-reviewed publications that explicitly state an interest in equity in the context of flood-risk management. Our main result is a taxonomy that systematizes how equity has been, and can be, defined and measured in flood-risk research. The taxonomy embodies how equity is a pluralistic and unavoidably ethical concept. Despite this, we find that most quantitative studies fail to motivate or defend critical value judgements on which their findings depend. We also find that studies often include only a single equity measurement. This practice can overlook important trade-offs between competing perspectives on equity. For example, the few studies that employ distinct principles show that conclusions about equity depend on which principle underlies a specific measurement and how that principle is operationalized. We draw on our analysis to suggest practices for developing more useful equity indicators and performing more comprehensive quantitative equity assessments in the broader context of environmental risks.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Data availability

All data and materials used in the analysis are freely and permanently available via Zenodo 115 . This analysis was tested and confirmed for reproducibility by S. Baboolal on 11 July 2023. If you have any issues reproducing the results, please contact the corresponding author on the GitHub repository.

Code availability

All code used in the analysis are freely and permanently available via Zenodo 115 . This analysis was tested and confirmed for reproducibility by S. Baboolal on 11 July 2023. If you have any issues reproducing the results, please contact the corresponding author on the GitHub repository.

Čapek, S. M. The ‘Environmental Justice’ frame: a conceptual discussion and an application. Soc. Probl. 40 , 5–24 (1993).

Article   Google Scholar  

Bullard, R. The legacy of American apartheid and environmental racism. J. Civil Rights Econ. Dev. 9 , 445–474 (1994).

Google Scholar  

Cutter, S. L. Race, class and environmental justice. Prog. Hum. Geogr. 19 , 111–122 (1995).

Bullard, R. D. Dismantling environmental racism in the USA. Local Environ. 4 , 5–19 (1999).

Brulle, R. J. & Pellow, D. N. Environmental justice: human health and environmental inequalities. Annu. Rev. Public Health 27 , 103–124 (2006).

Schlosberg, D. Defining Environmental Justice: Theories, Movements, and Nature (Oxford Univ. Press, 2007).

Mohai, P., Pellow, D. & Roberts, J. T. Environmental justice. Annu. Rev. Environ. Resour. 34 , 405–430 (2009).

Walker, C. & Burningham, K. Flood risk, vulnerability and environmental justice: evidence and evaluation of inequality in a UK context. Crit. Soc. Policy 31 , 216–240 (2011).

Schlosberg, D. Theorising environmental justice: the expanding sphere of a discourse. Env. Polit. 22 , 37–55 (2013).

Banzhaf, S., Ma, L. & Timmins, C. Environmental justice: the economics of race, place, and pollution. J. Econ. Perspect. 33 , 185–208 (2019).

McDermott, M., Mahanty, S. & Schreckenberg, K. Examining equity: a multidimensional framework for assessing equity in payments for ecosystem services. Environ. Sci. Policy 33 , 416–427 (2013).

Liao, K.-H., Chan, J. K. H. & Huang, Y.-L. Environmental justice and flood prevention: the moral cost of floodwater redistribution. Landsc. Urban Plan. 189 , 36–45 (2019).

Shi, L. et al. Roadmap towards justice in urban climate adaptation research. Nat. Clim. Change 6 , 131–137 (2016).

Siders, A. R. Social justice implications of US managed retreat buyout programs. Clim. Change 152 , 239–257 (2019).

Johnson, C., Penning-Rowsell, E. & Parker, D. Natural and imposed injustices: the challenges in implementing ‘fair’ flood risk management policy in England. Geogr. J. 173 , 374–390 (2007).

Kaminsky, T. An Act to Amend the Environmental Conservation Law, the Public Service Law, the Public Authorities Law, the Labor Law and the Community Risk and Resiliency Act, in Relation to Establishing the New York State Climate Leadership and Community Protection Act Bill Number: S6599 (The New York State Senate, 2019).

Climate and Equitable Jobs Act Bill SB2408 (Illinois General Assembly, 2021).

National Academies of Sciences, Engineering, and Medicine. Communities, Climate Change, and Health Equity—State-Level Implementation: Proceedings of a Workshop—in Brief (The National Academies Press, 2022).

Executive Office of the President. Advancing racial equity and support for underserved communities through the Federal Government. Fed. Regist . 86 , 7009–7013 (2021).

Executive Office of the President. Tackling the climate crisis at home and abroad. Fed. Regist . 86 , 7619–7633 (2021).

Diezmartínez, C. V. & Short Gianotti, A. G. US cities increasingly integrate justice into climate planning and create policy tools for climate justice. Nat. Commun. 13 , 5763 (2022).

Executive Order 45. One NYC Equity Review (The City of New York Office of the Mayor, 2019).

City Agencies’ Strategies for Equity (2022) (Equity NYC, 2022).

Executive Order No. 1-20 (City of Philadelphia Office of the Mayor, 2020).

Racial Equity Action Plans (City of Philadelphia, 2022).

Interim Implementation Guidance for the Justice40 Initiative (M-21-28) (Executive Office of the President, 2021).

FEMA Equity Action Plan (Federal Emergency Management Agency, 2022).

Executive Order 13985 Equity Action Plan (EPA, 2022).

HUD Equity Action Plan (Department of Housing and Urban Development, 2022).

Study to Identify Methods to Assess Equity: Report to the President (Office of Management and Budget, 2021).

Coggins, S. et al. Empirical assessment of equity and justice in climate adaptation literature: a systematic map. Environ. Res. Lett. 16 , 073003 (2021).

Seigerman, C. K. et al. Operationalizing equity for integrated water resources management. J. Am. Water Resour. Assoc. 59 , 281–298 (2022).

Jafino, B. A., Kwakkel, J. H. & Taebi, B. Enabling assessment of distributive justice through models for climate change planning: a review of recent advances and a research agenda. WIREs Clim. Change 12 , e721 (2021).

Jafino, B. A., Kwakkel, J. H. & Klijn, F. Evaluating the distributional fairness of alternative adaptation policies: a case study in Vietnam’s upper Mekong Delta. Clim. Change 173 , 17 (2022).

Kraan, C. M., Hino, M., Niemann, J., Siders, A. R. & Mach, K. J. Promoting equity in retreat through voluntary property buyout programs. J. Environ. Stud. Sci. 11 , 481–492 (2021).

Sovacool, B. K., Newell, P., Carley, S. & Fanzo, J. Equity, technological innovation and sustainable behaviour in a low-carbon future. Nat. Hum. Behav. 6 , 326–337 (2022).

Hino, M. & Nance, E. Five ways to ensure flood-risk research helps the most vulnerable. Nature 595 , 27–29 (2021).

Article   CAS   Google Scholar  

Fletcher, S et al. Equity in water resources planning: a path forward for decision support modelers. J. Water Resour. Plan. Manage. 148 , 02522005 (2022).

Rudge, K. Leveraging critical race theory to produce equitable climate change adaptation. Nat. Clim. Change 13 , 623–631 (2023).

Markanday, A., Galarraga, I. & Markandya, A. A critical review of cost-benefit analysis for climate change adaptation in cities. Clim. Change Econ. 10 , 1950014 (2019).

Eakin, H., Parajuli, J., Yogya, Y., Hernández, B. & Manheim, M. Entry points for addressing justice and politics in urban flood adaptation decision making. Curr. Opin. Environ. Sustain. 51 , 1–6 (2021).

Alexander, M., Doorn, N. & Priest, S. Bridging the legitimacy gap—translating theory into practical signposts for legitimate flood risk governance. Reg. Environ. Change 18 , 397–408 (2018).

Elliott, K. C. A Tapestry of Values: An Introduction to Values in Science (Oxford Univ. Press, 2017).

Cash, D. W. & Belloy, P. G. Salience, credibility and legitimacy in a rapidly shifting world of knowledge and action. Sustain. Sci. Pract. Policy 12 , 7376 (2020).

Cash, D. W. et al. Knowledge systems for sustainable development. Proc. Natl Acad. Sci. USA 100 , 8086–8091 (2003).

Bistline, J., Budolfson, M. & Francis, B. Deepening transparency about value-laden assumptions in energy and environmental modelling: improving best practices for both modellers and non-modellers. Clim. Policy 21 , 1–15 (2021).

Smith, A. B. U.S. billion-dollar weather and climate disasters, 1980 - present (NCEI accession 0209268). NOAA National Centers for Environmental Information https://doi.org/10.25921/STKW-7W73 (2020).

Historical Flood Risk and Costs (FEMA, 2022).

Building Resilient Infrastructure and Communities FY 2021 Subapplication and Selection Status (FEMA, 2022).

Flood Mitigation Assistance Fiscal Year 2021 Subapplication Status (FEMA, 2022).

Gotham, K. F. Reinforcing inequalities: the impact of the CDBG Program on post-Katrina rebuilding. Hous. Policy Debate 24 , 192–212 (2014).

Vilá, O., Smith, G., Cutts, B., Gyawali, S. & Bhattarai, S. Equity in FEMA hazard mitigation assistance programs: the role of state hazard mitigation officers. Environ. Sci. Policy 136 , 632–641 (2022).

Albert, C. et al. Planning nature-based solutions: principles, steps, and insights. Ambio 50 , 1446–1461 (2021).

Rudge, K. Participatory climate adaptation planning in New York City: analyzing the role of community-based organizations. Urban Clim. 40 , 101018 (2021).

Shi, L. et al. Equitable buyouts? Learning from state, county, and local floodplain management programs. Clim. Change 174 , 29 (2022).

Fussell, E. The long term recovery of New Orleans’ population after Hurricane Katrina. Am. Behav. Sci. 59 , 1231–1245 (2015).

Matthews, V. et al. Differential mental health impact six months after extensive river flooding in rural Australia: a cross-sectional analysis through an equity lens. Front. Public Health 7 , 367 (2019).

Jonkman, S. N., Maaskant, B., Boyd, E. & Levitan, M. L. Loss of life caused by the flooding of New Orleans after Hurricane Katrina: analysis of the relationship between flood characteristics and mortality. Risk Anal. 29 , 676–698 (2009).

Bessette, D. L. et al. Building a values-informed mental model for New Orleans climate risk management. Risk Anal. 37 , 1993–2004 (2017).

Kind, J., Wouter Botzen, W. J. & Aerts, J. C. J. H. Accounting for risk aversion, income distribution and social welfare in cost‐benefit analysis for flood risk management. WIREs Clim. Change 8 , e446 (2017).

Thaler, T. & Hartmann, T. Justice and flood risk management: reflecting on different approaches to distribute and allocate flood risk management in Europe. Nat. Hazards 83 , 129–147 (2016).

Dundon, L. A. & Camp, J. S. Climate justice and home-buyout programs: renters as a forgotten population in managed retreat actions. J. Environ. Stud. Sci . 11 , 420–433 (2021).

Objective 1.3 - Achieve Equitable Outcomes for Those We Serve (FEMA, 2021).

Rittel, H. W. J. & Webber, M. M. Dilemmas in a general theory of planning. Policy Sci. 4 , 155–169 (1973).

Lamont, J. & Favor, C. Distributive justice. The Stanford Encyclopedia of Philosophy (ed. Zalta, E. N.) https://plato.stanford.edu/archives/win2017/entries/justice-distributive/ (Metaphysics Research Lab, Stanford University, 2017).

Environmental Indicators: Better Coordination is Needed to Develop Environmental Indicator Sets that Inform Decisions GAO-05-52 (US Government Accountability Office, 2004).

Key Indicator Systems: Experiences of Other National and Subnational Systems Offer Insights for the United States GAO-11-396 (US Government Accountability Office, 2011).

Main Economic Indicators (OECD, 2023).

Informing Our Nation: Improving How to Understand and Assess the USA’s Position and Progress GAO-05-1 (US Government Accountability Office, 2004).

Alonso, W. & Starr, P. The Politics of Numbers (Russell Sage Foundation, 1987).

WDI - Economy (The World Bank, 2018).

Know Your Risk for Heart Disease (Centers for Disease Control and Prevention, 2023).

Ciullo, A., Kwakkel, J. H., De Bruijn, K. M., Doorn, N. & Klijn, F. Efficient or fair? Operationalizing ethical principles in flood risk management: a case study on the Dutch–German Rhine. Risk Anal. 40 , 1844–1862 (2020).

Dennig, F. Climate change and the re-evaluation of cost-benefit analysis. Clim. Change 151 , 43–54 (2018).

Elliott, J. R., Loughran, K. & Brown, P. L. Divergent residential pathways from flood-prone areas: how neighborhood inequalities are shaping urban climate adaptation. Soc. Probl. 70 , 869–892 (2021).

Tate, E., Strong, A., Kraus, T. & Xiong, H. Flood recovery and property acquisition in Cedar Rapids, Iowa. Nat. Hazards 80 , 2055–2079 (2016).

Fletcher, C. S., Rambaldi, A. N., Lipkin, F. & McAllister, R. R. J. Economic, equitable, and affordable adaptations to protect coastal settlements against storm surge inundation. Reg. Environ. Change. 16 , 1023–1034 (2016).

Apgar, J. M. et al. Identifying opportunities to improve governance of aquatic agricultural systems through participatory action research. Ecol. Soc . https://www.jstor.org/stable/26270053 (2017).

Merz, B., Kreibich, H., Schwarze, R. & Thieken, A. Review article “Assessment of economic flood damage”. Nat. Hazards Earth Syst. Sci. 10 , 1697–1724 (2010).

Pollack, A. B., Sue Wing, I. & Nolte, C. Aggregation bias and its drivers in large scale flood loss estimation: a Massachusetts case study. J. Flood Risk Manage . 15 , e12851 (2022).

de Moel, H., Bouwer, L. M. & Aerts, J. C. J. H. Uncertainty and sensitivity of flood risk calculations for a dike ring in the south of the Netherlands. Sci. Total Environ. 473–474 , 224–234 (2014).

Aerts, J. C. J. H. et al. Climate adaptation. Evaluating flood resilience strategies for coastal megacities. Science 344 , 473–475 (2014).

Kron, W. Flood risk = hazard • values • vulnerability. Water Int. 30 , 58–68 (2005).

Keller, K., Helgeson, C. & Srikrishnan, V. Climate risk management. Annu. Rev. Earth Planet. Sci . 49 , 95–116 (2021).

BCA Reference Guide (FEMA, 2009).

Mobini, S., Becker, P., Larsson, R. & Berndtsson, R. Systemic inequity in urban flood exposure and damage compensation. Water 12 , 3152 (2020).

Turnham, J. et al. Housing Recovery on the Gulf Coast, Phase II: Results of Property Owner Survey in Louisiana, Mississippi, and Texas (HUD User, 2011).

Simpson, N. P. et al. A framework for complex climate change risk assessment. One Earth 4 , 489–501 (2021).

Tate, E., Rahman, M. A., Emrich, C. T. & Sampson, C. C. Flood exposure and social vulnerability in the United States. Nat. Hazards 106 , 435–457 (2021).

Emrich, C. T., Tate, E., Larson, S. E. & Zhou, Y. Measuring social equity in flood recovery funding. Environ. Hazards 19 , 228–250 (2020).

Pallathadka, A., Sauer, J., Chang, H. & Grimm, N. B. Urban flood risk and green infrastructure: who is exposed to risk and who benefits from investment? A case study of three U.S. cities. Landsc. Urban Plan. 223 , 104417 (2022).

Zoll, D. Climate adaptation as a racial project: an analysis of color-blind flood resilience efforts in Austin, Texas. Environ. Justice 14 , 288–297 (2021).

Linscott, G., Rishworth, A., King, B. & Hiestand, M. P. Uneven experiences of urban flooding: examining the 2010 Nashville flood. Nat. Hazards 110 , 629–653 (2022).

Bosisio, A. & Moreno-Jiménez, A. Spatially disaggregated assessment of environmental inequalities among vulnerable groups due to urban rainstorm flooding. Appl. Spat. Anal. Policy 15 , 1263–1285 (2022).

Debbage, N. Multiscalar spatial analysis of urban flood risk and environmental justice in the Charlanta megaregion, USA. Anthropocene 28 , 100226 (2019).

Montgomery, M. C. & Chakraborty, J. Assessing the environmental justice consequences of flood risk: a case study in Miami, Florida. Environ. Res. Lett. 10 , 095010 (2015).

Gourevitch, J. D. et al. Spatial targeting of floodplain restoration to equitably mitigate flood risk. Glob. Environ. Change 61 , 102050 (2020).

Frontuto, V., Dalmazzone, S., Salcuni, F. & Pezzoli, A. Risk aversion, inequality and economic evaluation of flood damages: a case study in Ecuador. Sustain. Sci. Pract. Policy 12 , 10068 (2020).

Kind, J., Wouter Botzen, W. J. & Aerts, J. C. J. Social vulnerability in cost-benefit analysis for flood risk management. Environ. Dev. Econ. 25 , 115–134 (2020).

Halsnæs, K., Kaspersen, P. S. & Drews, M. Key drivers and economic consequences of high‑end climate scenarios: uncertainties and risks. Clim. Res. 64 , 85–98 (2015).

Markanday, A., Markandya, A., de Murieta, E. S. & Galarraga, I. Accounting for the effects of employment, equity, and risk aversion in cost–benefit analysis: an application to an adaptation project. J. Benefit Cost Anal. 12 , 313–334 (2021).

Palagi, E., Coronese, M., Lamperti, F. & Roventini, A. Climate change and the nonlinear impact of precipitation anomalies on income inequality. Proc. Natl Acad. Sci. USA 119 , e2203595119 (2022).

Chetty, R., Hendren, N., Kline, P., Saez, E. & Turner, N. Is the United States still a land of opportunity? Recent trends in intergenerational mobility. Am. Econ. Rev. 104 , 141–147 (2014).

Bakkensen, L. A. & Barrage, L. Going underwater? Flood risk belief heterogeneity and coastal home price dynamics. Rev. Financ. Stud. 35 , 3666–3709 (2022).

Principles of Environmental Justice (United Church of Christ, 2017).

Noonan, D. S. & Sadiq, A.-A. A. Flood risk management: exploring the impacts of the community rating system program on poverty and income inequality. Risk Anal. 38 , 489–503 (2018).

Huang, X. & Wang, C. Estimates of exposure to the 100-year floods in the conterminous United States using national building footprints. Int. J. Disaster Risk Reduct. 50 , 101731 (2020).

Messager, M. L., Ettinger, A. K., Murphy-Williams, M. & Levin, P. S. Fine-scale assessment of inequities in inland flood vulnerability. Appl. Geogr. 133 , 102492 (2021).

Bakkensen, L. A., Fox-Lent, C., Read, L. K. & Linkov, I. Validating resilience and vulnerability indices in the context of natural disasters. Risk Anal. 37 , 982–1004 (2017).

Spielman, S. E. et al. Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index. Nat. Hazards 100 , 417–436 (2020).

Kuhlicke, C. et al. Spinning in circles? A systematic review on the role of theory in social vulnerability, resilience and adaptation research. Glob. Environ. Change 80 , 102672 (2023).

Helgeson, C., Nicholas, R. E., Keller, K., Forest, C. E. & Tuana, N. Attention to values helps shape convergence research. Clim. Change 170 , 17 (2022).

Goal 1 - Instill Equity as a Foundation of Emergency Management (FEMA, 2021).

Anderson, E. S. What is the point of equality? Ethics 109 , 287–337 (1999).

Pollack, A. abpoll/equity_meas: V1.1.0. Zenodo https://doi.org/10.5281/zenodo.8139215 (2023).

The Pandas Development Team. pandas-dev/pandas: Pandas (v1.5.2). Zenodo https://doi.org/10.5281/zenodo.7344967 (2022).

Collaborative Data Science (Plotly Technologies, 2015).

Download references

Acknowledgements

We thank D. Spiegelman for an annotated bibliography of several key references; C. Bacon, M. Budolfson, C. Cooper, J. Doss-Gollin, A. Giang, B. Kopp, J. Kwakkel, C. Little, R. Nicholas, C. Nolte, Y. Romitti, V. Srikrishnan and N. Tuana for reading an earlier version of the manuscript and offering helpful feedback; A. Alipour, S. Baboolal, P. Hegde, X. Huang, M. May, S. Roth, S. Sreenivasan, N. Tebyanian and H. Ye for conversations and support. We also thank S. Baboolal for confirming code reproducibility and S. Wishbone for invaluable inputs. All authors acknowledge funding from the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. A.B.P. and K.K. acknowledge funding from Dartmouth College.

Author information

Authors and affiliations.

Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

Adam B. Pollack & Klaus Keller

Earth and Environmental Systems Institute, Penn State University, State College, PA, USA

Casey Helgeson

Department of Philosophy, Penn State University, State College, PA, USA

Environmental Defense Fund, Philadelphia, PA, USA

Carolyn Kousky

You can also search for this author in PubMed   Google Scholar

Contributions

A.B.P., C.H., C.K. and K.K. conceptualized the project, developed the methodology, and reviewed and edited the manuscript. A.B.P. conducted investigations, performed formal analysis and administered the project. A.B.P. and C.H. performed visualization. C.K. and K.K. acquired funding and supervised the project. A.B.P. wrote the original draft of the manuscript.

Corresponding author

Correspondence to Adam B. Pollack .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Sustainability thanks Danny Otto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Text and Table 1.

Reporting Summary

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Pollack, A.B., Helgeson, C., Kousky, C. et al. Developing more useful equity measurements for flood-risk management. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01345-3

Download citation

Received : 15 September 2023

Accepted : 10 April 2024

Published : 30 April 2024

DOI : https://doi.org/10.1038/s41893-024-01345-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.