How to Write About Coronavirus in a College Essay

Students can share how they navigated life during the coronavirus pandemic in a full-length essay or an optional supplement.

Writing About COVID-19 in College Essays

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Experts say students should be honest and not limit themselves to merely their experiences with the pandemic.

The global impact of COVID-19, the disease caused by the novel coronavirus, means colleges and prospective students alike are in for an admissions cycle like no other. Both face unprecedented challenges and questions as they grapple with their respective futures amid the ongoing fallout of the pandemic.

Colleges must examine applicants without the aid of standardized test scores for many – a factor that prompted many schools to go test-optional for now . Even grades, a significant component of a college application, may be hard to interpret with some high schools adopting pass-fail classes last spring due to the pandemic. Major college admissions factors are suddenly skewed.

"I can't help but think other (admissions) factors are going to matter more," says Ethan Sawyer, founder of the College Essay Guy, a website that offers free and paid essay-writing resources.

College essays and letters of recommendation , Sawyer says, are likely to carry more weight than ever in this admissions cycle. And many essays will likely focus on how the pandemic shaped students' lives throughout an often tumultuous 2020.

But before writing a college essay focused on the coronavirus, students should explore whether it's the best topic for them.

Writing About COVID-19 for a College Application

Much of daily life has been colored by the coronavirus. Virtual learning is the norm at many colleges and high schools, many extracurriculars have vanished and social lives have stalled for students complying with measures to stop the spread of COVID-19.

"For some young people, the pandemic took away what they envisioned as their senior year," says Robert Alexander, dean of admissions, financial aid and enrollment management at the University of Rochester in New York. "Maybe that's a spot on a varsity athletic team or the lead role in the fall play. And it's OK for them to mourn what should have been and what they feel like they lost, but more important is how are they making the most of the opportunities they do have?"

That question, Alexander says, is what colleges want answered if students choose to address COVID-19 in their college essay.

But the question of whether a student should write about the coronavirus is tricky. The answer depends largely on the student.

"In general, I don't think students should write about COVID-19 in their main personal statement for their application," Robin Miller, master college admissions counselor at IvyWise, a college counseling company, wrote in an email.

"Certainly, there may be exceptions to this based on a student's individual experience, but since the personal essay is the main place in the application where the student can really allow their voice to be heard and share insight into who they are as an individual, there are likely many other topics they can choose to write about that are more distinctive and unique than COVID-19," Miller says.

Opinions among admissions experts vary on whether to write about the likely popular topic of the pandemic.

"If your essay communicates something positive, unique, and compelling about you in an interesting and eloquent way, go for it," Carolyn Pippen, principal college admissions counselor at IvyWise, wrote in an email. She adds that students shouldn't be dissuaded from writing about a topic merely because it's common, noting that "topics are bound to repeat, no matter how hard we try to avoid it."

Above all, she urges honesty.

"If your experience within the context of the pandemic has been truly unique, then write about that experience, and the standing out will take care of itself," Pippen says. "If your experience has been generally the same as most other students in your context, then trying to find a unique angle can easily cross the line into exploiting a tragedy, or at least appearing as though you have."

But focusing entirely on the pandemic can limit a student to a single story and narrow who they are in an application, Sawyer says. "There are so many wonderful possibilities for what you can say about yourself outside of your experience within the pandemic."

He notes that passions, strengths, career interests and personal identity are among the multitude of essay topic options available to applicants and encourages them to probe their values to help determine the topic that matters most to them – and write about it.

That doesn't mean the pandemic experience has to be ignored if applicants feel the need to write about it.

Writing About Coronavirus in Main and Supplemental Essays

Students can choose to write a full-length college essay on the coronavirus or summarize their experience in a shorter form.

To help students explain how the pandemic affected them, The Common App has added an optional section to address this topic. Applicants have 250 words to describe their pandemic experience and the personal and academic impact of COVID-19.

"That's not a trick question, and there's no right or wrong answer," Alexander says. Colleges want to know, he adds, how students navigated the pandemic, how they prioritized their time, what responsibilities they took on and what they learned along the way.

If students can distill all of the above information into 250 words, there's likely no need to write about it in a full-length college essay, experts say. And applicants whose lives were not heavily altered by the pandemic may even choose to skip the optional COVID-19 question.

"This space is best used to discuss hardship and/or significant challenges that the student and/or the student's family experienced as a result of COVID-19 and how they have responded to those difficulties," Miller notes. Using the section to acknowledge a lack of impact, she adds, "could be perceived as trite and lacking insight, despite the good intentions of the applicant."

To guard against this lack of awareness, Sawyer encourages students to tap someone they trust to review their writing , whether it's the 250-word Common App response or the full-length essay.

Experts tend to agree that the short-form approach to this as an essay topic works better, but there are exceptions. And if a student does have a coronavirus story that he or she feels must be told, Alexander encourages the writer to be authentic in the essay.

"My advice for an essay about COVID-19 is the same as my advice about an essay for any topic – and that is, don't write what you think we want to read or hear," Alexander says. "Write what really changed you and that story that now is yours and yours alone to tell."

Sawyer urges students to ask themselves, "What's the sentence that only I can write?" He also encourages students to remember that the pandemic is only a chapter of their lives and not the whole book.

Miller, who cautions against writing a full-length essay on the coronavirus, says that if students choose to do so they should have a conversation with their high school counselor about whether that's the right move. And if students choose to proceed with COVID-19 as a topic, she says they need to be clear, detailed and insightful about what they learned and how they adapted along the way.

"Approaching the essay in this manner will provide important balance while demonstrating personal growth and vulnerability," Miller says.

Pippen encourages students to remember that they are in an unprecedented time for college admissions.

"It is important to keep in mind with all of these (admission) factors that no colleges have ever had to consider them this way in the selection process, if at all," Pippen says. "They have had very little time to calibrate their evaluations of different application components within their offices, let alone across institutions. This means that colleges will all be handling the admissions process a little bit differently, and their approaches may even evolve over the course of the admissions cycle."

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Persuasive Essay Guide

Persuasive Essay About Covid19

Caleb S.

How to Write a Persuasive Essay About Covid19 | Examples & Tips

11 min read

Persuasive Essay About Covid19

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Are you looking to write a persuasive essay about the Covid-19 pandemic?

Writing a compelling and informative essay about this global crisis can be challenging. It requires researching the latest information, understanding the facts, and presenting your argument persuasively.

But don’t worry! with some guidance from experts, you’ll be able to write an effective and persuasive essay about Covid-19.

In this blog post, we’ll outline the basics of writing a persuasive essay . We’ll provide clear examples, helpful tips, and essential information for crafting your own persuasive piece on Covid-19.

Read on to get started on your essay.

Arrow Down

  • 1. Steps to Write a Persuasive Essay About Covid-19
  • 2. Examples of Persuasive Essay About Covid19
  • 3. Examples of Persuasive Essay About Covid-19 Vaccine
  • 4. Examples of Persuasive Essay About Covid-19 Integration
  • 5. Examples of Argumentative Essay About Covid 19
  • 6. Examples of Persuasive Speeches About Covid-19
  • 7. Tips to Write a Persuasive Essay About Covid-19
  • 8. Common Topics for a Persuasive Essay on COVID-19 

Steps to Write a Persuasive Essay About Covid-19

Here are the steps to help you write a persuasive essay on this topic, along with an example essay:

Step 1: Choose a Specific Thesis Statement

Your thesis statement should clearly state your position on a specific aspect of COVID-19. It should be debatable and clear. For example:

Step 2: Research and Gather Information

Collect reliable and up-to-date information from reputable sources to support your thesis statement. This may include statistics, expert opinions, and scientific studies. For instance:

  • COVID-19 vaccination effectiveness data
  • Information on vaccine mandates in different countries
  • Expert statements from health organizations like the WHO or CDC

Step 3: Outline Your Essay

Create a clear and organized outline to structure your essay. A persuasive essay typically follows this structure:

  • Introduction
  • Background Information
  • Body Paragraphs (with supporting evidence)
  • Counterarguments (addressing opposing views)

Step 4: Write the Introduction

In the introduction, grab your reader's attention and present your thesis statement. For example:

Step 5: Provide Background Information

Offer context and background information to help your readers understand the issue better. For instance:

Step 6: Develop Body Paragraphs

Each body paragraph should present a single point or piece of evidence that supports your thesis statement. Use clear topic sentences, evidence, and analysis. Here's an example:

Step 7: Address Counterarguments

Acknowledge opposing viewpoints and refute them with strong counterarguments. This demonstrates that you've considered different perspectives. For example:

Step 8: Write the Conclusion

Summarize your main points and restate your thesis statement in the conclusion. End with a strong call to action or thought-provoking statement. For instance:

Step 9: Revise and Proofread

Edit your essay for clarity, coherence, grammar, and spelling errors. Ensure that your argument flows logically.

Step 10: Cite Your Sources

Include proper citations and a bibliography page to give credit to your sources.

Remember to adjust your approach and arguments based on your target audience and the specific angle you want to take in your persuasive essay about COVID-19.

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Examples of Persuasive Essay About Covid19

When writing a persuasive essay about the Covid-19 pandemic, it’s important to consider how you want to present your argument. To help you get started, here are some example essays for you to read:

Check out some more PDF examples below:

Persuasive Essay About Covid-19 Pandemic

Sample Of Persuasive Essay About Covid-19

Persuasive Essay About Covid-19 In The Philippines - Example

If you're in search of a compelling persuasive essay on business, don't miss out on our “ persuasive essay about business ” blog!

Examples of Persuasive Essay About Covid-19 Vaccine

Covid19 vaccines are one of the ways to prevent the spread of Covid-19, but they have been a source of controversy. Different sides argue about the benefits or dangers of the new vaccines. Whatever your point of view is, writing a persuasive essay about it is a good way of organizing your thoughts and persuading others.

A persuasive essay about the Covid-19 vaccine could consider the benefits of getting vaccinated as well as the potential side effects.

Below are some examples of persuasive essays on getting vaccinated for Covid-19.

Covid19 Vaccine Persuasive Essay

Persuasive Essay on Covid Vaccines

Interested in thought-provoking discussions on abortion? Read our persuasive essay about abortion blog to eplore arguments!

Examples of Persuasive Essay About Covid-19 Integration

Covid19 has drastically changed the way people interact in schools, markets, and workplaces. In short, it has affected all aspects of life. However, people have started to learn to live with Covid19.

Writing a persuasive essay about it shouldn't be stressful. Read the sample essay below to get idea for your own essay about Covid19 integration.

Persuasive Essay About Working From Home During Covid19

Searching for the topic of Online Education? Our persuasive essay about online education is a must-read.

Examples of Argumentative Essay About Covid 19

Covid-19 has been an ever-evolving issue, with new developments and discoveries being made on a daily basis.

Writing an argumentative essay about such an issue is both interesting and challenging. It allows you to evaluate different aspects of the pandemic, as well as consider potential solutions.

Here are some examples of argumentative essays on Covid19.

Argumentative Essay About Covid19 Sample

Argumentative Essay About Covid19 With Introduction Body and Conclusion

Looking for a persuasive take on the topic of smoking? You'll find it all related arguments in out Persuasive Essay About Smoking blog!

Examples of Persuasive Speeches About Covid-19

Do you need to prepare a speech about Covid19 and need examples? We have them for you!

Persuasive speeches about Covid-19 can provide the audience with valuable insights on how to best handle the pandemic. They can be used to advocate for specific changes in policies or simply raise awareness about the virus.

Check out some examples of persuasive speeches on Covid-19:

Persuasive Speech About Covid-19 Example

Persuasive Speech About Vaccine For Covid-19

You can also read persuasive essay examples on other topics to master your persuasive techniques!

Tips to Write a Persuasive Essay About Covid-19

Writing a persuasive essay about COVID-19 requires a thoughtful approach to present your arguments effectively. 

Here are some tips to help you craft a compelling persuasive essay on this topic:

Choose a Specific Angle

Start by narrowing down your focus. COVID-19 is a broad topic, so selecting a specific aspect or issue related to it will make your essay more persuasive and manageable. For example, you could focus on vaccination, public health measures, the economic impact, or misinformation.

Provide Credible Sources 

Support your arguments with credible sources such as scientific studies, government reports, and reputable news outlets. Reliable sources enhance the credibility of your essay.

Use Persuasive Language

Employ persuasive techniques, such as ethos (establishing credibility), pathos (appealing to emotions), and logos (using logic and evidence). Use vivid examples and anecdotes to make your points relatable.

Organize Your Essay

Structure your essay involves creating a persuasive essay outline and establishing a logical flow from one point to the next. Each paragraph should focus on a single point, and transitions between paragraphs should be smooth and logical.

Emphasize Benefits

Highlight the benefits of your proposed actions or viewpoints. Explain how your suggestions can improve public health, safety, or well-being. Make it clear why your audience should support your position.

Use Visuals -H3

Incorporate graphs, charts, and statistics when applicable. Visual aids can reinforce your arguments and make complex data more accessible to your readers.

Call to Action

End your essay with a strong call to action. Encourage your readers to take a specific step or consider your viewpoint. Make it clear what you want them to do or think after reading your essay.

Revise and Edit

Proofread your essay for grammar, spelling, and clarity. Make sure your arguments are well-structured and that your writing flows smoothly.

Seek Feedback 

Have someone else read your essay to get feedback. They may offer valuable insights and help you identify areas where your persuasive techniques can be improved.

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Common Topics for a Persuasive Essay on COVID-19 

Here are some persuasive essay topics on COVID-19:

  • The Importance of Vaccination Mandates for COVID-19 Control
  • Balancing Public Health and Personal Freedom During a Pandemic
  • The Economic Impact of Lockdowns vs. Public Health Benefits
  • The Role of Misinformation in Fueling Vaccine Hesitancy
  • Remote Learning vs. In-Person Education: What's Best for Students?
  • The Ethics of Vaccine Distribution: Prioritizing Vulnerable Populations
  • The Mental Health Crisis Amidst the COVID-19 Pandemic
  • The Long-Term Effects of COVID-19 on Healthcare Systems
  • Global Cooperation vs. Vaccine Nationalism in Fighting the Pandemic
  • The Future of Telemedicine: Expanding Healthcare Access Post-COVID-19

In search of more inspiring topics for your next persuasive essay? Our persuasive essay topics blog has plenty of ideas!

To sum it up,

You have read good sample essays and got some helpful tips. You now have the tools you needed to write a persuasive essay about Covid-19. So don't let the doubts stop you, start writing!

If you need professional writing help, don't worry! We've got that for you as well. is a professional persuasive essay writing service that can help you craft an excellent persuasive essay on Covid-19. Our experienced essay writer will create a well-structured, insightful paper in no time!

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Frequently Asked Questions

Are there any ethical considerations when writing a persuasive essay about covid-19.

FAQ Icon

Yes, there are ethical considerations when writing a persuasive essay about COVID-19. It's essential to ensure the information is accurate, not contribute to misinformation, and be sensitive to the pandemic's impact on individuals and communities. Additionally, respecting diverse viewpoints and emphasizing public health benefits can promote ethical communication.

What impact does COVID-19 have on society?

The impact of COVID-19 on society is far-reaching. It has led to job and economic losses, an increase in stress and mental health disorders, and changes in education systems. It has also had a negative effect on social interactions, as people have been asked to limit their contact with others.

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COVID-19 Pandemic

By: Editors

Updated: March 11, 2024 | Original: April 25, 2023


The outbreak of the infectious respiratory disease known as COVID-19 triggered one of the deadliest pandemics in modern history. COVID-19 claimed nearly 7 million lives worldwide. In the United States, deaths from COVID-19 exceeded 1.1 million, nearly twice the American death toll from the 1918 flu pandemic . The COVID-19 pandemic also took a heavy toll economically, politically and psychologically, revealing deep divisions in the way that Americans viewed the role of government in a public health crisis, particularly vaccine mandates. While the United States downgraded its “national emergency” status over the pandemic on May 11, 2023, the full effects of the COVID-19 pandemic will reverberate for decades.

A New Virus Breaks Out in Wuhan, China

In December 2019, the China office of the World Health Organization (WHO) received news of an isolated outbreak of a pneumonia-like virus in the city of Wuhan. The virus caused high fevers and shortness of breath, and the cases seemed connected to the Huanan Seafood Wholesale Market in Wuhan, which was closed by an emergency order on January 1, 2020.

After testing samples of the unknown virus, the WHO identified it as a novel type of coronavirus similar to the deadly SARS virus that swept through Asia from 2002-2004. The WHO named this new strain SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The first Chinese victim of SARS-CoV-2 died on January 11, 2020.

Where, exactly, the novel virus originated has been hotly debated. There are two leading theories. One is that the virus jumped from animals to humans, possibly carried by infected animals sold at the Wuhan market in late 2019. A second theory claims the virus escaped from the Wuhan Institute of Virology, a research lab that was studying coronaviruses. U.S. intelligence agencies maintain that both origin stories are “plausible.”

The First COVID-19 Cases in America

The WHO hoped that the virus outbreak would be contained to Wuhan, but by mid-January 2020, infections were reported in Thailand, Japan and Korea, all from people who had traveled to China.

On January 18, 2020, a 35-year-old man checked into an urgent care center near Seattle, Washington. He had just returned from Wuhan and was experiencing a fever, nausea and vomiting. On January 21, he was identified as the first American infected with SARS-CoV-2.

In reality, dozens of Americans had contracted SARS-CoV-2 weeks earlier, but doctors didn’t think to test for a new type of virus. One of those unknowingly infected patients died on February 6, 2020, but her death wasn’t confirmed as the first American casualty until April 21.

On February 11, 2020, the WHO released a new name for the disease causing the deadly outbreak: Coronavirus Disease 2019 or COVID-19. By mid-March 2020, all 50 U.S. states had reported at least one positive case of COVID-19, and nearly all of the new infections were caused by “community spread,” not by people who contracted the disease while traveling abroad. 

At the same time, COVID-19 had spread to 114 countries worldwide, killing more than 4,000 people and infecting hundreds of thousands more. On March 11, the WHO made it official and declared COVID-19 a pandemic.

The World Shuts Down

New York City's famous Times Square is seen nearly empty due to the COVID-19 pandemic on March 16, 2020.

Pandemics are expected in a globally interconnected world, so emergency plans were in place. In the United States, health officials at the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) set in motion a national response plan developed for flu pandemics.

State by state and city by city, government officials took emergency measures to encourage “ social distancing ,” one of the many new terms that became part of the COVID-19 vocabulary. Travel was restricted. Schools and churches were closed. With the exception of “essential workers,” all offices and businesses were shuttered. By early April 2020, more than 316 million Americans were under a shelter-in-place or stay-at-home order.

With more than 1,000 deaths and nearly 100,000 cases, it was clear by April 2020 that COVID-19 was highly contagious and virulent. What wasn’t clear, even to public health officials, was how individuals could best protect themselves from COVID-19. In the early weeks of the outbreak, the CDC discouraged people from buying face masks, because officials feared a shortage of masks for doctors and hospital workers.

By April 2020, the CDC revised its recommendations, encouraging people to wear masks in public, to socially distance and to wash hands frequently. President Donald Trump undercut the CDC recommendations by emphasizing that masking was voluntary and vowing not to wear a mask himself. This was just the beginning of the political divisions that hobbled the COVID-19 response in America.

Global Financial Markets Collapse

In the early months of the COVID-19 pandemic, with billions of people worldwide out of work, stuck at home, and fretting over shortages of essential items like toilet paper , global financial markets went into a tailspin.

In the United States, share prices on the New York Stock Exchange plummeted so quickly that the exchange had to shut down trading three separate times. The Dow Jones Industrial Average eventually lost 37 percent of its value, and the S&P 500 was down 34 percent.

Business closures and stay-at-home orders gutted the U.S. economy. The unemployment rate skyrocketed, particularly in the service sector (restaurant and other retail workers). By May 2020, the U.S. unemployment rate reached 14.7 percent, the highest jobless rate since the Great Depression . 

All across America, households felt the pinch of lost jobs and lower wages. Food insecurity reached a peak by December 2020 with 30 million American adults—a full 14 percent—reporting that their families didn’t get enough to eat in the past week.

The economic effects of the COVID-19 pandemic, like its health effects, weren’t experienced equally. Black, Hispanic and Native Americans suffered from unemployment and food insecurity at significantly higher rates than white Americans. 

Congress tried to avoid a complete economic collapse by authorizing a series of COVID-19 relief packages in 2020 and 2021, which included direct stimulus checks for all American families.

The Race for a Vaccine

A new vaccine typically takes 10 to 15 years to develop and test, but the world couldn’t wait that long for a COVID-19 vaccine. The U.S. Department of Health and Human Services (HHS) under the Trump administration launched “ Operation Warp Speed ,” a public-private partnership which provided billions of dollars in upfront funding to pharmaceutical companies to rapidly develop vaccines and conduct clinical trials.

The first clinical trial for a COVID-19 vaccine was announced on March 16, 2020, only days after the WHO officially classified COVID-19 as a pandemic. The vaccines developed by Moderna and Pfizer were the first ever to employ messenger RNA, a breakthrough technology. After large-scale clinical trials, both vaccines were found to be greater than 95 percent effective against infection with COVID-19.

A nurse from New York officially became the first American to receive a COVID-19 vaccine on December 14, 2020. Ten days later, more than 1 million vaccines had been administered, starting with healthcare workers and elderly residents of nursing homes. As the months rolled on, vaccine availability was expanded to all American adults, and then to teenagers and all school-age children.

By the end of the pandemic in early 2023, more than 670 million doses of COVID-19 vaccines had been administered in the United States at a rate of 203 doses per 100 people. Approximately 80 percent of the U.S. population received at least one COVID-19 shot, but vaccination rates were markedly lower among Black, Hispanic and Native Americans.

COVID-19 Deaths Heaviest Among Elderly and People of Color

In America, the COVID-19 pandemic impacted everyone’s lives, but those who died from the disease were far more likely to be older and people of color.

Of the more than 1.1 million COVID deaths in the United States, 75 percent were individuals who were 65 or older. A full 93 percent of American COVID-19 victims were 50 or older. Throughout the emergence of COVID-19 variants and the vaccine rollouts, older Americans remained the most at-risk for being hospitalized and ultimately dying from the disease.

Black, Hispanic and Native Americans were also at a statistically higher risk of developing life-threatening COVID-19 systems and succumbing to the disease. For example, Black and Hispanic Americans were twice as likely to be hospitalized from COVID-19 than white Americans. The COVID-19 pandemic shined light on the health disparities between racial and ethnic groups driven by systemic racism and lower access to healthcare.

Mental health also worsened during the COVID-19 pandemic. The anxiety of contracting the disease, and the stresses of being unemployed or confined at home, led to unprecedented numbers of Americans reporting feelings of depression and suicidal ideation.

A Time of Social & Political Upheaval

Thousands gather for the ''Get Your Knee Off Our Necks'' march in Washington DC USA, on August 28, 2020.

In the United States, the three long years of the COVID-19 pandemic paralleled a time of heightened political contention and social upheaval.

When George Floyd was killed by Minneapolis police on May 25, 2020, it sparked nationwide protests against police brutality and energized the Black Lives Matter movement. Because so many Americans were out of work or home from school due to COVID-19 shutdowns, unprecedented numbers of people from all walks of life took to the streets to demand reforms.

Instead of banding together to slow the spread of the disease, Americans became sharply divided along political lines in their opinions of masking requirements, vaccines and social distancing.

By March 2024, in signs that the pandemic was waning, the CDC issued new guidelines for people who were recovering from COVID-19. The agency said those infected with the virus no longer needed to remain isolated for five days after symptoms. And on March 10, 2024, the Johns Hopkins Coronavirus Resource Center stopped collecting data for its highly referenced COVID-19 dashboard.

Still, an estimated 17 percent of U.S. adults reported having experienced symptoms of long COVID, according to the Household Pulse Survey. The medical community is still working to understand the causes behind long COVID, which can afflict a patient for weeks, months or even years.

introduction essay about covid 19 pandemic


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“CDC Museum COVID Timeline.” Centers for Disease Control and Prevention . “Coronavirus: Timeline.” U.S. Department of Defense . “COVID-19 and Related Vaccine Development and Research.” Mayo Clinic . “COVID-19 Cases and Deaths by Race/Ethnicity: Current Data and Changes Over Time.” Kaiser Family Foundation . “Number of COVID-19 Deaths in the U.S. by Age.” Statista . “The Pandemic Deepened Fault Lines in American Society.” Scientific American . “Tracking the COVID-19 Economy’s Effects on Food, Housing, and Employment Hardships.” Center on Budget and Policy Priorities . “U.S. Confirmed Country’s First Case of COVID-19 3 Years Ago.” CNN .

introduction essay about covid 19 pandemic

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The Impact of COVID-19 Pandemic

The year 2019 will forever be engraved in many people’s hearts and minds as the time when a deadly virus known as the coronavirus disease 2019 (COVID-19) invaded almost all the sectors, thereby disrupting daily activities. It is described as a communicable respiratory illness which is triggered by a new strain of coronavirus which leads to various ailments in human beings. There is currently no known cure or vaccine for the virus as scientists worldwide are still trying to learn about the illness to respond appropriately through research (Goodell, 2020). This paper aims at exploring the effects that the pandemic has had on society regarding the economy, social life, education, religion, and family.

The emergence of the pandemic, which began in China-2019, quickly spread to other nations across the world with devastating effects on their economies As a way of containing the disease, many countries instituted strict measures, such as curfews, the mandatory wearing of masks, and social distancing of 1 meter apart (Goodell, 2020). Covid-19 has significantly changed the way these preventive methods relate with each concerning trade matters. The majority of the states affected opted to close their borders as fear among the citizens increased. The implementation of the strict rules interfered with the business operations of many nations. It became difficult for international trade to continue as a result of the closed borders. Most businesses have also had to close due to financial constraints.

When it comes to socialization, people have been forced to use other means to meet their friends and families across the world. Social media platforms have seen an increased usage during this difficult time as people try to find new ways of socializing. It has happened especially in such countries as Australia, where the restrictions were extreme as it enforced a lockdown for close to a hundred days (Goodell, 2020). The use of masks is also quickly becoming the new norm across numerous states. Unlike in developed countries where the governments have offered their citizens some aid mostly in terms of cash transfers, developing countries have struggled to balance between the people’s livelihood and the containment of the Covid-19. As such, most people have turned to social media platforms as a medium of communication and socialization due to lockdowns.

Learning institutions have also not been spared by the Covid-19 pandemic. Most countries affected by the spread of the virus were forced to suspend their educational curriculum calendar to allow children and university students to stay home until the time when the disease is finally neutralized (Goodell, 2020). However, students and parents have been pushing the governments to resume schools with clear protocols which ensure that both the students and the teachers follow the rules, including the mandatory wearing of masks. Religion has also been significantly affected as it has become difficult for people to seek for spiritual nourishment (Goodell, 2020). Many religious leaders have had to devise other ways of reaching out to the congregates. For example, many churches now have to move their services online by using such platforms as YouTube, Facebook, Zoom, among others to convey essential teachings.

Covid-19 has also directly affected many families across the world, as the majority have succumbed to the disease. The United States of America and Italy are some of the pandemic’s worst casualties, where many people were killed by the lethal virus (Goodell, 2020). Some people have in the end lost more than one member of the family because of the disease, and in some worse case scenarios, the illness has claimed a whole family.

In conclusion, this paper has highlighted the impacts of the Covid-19 pandemic on the economy, social life, education, religion, and family units. Many countries and businesses had underestimated the disease’s impact before they later suffered from the consequences. Therefore, international bodies, such as the World Health Organization, need to help developing countries establish critical management healthcare systems, which can help to deal with the future pandemics.

Goodell, J. W. (2020). COVID-19 and finance: Agendas for future research. Finance Research Letters , 35 , 101512. Web.

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Writing about COVID-19 in a college admission essay

by: Venkates Swaminathan | Updated: September 14, 2020

Print article

Writing about COVID-19 in your college admission essay

For students applying to college using the CommonApp, there are several different places where students and counselors can address the pandemic’s impact. The different sections have differing goals. You must understand how to use each section for its appropriate use.

The CommonApp COVID-19 question

First, the CommonApp this year has an additional question specifically about COVID-19 :

Community disruptions such as COVID-19 and natural disasters can have deep and long-lasting impacts. If you need it, this space is yours to describe those impacts. Colleges care about the effects on your health and well-being, safety, family circumstances, future plans, and education, including access to reliable technology and quiet study spaces. Please use this space to describe how these events have impacted you.

This question seeks to understand the adversity that students may have had to face due to the pandemic, the move to online education, or the shelter-in-place rules. You don’t have to answer this question if the impact on you wasn’t particularly severe. Some examples of things students should discuss include:

  • The student or a family member had COVID-19 or suffered other illnesses due to confinement during the pandemic.
  • The candidate had to deal with personal or family issues, such as abusive living situations or other safety concerns
  • The student suffered from a lack of internet access and other online learning challenges.
  • Students who dealt with problems registering for or taking standardized tests and AP exams.

Jeff Schiffman of the Tulane University admissions office has a blog about this section. He recommends students ask themselves several questions as they go about answering this section:

  • Are my experiences different from others’?
  • Are there noticeable changes on my transcript?
  • Am I aware of my privilege?
  • Am I specific? Am I explaining rather than complaining?
  • Is this information being included elsewhere on my application?

If you do answer this section, be brief and to-the-point.

Counselor recommendations and school profiles

Second, counselors will, in their counselor forms and school profiles on the CommonApp, address how the school handled the pandemic and how it might have affected students, specifically as it relates to:

  • Grading scales and policies
  • Graduation requirements
  • Instructional methods
  • Schedules and course offerings
  • Testing requirements
  • Your academic calendar
  • Other extenuating circumstances

Students don’t have to mention these matters in their application unless something unusual happened.

Writing about COVID-19 in your main essay

Write about your experiences during the pandemic in your main college essay if your experience is personal, relevant, and the most important thing to discuss in your college admission essay. That you had to stay home and study online isn’t sufficient, as millions of other students faced the same situation. But sometimes, it can be appropriate and helpful to write about something related to the pandemic in your essay. For example:

  • One student developed a website for a local comic book store. The store might not have survived without the ability for people to order comic books online. The student had a long-standing relationship with the store, and it was an institution that created a community for students who otherwise felt left out.
  • One student started a YouTube channel to help other students with academic subjects he was very familiar with and began tutoring others.
  • Some students used their extra time that was the result of the stay-at-home orders to take online courses pursuing topics they are genuinely interested in or developing new interests, like a foreign language or music.

Experiences like this can be good topics for the CommonApp essay as long as they reflect something genuinely important about the student. For many students whose lives have been shaped by this pandemic, it can be a critical part of their college application.

Want more? Read 6 ways to improve a college essay , What the &%$! should I write about in my college essay , and Just how important is a college admissions essay? .

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Essay On Covid-19: 100, 200 and 300 Words

introduction essay about covid 19 pandemic

  • Updated on  
  • Apr 30, 2024

Essay on Covid-19

COVID-19, also known as the Coronavirus, is a global pandemic that has affected people all around the world. It first emerged in a lab in Wuhan, China, in late 2019 and quickly spread to countries around the world. This virus was reportedly caused by SARS-CoV-2. Since then, it has spread rapidly to many countries, causing widespread illness and impacting our lives in numerous ways. This blog talks about the details of this virus and also drafts an essay on COVID-19 in 100, 200 and 300 words for students and professionals. 

Table of Contents

  • 1 Essay On COVID-19 in English 100 Words
  • 2 Essay On COVID-19 in 200 Words
  • 3 Essay On COVID-19 in 300 Words
  • 4 Short Essay on Covid-19

Essay On COVID-19 in English 100 Words

COVID-19, also known as the coronavirus, is a global pandemic. It started in late 2019 and has affected people all around the world. The virus spreads very quickly through someone’s sneeze and respiratory issues.

COVID-19 has had a significant impact on our lives, with lockdowns, travel restrictions, and changes in daily routines. To prevent the spread of COVID-19, we should wear masks, practice social distancing, and wash our hands frequently. 

People should follow social distancing and other safety guidelines and also learn the tricks to be safe stay healthy and work the whole challenging time. 

Also Read: National Safe Motherhood Day 2023

Essay On COVID-19 in 200 Words

COVID-19 also known as coronavirus, became a global health crisis in early 2020 and impacted mankind around the world. This virus is said to have originated in Wuhan, China in late 2019. It belongs to the coronavirus family and causes flu-like symptoms. It impacted the healthcare systems, economies and the daily lives of people all over the world. 

The most crucial aspect of COVID-19 is its highly spreadable nature. It is a communicable disease that spreads through various means such as coughs from infected persons, sneezes and communication. Due to its easy transmission leading to its outbreaks, there were many measures taken by the government from all over the world such as Lockdowns, Social Distancing, and wearing masks. 

There are many changes throughout the economic systems, and also in daily routines. Other measures such as schools opting for Online schooling, Remote work options available and restrictions on travel throughout the country and internationally. Subsequently, to cure and top its outbreak, the government started its vaccine campaigns, and other preventive measures. 

In conclusion, COVID-19 tested the patience and resilience of the mankind. This pandemic has taught people the importance of patience, effort and humbleness. 

Also Read : Essay on My Best Friend

Essay On COVID-19 in 300 Words

COVID-19, also known as the coronavirus, is a serious and contagious disease that has affected people worldwide. It was first discovered in late 2019 in Cina and then got spread in the whole world. It had a major impact on people’s life, their school, work and daily lives. 

COVID-19 is primarily transmitted from person to person through respiratory droplets produced and through sneezes, and coughs of an infected person. It can spread to thousands of people because of its highly contagious nature. To cure the widespread of this virus, there are thousands of steps taken by the people and the government. 

Wearing masks is one of the essential precautions to prevent the virus from spreading. Social distancing is another vital practice, which involves maintaining a safe distance from others to minimize close contact.

Very frequent handwashing is also very important to stop the spread of this virus. Proper hand hygiene can help remove any potential virus particles from our hands, reducing the risk of infection. 

In conclusion, the Coronavirus has changed people’s perspective on living. It has also changed people’s way of interacting and how to live. To deal with this virus, it is very important to follow the important guidelines such as masks, social distancing and techniques to wash your hands. Getting vaccinated is also very important to go back to normal life and cure this virus completely.

Also Read: Essay on Abortion in English in 650 Words

Short Essay on Covid-19

Please find below a sample of a short essay on Covid-19 for school students:

Also Read: Essay on Women’s Day in 200 and 500 words

to write an essay on COVID-19, understand your word limit and make sure to cover all the stages and symptoms of this disease. You need to highlight all the challenges and impacts of COVID-19. Do not forget to conclude your essay with positive precautionary measures.

Writing an essay on COVID-19 in 200 words requires you to cover all the challenges, impacts and precautions of this disease. You don’t need to describe all of these factors in brief, but make sure to add as many options as your word limit allows.

The full form for COVID-19 is Corona Virus Disease of 2019.

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  • Volume 5, Issue 7
  • The COVID-19 pandemic: diverse contexts; different epidemics—how and why?
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  • Wim Van Damme 1 ,
  • Ritwik Dahake 2 ,
  • Alexandre Delamou 3 ,
  • Brecht Ingelbeen 1 ,
  • Edwin Wouters 4 , 5 ,
  • Guido Vanham 6 , 7 ,
  • Remco van de Pas 1 ,
  • Jean-Paul Dossou 1 , 8 ,
  • Seye Abimbola 10 , 11 ,
  • Stefaan Van der Borght 12 ,
  • Devadasan Narayanan 13 ,
  • Gerald Bloom 14 ,
  • Ian Van Engelgem 15 ,
  • Mohamed Ali Ag Ahmed 16 ,
  • Joël Arthur Kiendrébéogo 1 , 17 , 18 ,
  • Kristien Verdonck 1 ,
  • Vincent De Brouwere 1 ,
  • Kéfilath Bello 8 ,
  • Helmut Kloos 19 ,
  • Peter Aaby 20 ,
  • Andreas Kalk 21 ,
  • Sameh Al-Awlaqi 22 ,
  • NS Prashanth 23 ,
  • Jean-Jacques Muyembe-Tamfum 24 ,
  • Placide Mbala 24 ,
  • Steve Ahuka-Mundeke 24 ,
  • Yibeltal Assefa 25
  • 1 Department of Public Health , Institute of Tropical Medicine , Antwerpen , Belgium
  • 2 Independent Researcher , Bengaluru , India
  • 3 Africa Centre of Excellence for Prevention and Control of Transmissible Diseases , Gamal Abdel Nasser University of Conakry , Conakry , Guinea
  • 4 Department of Sociology and Centre for Population , University of Antwerp , Antwerpen , Belgium
  • 5 Centre for Health Systems Research and Development , University of the Free State—Bloemfontein Campus , Bloemfontein , Free State , South Africa
  • 6 Biomedical Department , Institute of Tropical Medicine , Antwerpen , Belgium
  • 7 Biomedical Department , University of Antwerp , Antwerpen , Belgium
  • 8 Public Health , Centre de recherche en Reproduction Humaine et en Démographie , Cotonou , Benin
  • 9 National Institute of Public Health , Phnom Penh , Cambodia
  • 10 School of Public Health , University of Sydney , Sydney , New South Wales , Australia
  • 11 The George Institute for Global Health , Sydney , New South Wales , Australia
  • 12 Board Member , Institute of Tropical Medicine , Antwerpen , Belgium
  • 13 Health Systems Transformation Platform , New Delhi , India
  • 14 Health and Nutrition Cluster , Institute of Development Studies , Brighton , UK
  • 15 European Commission Directorate General for Civil Protection and Humanitarian Aid Operations , Kinshasa , Democratic Republic of Congo
  • 16 University of Sherbrooke , Sherbrooke , Quebec , Canada
  • 17 Public Health , University of Ouagadougou Health Sciences Training and Research Unit , Ouagadougou , Burkina Faso
  • 18 Heidelberg Institute of Global Health, Medical Faculty and University Hospital , Heidelberg University , Heidelberg , Germany
  • 19 Department of Epidemiology and Biostatistics , University of California San Francisco , San Francisco , California , USA
  • 20 INDEPTH Network , Bandim Health Project , Bissau , Guinea-Bissau
  • 21 Bureau GIZ à Kinshasa , Kinshasa , Democratic Republic of Congo
  • 22 Center for International Health Protection , Robert Koch Institute , Berlin , Germany
  • 23 Health Equity Cluster , Institute of Public Health , Bengaluru , India
  • 24 Institut National de Recherche Biomédicale , Kinshasa , Democratic Republic of Congo
  • 25 School of Public Health , The University of Queensland , Brisbane , Queensland , Australia
  • Correspondence to Professor Wim Van Damme; wvdamme{at}

It is very exceptional that a new disease becomes a true pandemic. Since its emergence in Wuhan, China, in late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has spread to nearly all countries of the world in only a few months. However, in different countries, the COVID-19 epidemic takes variable shapes and forms in how it affects communities. Until now, the insights gained on COVID-19 have been largely dominated by the COVID-19 epidemics and the lockdowns in China, Europe and the USA. But this variety of global trajectories is little described, analysed or understood. In only a few months, an enormous amount of scientific evidence on SARS-CoV-2 and COVID-19 has been uncovered (knowns). But important knowledge gaps remain (unknowns). Learning from the variety of ways the COVID-19 epidemic is unfolding across the globe can potentially contribute to solving the COVID-19 puzzle. This paper tries to make sense of this variability—by exploring the important role that context plays in these different COVID-19 epidemics; by comparing COVID-19 epidemics with other respiratory diseases, including other coronaviruses that circulate continuously; and by highlighting the critical unknowns and uncertainties that remain. These unknowns and uncertainties require a deeper understanding of the variable trajectories of COVID-19. Unravelling them will be important for discerning potential future scenarios, such as the first wave in virgin territories still untouched by COVID-19 and for future waves elsewhere.

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Summary box

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has spread to nearly all countries of the world in only a few months. It is unique that an emerging respiratory virus becomes a pandemic, and can continue human-to-human transmission unabated, probably permanently.

Depending on the context, the trajectory and the impact of the COVID-19 epidemic vary widely across affected countries. This is in fact the case with most infectious diseases.

Despite limited initial knowledge on COVID-19, most societies have deployed draconian measures, including lockdowns, to contain the virus and mitigate its impact. This had variable success, but invariably with profound socioeconomic collateral effects.

Through research and rapid sharing of its findings, progressively more insights on SARS-CoV-2 and COVID-19 have been uncovered (knowns), mainly based on evidence from China, Europe and the USA; however, important knowledge gaps remain (unknowns).

The different COVID-19 epidemics and the responses unfolding in the Global South are little described, analysed or understood. Insights from these less researched contexts are important for discerning potential future scenarios, not only for the first wave in virgin territories still untouched by COVID-19, but also for future waves.

More understanding of lived experiences of people in a variety of contexts is necessary to get a full global picture and allow learning from this variety.

BMJ Global Health and Emerging Voices for Global Health have launched a call for such on-the-ground narratives and analyses on the epidemics of, and responses to, COVID-19.


Late in 2019, a cluster of acute respiratory disease in Wuhan, China, was attributed to a new coronavirus, 1–3 later named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 4 It was soon discovered that the virus is easily transmitted, can cause severe disease and can be quite lethal especially in the elderly and those with comorbidities. 5–8 The new human disease is called COVID-19. 9 Soon it became clear that its global spread was unstoppable. Even with draconian containment measures, such as strict movement restrictions, the so-called lockdown, it spread, and within a few months reached almost all countries and was declared a pandemic by the WHO. 10 Table 1 summarises key events in the unfolding of the COVID-19 pandemic, from December 2019 to May 2020.

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Key events in the COVID-19 pandemic, December 2019–May 2020

This progression is quite unique. New human pathogens emerge frequently from an animal host, but most cause only a local outbreak. Human-to-human transmission stops at some point, and the virus can only re-emerge as a human pathogen from its animal host. Only very rarely does an emerging pathogen become a pandemic. Over the past decades, a totally new pathogen emerged, caused serious disease, and spread around the globe continuously only once before: the HIV. It seems increasingly likely that SARS-CoV-2 transmission will be continuing. All countries are now facing their own ‘COVID-19 epidemic’.

In only a few months, the scientific community has started to learn the virus’s characteristics and its manifestations in different contexts. 11 But we fail to understand fully why the virus spreads at different speeds and affects populations differently. Our main objective is to make sense of those different expressions of the COVID-19 pandemic, to understand why COVID-19 follows variable trajectories in ways that are often quite different from the collective image created by the mediatisation of the dramatic COVID-19 epidemics in densely populated areas.

We start by exploring the role of context, followed by a brief summary of what is already known at the time of writing about SARS-CoV-2 and COVID-19. We then compare these knowns with what is known of some other viral respiratory pathogens and identify the critical unknowns. We also discuss the coping strategies and collective strategies implemented to contain and mitigate the effect of the epidemic. We finally look ahead to potential future scenarios.

The unfolding COVID-19 pandemic: importance of context

Initially, human-to-human transmission was documented in family/friends clusters. 12–17 Progressively, it became clear that superspreading events, typically during social gatherings such as parties, religious services, weddings, sports events and carnival celebrations, have played an important role. 18–21 Dense transmission has also been documented in hospitals 22 and nursing homes possibly through aerosols. 23 24

SARS-CoV-2 has spread around the world through international travellers. The timing of the introduction of SARS-CoV-2 has largely depended on the intensity of connections with locations with ongoing COVID-19 epidemics; thus, it reached big urban centres first and, within these, often the most affluent groups. From there, the virus has spread at variable speeds to other population groups. 25 26

As of May 2020, the most explosive COVID-19 epidemics observed have been in densely populated areas in temperate climates in relatively affluent countries. 27 The COVID-19 pandemic and the lockdowns have been covered intensively in the media and have shaped our collective image of the COVID-19 epidemic, both in the general public and in the scientific community.

The COVID-19 epidemic has spread more slowly and less intensively in rural areas, in Africa and the Indian subcontinent, and the rural areas of low and lower-middle income countries (LICs/LMICs). Not only the media but also the scientific community has paid much less attention to these realities, emerging later and spreading more slowly.

The dominant thinking has been that it is only a question of time before dramatic epidemics occur everywhere. This thinking, spread globally by international public health networks, has been substantiated by predictive mathematical models based largely on data from the epidemics of the Global North. However, what has been observed elsewhere is quite different although not necessarily less consequential. 28

The effects of the COVID-19 epidemic manifest in peculiar ways in each context. In the early stages of the COVID-19 epidemic in sub-Saharan Africa, the virus first affected the urban elites with international connections. From there, it was seeded to other sections of the society more slowly. In contrast, the collateral effects of a lockdown, even partial in many cases, are mostly felt by the urban poor, as ‘stay home’ orders abruptly intensify hardship for those earning their daily living in the informal urban economy. Governments of LICs/LMICs lack the budgetary space to grant generous benefit packages to counter the socioeconomic consequences. International agencies are very thinly spread, as the pandemic has been concurrent everywhere. Donor countries have focused mainly on their own COVID-19 epidemics.

The epidemic is thus playing out differently in different contexts. Many factors might explain SARS-CoV-2 transmission dynamics. Climate, population structure, social practices, pre-existing immunity and many other variables that have been explored are summarised in table 2 .

Contextual variables potentially influencing transmission of severe acute respiratory syndrome coronavirus 2

Although all these variables probably play some role, many uncertainties remain. It is difficult to assess how much these variables influence transmission in different contexts. It is even more difficult to assess how they interact and change over time and influence transmission among different social groups, resulting in the peculiar COVID-19 epidemic in any particular context.

Insights from other viruses

We do not attempt to give a complete overview of viruses but select only those viruses that emerged recently and caused epidemics such as Ebola, that have obvious similarities in transmission patterns such as influenza and measles, or that are closely related such as other coronaviruses.

Emerging viral respiratory pathogens

Respiratory viruses such as severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and avian influenza A and also Ebola have originated from animal hosts and caused human diseases ( table 3 ). These viruses do not continuously circulate from human to human. They create an outbreak only when there is interspecies cross-over transmission, most frequently from bats to another animal host.

Examples of emerging human respiratory viral diseases without sustained human-to-human transmission

The first human case of a disease from an emerging viral pathogen, the ‘index case’ or ‘patient zero’, is invariably someone in close contact with the originating animal host or an intermediary animal host. If this contact occurs in a remote rural community, the spread is usually slow, at low intensity, and could fade out before the pathogen gets a chance to spread to another community. The spread can suddenly intensify if seeded in a densely populated community, frequently in a particular context such as a hospital or during a social event, often referred to as a superspreading event. When the spread reaches a city, it can become a major outbreak, from where it can spread further; this happened with SARS-CoV in Hong Kong in 2003 and with Ebola in Conakry, Freetown and Monrovia in 2014–2016. 29 30 But at some stage human-to-human transmission is interrupted and the outbreak stops.

Only very exceptionally can a new viral pathogen sustain continuous human-to-human transmission. Other viral diseases such as measles and influenza are ‘old’ diseases; they have been studied in great depth. What can we learn from them?

Measles and influenza: the importance of context

It is thought that measles emerged thousands of years ago in the Middle East. It is assumed that a cross-over occurred from the rinderpest virus, 31 to become the human measles virus. Measles has since spread around the globe in continuous human-to-human transmission. When measles, along with other viruses such as smallpox and influenza, was introduced in the Americas by European conquerors, it contributed to a massive die-off of up to 90% of the original population. 32

The transmission dynamics of SARS-CoV-2 can be compared with influenza. Influenza typically causes yearly epidemics in temperate climates during winter with less seasonal patterns in tropical or subtropical regions. 33 34 In hotter climates, such as in sub-Saharan Africa 33 or South and Southeast Asia, 34 it is transmitted year round, often not identified as influenza. Such different epidemic patterns of influenza are still incompletely understood but thought to be associated with temperature and humidity and human behavioural factors such as indoor crowding. 35

But, in contrast to SARS-CoV-2, the influenza virus is not new. Influenza is a very old disease, certainly circulating for several centuries. It has infected most human beings living on the planet already, many of them several times, leaving some immunity but no durable protection. The virus also mutates, giving rise to a new dominant strain every influenza season. Influenza is every year a slightly different virus (due to antigenic drift as a result of progressive mutations) with major differences every few decades (antigenic shift as a result of recombination with novel strains).

One such antigenic shift resulted in the 1918 H1N1 ‘Spanish’ Influenza pandemic, which had an estimated case fatality rate (CFR) of 2%–3%, killing millions. 36 Box 1 summarises some key facts about H1N1, including factors thought to be associated with its high CFR.

Pandemic H1N1 influenza, 1918–2009

The 1918 H1N1 virus probably infected one-third of the world’s population at that time (or ~500 million people). 84

The pandemic had three waves in quick succession; the second wave, in 1919, was worse than the first wave. 84

High mortality, especially in younger persons (5–15 years; ~25% of total deaths) in the 1918 pandemic, may have been due to antibody-dependent enhancement and ‘cytokine storms’. 84 Another possible explanation is that older persons had some protective cross-immunity from previous influenza outbreaks while younger persons did not.

H1N1 continued to circulate along with seasonal influenza viruses, often recombining to produce more severe local outbreaks, including other pandemics between 1918 and 2009, giving it the nickname ‘mother of all pandemics’.

The original 1918 H1N1 strain was replaced by A(H1N1)pdm09 virus that resulted from an antigenic shift and caused the 2009 H1N1 influenza pandemic.

The 2009 H1N1 virus originated in pigs in central Mexico in March 2009 and was responsible for an estimated 284 000 deaths worldwide with an estimated CFR<0.1%. 85 86

During the 2009 pandemic, mortality was much lower than in the 1918 pandemic. Higher mortality in persons younger than 65 years was related to cytokine storms. 87 A role of protective cross-immunity from previous influenza strains in older persons has been suggested.

After August 2010, the A(H1N1)pdm09 virus appeared to have integrated with circulating strains of influenza and continues to cause localised seasonal influenza outbreaks worldwide. 88

A major difference between COVID-19 and influenza is that SARS-CoV-2 is a new pathogen and influenza is not. At the time of writing (May 2020), SARS-CoV-2 has triggered an immune response in over 5 million confirmed infections (and probably in many more), definitely too few to create anything close to herd immunity. Calculations using an estimated reproductive number (R0) for SARS-CoV-2 suggest that herd immunity would require at least 60% of the population to have protective immunity (see box 2 ). 37

On the use of mathematical models during epidemics

 A dominant way of studying the transmission dynamics of an infectious disease such as COVID-19, and predicting the amplitude and peak of the epidemic in a population (city, province, country) and analysing the effect of control measures is using mathematical models. Based on available data and several assumptions, a model attempts to predict the course of the epidemic, the expected number of infections, clinical cases and deaths over time. Critical is the effective reproductive number (Rt). When Rt >1, the number of cases in a population increases; when Rt <1, the number of cases decreases. A relatively simple and widely used model is the susceptible-exposed-infectious-recovered model, as used in the two papers recently published in BMJ Global Health on COVID-19 in Africa. 67 89 There are many more types of models, with varying degrees of complexity.

 The use of such models has strengths and limitations. Building a mathematical model implies trade-offs between accuracy, transparency, flexibility and timeliness. A difficulty, in general, is that the parameters on which the model is based, the so-called assumptions are frequently uncertain ( table 7 ) and predictions can vary widely if any of the parameters are modestly different. This uncertainty is captured in a sensitivity analysis, leading to various possible quantitative outcomes, usually expressed as a range of plausible possibilities, between ‘worst-case’ and ‘best-case’ scenarios.

 With a new disease such as COVID-19, certainly at the start of the outbreak, the parameters had to be based on very limited data from a particular context. However, many variables can widely differ across communities as they critically depend on contextual factors ( table 2 ). In mathematical models, all such uncertainties and unknowns are somehow hidden in the complex formulae of the model, as a quasi ‘black box’. Few people have the knowledge and skill to ‘open up the black box’.

 As uncertainties in COVID-19 are large, the range of possibilities produced by a model is wide, with the worst-case scenario typically predicting catastrophic numbers of cases and deaths. Such predictions are often misunderstood by journalists, practitioners and policy-makers, with worst-case estimates getting the most attention, 68 not specifying the huge uncertainties.

Knowns, uncertainties and unknowns about COVID-19, as of May 2020

Like COVID-19, measles and influenza have different epidemic patterns in different contexts. This also is the case for cholera, tuberculosis, HIV/AIDS and most infectious diseases. The difference in patterns is most pronounced and so is easily understood with vector-borne and water-borne diseases. Epidemic patterns are also different for air-borne infections, although they are less easily understood. Transmission of respiratory viruses is influenced by factors related to the virus and the human host but also by factors related to the natural and human environment ( table 4 ).

Factors related to transmission patterns and severity of respiratory viruses

However, we are quite unable to explain fully which factor has which influence, how these factors vary among different social groups and how interdependent or isolated they are. We are certainly unable to fully model all these variables mathematically to explain the epidemic pattern across a variety of different contexts. Too many variables and their interrelations are difficult to quantify, and when all these factors change over time while the pathogen continues to spread in diverse societies, the complexity becomes daunting.

Understanding transmission dynamics is a bit less daunting for measles, as several variables are well known and rather constant across individuals and contexts. The natural transmission pattern of measles, before the introduction of vaccines, has been well described. Measles is mostly a childhood disease, but this is not the case in very remote communities, where measles transmission had been interrupted for extended periods (such as the Faroe Islands). 38 39 Measles affected all age groups when reaching new territories, causing dramatic first-wave epidemics, a phenomenon called ‘virgin soil epidemic’. 40 41 The latest stages of the global dissemination of measles have been well documented, including in Australia, the Fiji islands and the Arctic countries, where such virgin soil epidemics occurred in the 19th and the mid-20th centuries. 32 42 Fortunately, measles infection creates robust protective immunity and after a first wave becomes a typical childhood disease, affecting only those without any prior immunity. 43 Human-to-human transmission of measles virus in a community stops when the virus cannot find new susceptible human hosts and the so-called herd immunity is reached. 44 45 But transmission of measles continues elsewhere on the planet from where it can be reintroduced a few years later when the population without protective immunity has grown large enough to allow human-to-human transmission again.

The epidemic patterns of measles are easily understood as measles is highly infectious, creates disease in almost every infected person and leaves lifelong natural immunity. Measles circulation, prior to vaccination, was continuous only in large urban areas with high birth rates. Everywhere else reintroduction occurred typically every 3–5 years but sometimes only after 10 or 15 years in isolated rural communities (such as among nomadic groups in the Sahel), causing epidemics among all those without acquired immunity and having lost maternal antibodies. 46 These diverse patterns of measles epidemics have been fundamentally changed by variable coverage of measles vaccination. They can still help us make sense of the diversity of COVID-19 epidemics being observed in 2020.

Measles illustrates convincingly that the transmission pattern of a respiratory virus is strongly influenced by the demographic composition, density and mixing pattern of the population and the connectedness to big urban centres. Measles transmission is continuous only in some large urban areas. It presents in short epidemics everywhere else with variable periodicity. This transmission pattern may well be a bit similar for COVID-19. But it took thousands of years for measles to reach all human communities while SARS-CoV-2 spread to all countries in only a few months, despite measles being much more transmissible than SARS-CoV-2. Factors such as increased air travel and more dense community structures play bigger roles for SARS-CoV-2 than they did for measles.

Comparison with other pathogenic coronaviruses

SARS-CoV-2 has many close relatives. Six other human coronaviruses (HCoVs) are known to infect humans. SARS-CoV and MERS-CoV (causing SARS and MERS, respectively) are very rare and do not continuously circulate among humans. The other four (HCoV-229E, HCoV-OC43, HCoV-HKU1 and HCoV-NL63) cause the common cold or diarrhoea and continuously circulate and mutate frequently. 47 48 They can cause disease in the same person repeatedly. The typical coronavirus remains localised to the epithelium of the upper respiratory tract, causes mild disease and elicits a poor immune response, hence the high rate of reinfection (in contrast to SARS-CoV and MERS-CoV, which go deeper into the lungs and hence are relatively less contagious). There is no cross-immunity between HCoV-229E and HCoV-OC43, and new strains arise continually by mutation selection. 49

Coping strategies and collective strategies

How a virus spreads and its disease progresses depend not only on the variables described above ( table 4 ) but also on the human reactions deployed when people are confronted with a disease outbreak or the threat of an outbreak. All these variables combined result in what unfolds as ‘the epidemic’ and the diverse ways it affects communities.

What a population experiences during an epidemic is not fully characterised by the numbers of known infections and deaths at the scale of a country. Such numbers hide regional and local differences, especially in large and diverse countries. The epidemic reaches the different geographical areas of a country at different moments and with different intensities. It affects different communities in variable ways, influencing how these communities perceive it and react to it. What constitutes a local COVID-19 epidemic is thus also characterised by the perceptions and the reactions it triggers in the different sections of the society.

Even before the virus reaches a community, the threat of an epidemic already causes fear, stress and anxiety. Consequently, the threat or arrival of the epidemic also triggers responses, early or late, with various degrees of intensity and effectiveness. The response to an epidemic can be divided into individual and household actions (coping strategies), and collectively organised strategies (collective strategies). Coping strategies are the actions people and families take when disease threatens and sickness occurs, including the ways they try to protect themselves from contagion. Collective strategies are voluntary or mandated measures deployed by organised communities and public authorities in response to an epidemic. These include, among others, isolation of the sick or the healthy, implementation of hygiene practices and physical distancing measures. They can also include mobility restrictions such as quarantine and cordon sanitaire . Coping strategies and collective strategies also include treatment of the sick, which critically depends on the availability and effectiveness of diagnostic and therapeutic tools, and performance of the health system. Collective strategies also include research being deployed to further scientific insight and the development of diagnostic and therapeutic tools, potentially including a vaccine.

Implementation of these measures depends not only on resources available but also on the understanding and interpretation of the disease by both the scientific community and the community at large, influenced by the information people receive from scientists, public authorities and the media. This information is interpreted within belief systems and influenced by rumours, increasingly so over social media, including waves of fake news, recently labelled ‘infodemics’. 50

Coping strategies and collective strategies start immediately, while there are still many unknowns and uncertainties. Progressively, as the pandemic unfolds and scientists interpret observations in the laboratory, in the clinic, and in society, more insights are gained and inform the response.

Table 5 lists measures recommended by the WHO for preventing transmission and slowing down the COVID-19 epidemic. 51–53 ‘Lockdown’ first employed in early 2020 in Wuhan, China, is the label often given to the bundle of containment and mitigation measures promoted or imposed by public authorities, although the specific measures may vary greatly between countries. In China, lockdown was very strictly applied and enforced. It clearly had an impact, resulting in total interruption of transmission locally. 54 55

Measures recommended by the WHO for preventing transmission and slowing down the COVID-19 epidemic, 2020

This list or catalogue of measures is quite comprehensive; it includes all measures that at first sight seem to reduce transmission opportunities for a respiratory virus. However, knowledge is lacking about the effectiveness of each measure in different contexts. As a global health agency, the WHO recommends a ‘generic catalogue’ of measures from which all countries can select an appropriate mix at any one time depending on the phase of the epidemic, categorised in four transmission scenarios (no cases, first cases, first clusters, and community transmission). 52 However, under pressure to act and with little time to consider variable options, public authorities often adopted as ‘blueprint’ with limited consideration for the socioeconomic context. 53 56

The initial lockdown in China thus much inspired the collective strategies elsewhere. This has been referred to as ‘global mimicry’, 57 : the response is somehow partly ‘copy/paste’ from measures observed previously (strong path dependency).

Some epidemiologists in Northern Europe (including the UK, 58 Sweden 59 and the Netherlands 60 ) pleaded against strict containment measures and proposed that building up herd immunity against SARS-CoV-2 might be wiser. Towards early April 2020, it became increasingly clear that reaching herd immunity in the short term was illusive. Most countries thus backed off from the herd immunity approach to combating COVID-19 and implemented lockdowns. 61 The intensity of the lockdowns has been variable, ranging from very strict (‘Chinese, Wuhan style’), over intermediary (‘French/Italian/New York City style’ and ‘Hong Kong style’), to relaxed (‘Swedish style’), or piecemeal.

The effectiveness of lockdowns largely depends on at what stage of the epidemic they are started, and how intensively they are applied. This is quite variable across countries, depending on the understanding and motivation of the population and their perceived risk (‘willingness to adhere’), on the trust they have in government advice (‘willingness to comply’), and on the degree of enforcement by public authorities. The feasibility for different population groups to follow these measures depends largely on their socioeconomic and living conditions. It is obviously more difficult for people living in crowded shacks in urban slums to practise physical distancing measures and strict hand hygiene when water is scarce than for people living in wealthier parts of a city.

Collateral effects of the response

Every intervention against the COVID-19 epidemic has a certain degree of effect and comes at a cost with collateral effects. Each collective strategy (1) has intended and unintended consequences (some are more or less desirable); (2) is more or less feasible and/or acceptable in a given context and for certain subgroups in that society; (3) has a cost, not only in financial terms but in many other ways, such as restrictions on movement and behaviour, stress, uncertainty and others. These costs are more or less acceptable, depending on the perception of the risk and many societal factors; (4) can be implemented with more or less intensity; and (5) can be enforced more or less vigorously.

The balance between benefit and cost is crucial in judging whether measures are appropriate, which is very context specific. Furthermore, benefits and costs are also related to the positionality from which they are analysed: benefits for whom and costs borne by whom? More wealthy societies with strong social safety nets can afford increased temporary unemployment. This is much more consequential in poorer countries, where large proportions of the population live precarious lives and where public authorities cannot implement generous mitigation measures at scale.

The adherence to hygiene and distancing measures depends not only on living conditions but also on risk perception and cultural norms. Mass masking has been readily accepted in some Asian countries, where it was already broadly practised even before the COVID-19 epidemic. It remains more controversial in Western societies, some of which even have legal bans on veiling in public places.

Lockdowns are unprecedented and have triggered intensive public debate. Not surprisingly, the impact of lighter lockdowns on the transmission is much less impressive; they decrease transmission but do not stop it. Quite rapidly, the justification for lockdowns shifted from stopping transmission to ‘flattening the curve’. Also, once a lockdown is started, rationalised, explained and enforced, it is difficult to decide when to stop it. Exit scenarios, usually some form of progressive relaxation, are implemented with the knowledge that transmission will be facilitated again. 62

Knowns and unknowns about SARS-CoV-2/COVID-19

What we already know.

The available information on SARS-CoV-2 and the spectrum of COVID-19 disease is summarised in tables 6 and 7 . It is increasingly becoming clear that most transmission happens indoors and that superspreading events trigger intensive dissemination.

Knowns, uncertainties and unknowns about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as of May 2020

Relationship between the dose of the initial infectious inoculum, transmission dynamics and severity of the COVID-19 disease


The dose of the virus in the initial inoculum may be a missing link between the variation observed in the transmission dynamics and the spectrum of the COVID-19 disease. It is plausible that:

Viral dose in inoculum is related to severity of disease.

Severity of disease is related to viral shedding and transmission potential.

This hypothesis plays out potentially at three levels:

At individual level: a person infected with a small dose of viral inoculum will on average develop milder disease than a person infected with a high viral inoculum and vice versa.

At cluster level: a person with asymptomatic infection or mild disease will on average spread lower doses of virus in droplets and aerosols and is less likely to transmit disease; when the person transmits, the newly infected person is more likely to have milder disease than if infected by a severely ill person, who spreads on an average higher doses of virus. This causes clusters and chains of milder cases or of more severe cases.

At community level: in certain contexts, such as dense urban centres in moderate climates during the season when people live mostly indoors, the potential for intensive transmission and explosive outbreaks is high, especially during indoor superspreading events. In other contexts, such as in rural areas or in regions with hot and humid climate where people live mostly outdoors, intensive transmission and explosive outbreaks are less likely.

The virology and immunology of SARS-CoV-2/COVID-19 are being studied intensively. This is critical not only to understand what will potentially happen in future waves but also for the development of a vaccine. Some scientists and companies are very upbeat about the possibility of producing a vaccine in record time. Having a vaccine is one thing, but how effective it is, is quite another. As acquired immunity after a natural infection is probably not very robust ( table 6 ), it will also be challenging to trigger robust immunity with a vaccine, but perhaps it is not impossible. Many questions remain, some of which are summarised in table 8 .

Questions and considerations in case a COVID-19 vaccine is developed

Regarding the severity of COVID-19, initial fears of very high mortality have also lessened. It has progressively become clear that many infections remain asymptomatic, that severe disease is rare in children and young adults, and that mortality is heavily concentrated in the very old and those with comorbidities. Table 7 summarises a fuller overview of the present state of knowledge regarding COVID-19.

With COVID-19 epidemics unfolding rapidly, several of the variables in the transmission of SARS-CoV-2 and the disease spectrum of COVID-19 could be quantified. This allows for mathematical modelling. Several models have been quickly developed, leading to predictions of the speed of transmission and the burden of COVID-19 ( box 2 ). Predictive models developed by the Imperial College 63 ; the Center for Disease Dynamics, Economics & Policy and Johns Hopkins University 28 ; the Institute for Health Metrics and Evaluation 64 ; Harvard University 65 ; and the WHO, 66 including an ‘African model’, 67 are a few that are influencing containment strategies around the world.

Critical unknowns and uncertainties

Although the COVID-19 pandemic triggered unprecedented research efforts globally, with over 30 000 scientific papers published between January and April 2020, there are still critical unknowns and many uncertainties.

Tables 6 and 7 summarise many of the knowns, but their relative importance or weight is not clear. For instance, the virus can spread via droplets, hands, aerosols, fomites and possibly through the environment. However, the relative importance of these in various contexts is much less clear. These factors undoubtedly vary between settings, whether in hospitals, in elderly homes, or at mass events. The weight of the variables also probably differs between the seeding and initial spread in a community and the spread when it suddenly amplifies and intensifies. The importance of each variable probably also depends on climatic conditions, not only outdoors, but also on microclimates indoors, influenced by ventilation and air conditioning and built environments.

We summarise the critical unknowns in table 9 along some elements to consider in addressing the unknowns and thoughts on their importance.

Some critical unknowns in SARS-CoV-2 transmission

Uncertainty remains, leading to controversy and directly influencing the choice of containment measures. Controversy continues regarding when and where lockdown or more selective measures are equally effective with lower societal effects.

New evidence is being discovered rapidly. Some evidence comes from field observations and ecological studies; other evidence results from scientific experiments or observations in the laboratory and the clinic. Sense-making by combining insights from different observations and through the lens of various disciplines can lead to hypotheses that can be tested and verified or refuted. One such hypothesis is that there is a relationship between the dose of virus in the infectious inoculum and the severity of COVID-19 disease. Several intriguing observations in the current pandemic could be (partially) explained by such a relationship. We develop this hypothesis in box 3 , as an example of possible further research, to create new insight which may influence control strategies.

This viral inoculum theory is consistent with many observations from the early stages of the COVID-19 pandemic, but it is not easy to test scientifically.

Potential future scenarios of COVID-19

As COVID-19 is a new disease, we should make a distinction between (1) the current 2019–2020 ‘virgin soil pandemic’ caused by SARS-CoV-2, specifically in how it will further spread around the globe in the first wave, and (2) the potential future transmission in subsequent waves. In some countries, transmission will continue at lower levels. In other countries, such as China, the virus may have been eliminated but can be reintroduced in identical or mutated form.

For the current first wave, using influenza and the common cold as reasonable comparisons, it is possible that the major epidemics, as witnessed in Wuhan, northern Italy, or New York, will typically occur in temperate climates in the winter season. Some predict that such epidemics will last between 8 and 10 weeks (but this is just a plausible and reasonable comparison in analogy with seasonal influenza). It is possible that in hotter climates the transmission may become continuous, year round at lower levels. It is increasingly clear that hot climate does not exclude superspreading events as observed in Guayaquil, Ecuador and in various cities in Brazil. Ventilation, air-conditioning and crowded places may still create favourable environments for intensive transmission. It is also quite possible that the more difficult spread of SARS-CoV-2 in such climates may, in certain communities, be compensated for by human factors such as higher population density, closer human contacts and lesser hygiene (as, for instance, exist in urban slums in mega cities in low income countries). How all this plays out in sub-Saharan Africa, in its slums and remote areas, is still largely unknown. With SARS-CoV-2, transmission scenarios are mainly based on mathematical models despite their serious limitations ( box 2 ).

As the virus continues to circulate, it will progressively be less of a ‘new disease’ during subsequent waves. The immunity caused by the first epidemic will influence how the virus spreads and causes disease. Whether later waves will become progressively milder or worse, as observed in the 1918–19 Spanish influenza, is a matter of intense speculation. Both views seem plausible and the two are not necessarily mutually exclusive. Indeed, immunity should be defined on two levels: individual immunity and herd immunity. Individual immunity will dictate how mild or severe the disease will be in subsequent infections. Herd immunity could be defined in different communities/regions/countries that, in theory, could be fenced off, allowing only limited interaction with other areas, impacting the spread of the virus to more vulnerable populations.

The future is unknown, but we can think of likely futures and critical elements therein.

Some obvious critical elements are:

Will there be an effective vaccine? How soon? How effective? How available at scale? How acceptable?

Will there be an effective treatment? How soon? How effective? How available at scale?

The current first wave is unfolding in the absence of effective biomedical tools (no vaccine, no effective antiviral or immune-modulating medicine, only supportive treatment such as oxygen therapy). This comes close to what can be called a ‘natural evolution’ of the COVID-19 pandemic, mostly modified by the containment measures deployed ( table 5 ) and the effect of supportive treatment.

Progressively, we can learn more about the direct health effects of COVID-19 (morbidity and mortality), about appropriate individual and collective measures, 68 the various degrees of societal disruption and the collateral effects on other essential health services (eg, reluctance to use health services for other health problems, because of ‘corona fear’). Our growing knowledge may enable us to progressively improve our response.

Learning from the variety of ways the COVID-19 epidemic is unfolding across the globe provides important ‘ecological evidence’ and creates insights into its epidemiology and impacts. Until now, the insights gained on COVID-19 have been largely dominated by the COVID-19 epidemics in the Global North. More understanding of lived experiences of people in a variety of contexts, where the epidemic is spreading more slowly and with different impacts, is necessary to get a full global picture and allow learning from this variety. This is an important missing piece of the COVID-19 puzzle.

BMJ Global Health and Emerging Voices for Global Health have launched a call ( ) for such on-the-ground narratives and analyses of the spread of and response to COVID-19, local narratives and analyses that will hopefully help to further enrich our understanding of how and why the COVID-19 pandemic continues to unfold in multiple local epidemics along diverse trajectories around the globe.


We would like to thank Johan Leeuwenburg, Piet Kager, and Luc Bonneux for useful comments on a previous draft, the teams of the Riposte corona, INRB, Kinshasa and the Belgian Embassy in Kinshasa for welcoming and hosting WVD during his unscheduled extended stay in Kinshasa during the lockdown, March–June 2020. We are thankful to Mrs. Ann Byers for editing the manuscript at short notice.

  • Stratton CW ,
  • Tang Yi‐Wei
  • Wang W , et al
  • Coronaviridae Study Group of the International Committee on Taxonomy of Viruses
  • Wang Y , et al
  • Fan Y , et al
  • Hu C , et al
  • Wu P , et al
  • World Health Organization
  • Ko W-C , et al
  • Wang Y-J , et al
  • Ma AHY , et al
  • Epaulard O ,
  • Bénet T , et al
  • Böhmer MM ,
  • Buchholz U ,
  • Corman VM , et al
  • Chan JF-W ,
  • Kok K-H , et al
  • Capron I , et al
  • Kucharski AJ , et al
  • Kupferschmidt K
  • The Guardian
  • Wang M , et al
  • Li Y , et al
  • Chen Y , et al
  • Wilson ME ,
  • The Center For Disease Dynamics Economics & Policy
  • Coltart CEM ,
  • Lindsey B ,
  • Ghinai I , et al
  • Newman LP ,
  • Paget J , et al
  • Leclerc QJ ,
  • Fuller NM ,
  • Knight LE , et al
  • Taubenberger JK
  • Altmann DM ,
  • Rhodes CJ ,
  • Anderson RM
  • Gajdusek DC
  • Aaby P , et al
  • Krugman S ,
  • Friedman H , et al
  • van den Driessche P ,
  • Yuen K-S , et al
  • Shi W , et al
  • Burrell C ,
  • Zarocostas J
  • Ruktanonchai NW ,
  • Zhou L , et al
  • Kucharski AJ ,
  • Russell TW ,
  • Diamond C , et al
  • Bedford J ,
  • Giesecke J , et al
  • Pancevski B
  • National Geographic
  • Gutiérrez P ,
  • Ferguson NM ,
  • Nedjati-Gilani G , et al
  • Institute for Health Metrics and Evaluation
  • Kissler SM ,
  • Tedijanto C ,
  • Goldstein E , et al
  • Cabore JW ,
  • Karamagi HC ,
  • Kipruto H , et al
  • Van Damme W ,
  • Van Lerberghe W
  • Fischetti M ,
  • Krzywinski M
  • Deslandes A ,
  • Tandjaoui-Lambotte Y , et al
  • Kumar H , et al
  • Centers for Disease Control and Prevention
  • Taubenberger JK ,
  • Trifonov V ,
  • Khiabanian H ,
  • Dawood FS ,
  • Iuliano AD ,
  • Reed C , et al
  • Writing Committee of the WHO Consultation on Clinical Aspects of Pandemic (H1N1) 2009 Influenza ,
  • Bautista E ,
  • Chotpitayasunondh T , et al
  • Pougué Biyong C , et al
  • Dorigatti I , et al
  • Epidemiology Working Group for NCIP Epidemic Response - Chinese Center for Disease Control and Prevention
  • McGoogan JM
  • Mizumoto K ,
  • Zarebski A , et al
  • National Institute of Infectious Diseases (NIID)
  • Nishiura H ,
  • Kobayashi T ,
  • Miyama T , et al
  • Heneghan C ,
  • Brassey J ,
  • Jefferson T , Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford
  • Redfield RR
  • Hellewell J ,
  • Jarvis CI , et al
  • Streeck H ,
  • Hartmann G ,
  • Exner M , et al
  • Docherty AB ,
  • Harrison EM ,
  • Green CA , et al
  • Banerjee A ,
  • Harris S , et al
  • Khafaie MA ,
  • Cyranoski D
  • Wexler AS ,
  • Cappa CD , et al
  • Chiew CJ , et al
  • van Doremalen N ,
  • Bushmaker T ,
  • Morris DH , et al
  • Zhu B , et al
  • Heijnen L ,
  • Elsinga G , et al
  • Edson J , et al
  • de Roda Husman AM
  • Cheng H-Y ,
  • Liu D-P , et al
  • Ferretti L ,
  • Kendall M , et al
  • Araujo MB ,
  • Sajadi MM ,
  • Habibzadeh P ,
  • Vintzileos A , et al
  • Majumder MS ,
  • Liu D , et al
  • Martelletti L ,
  • Martelletti P
  • Yang S , et al
  • Gao H , et al
  • Liang Y , et al
  • Ye G , et al
  • Bobrovitz N ,
  • Yan T , et al
  • Müller MA ,
  • Li W , et al
  • Vitale JN , et al
  • Wheeler S , et al
  • Britton T ,
  • Trapman P ,
  • Gomes MGM ,
  • Corder RM ,
  • King JG , et al
  • Wei WI , et al
  • Bar-On YM ,
  • Flamholz A ,
  • Phillips R , et al
  • Chen Z , et al
  • Achenbach J
  • Larremore DB ,
  • Ludvigsson JF
  • Mytton OT ,
  • Bonell C , et al
  • Wei T , et al
  • Sothmann P , et al

Twitter @Ingelbeen, @jdossou80, @seyeabimbola, @jarthurk, @@vdbrouwere, @SamehAlawlaqi, @prashanthns

Contributors WVD, RD, EW and YA conceived and designed the study. RD, GV, YA and WVD searched the literature and screened for new emerging evidence. WVD, RD and YA drafted successive versions of the manuscript and coordinated inputs from all coauthors. YA, SA, KV, BI, RvdP and HK contributed to writing the manuscript. AD, J-PD, PI, SVdB, DN, GB, IVE, MAAA, JAK, VDB, KB, PA, AK, SA-A, NSP, J-JM-T, PM and SA-M reviewed successive versions of the manuscript and oriented it, with a field-based and local gaze from Guinea, Benin, Cambodia, Belgium, India, the UK, Mali, Canada, Burkina Faso, Germany, the USA, Guinea-Bissau, the Democratic Republic of Congo, Yemen and Australia. All authors commented on subsequent versions of the manuscript and approved the final version. WVD attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement No additional data are available.

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I Thought We’d Learned Nothing From the Pandemic. I Wasn’t Seeing the Full Picture

introduction essay about covid 19 pandemic

M y first home had a back door that opened to a concrete patio with a giant crack down the middle. When my sister and I played, I made sure to stay on the same side of the divide as her, just in case. The 1988 film The Land Before Time was one of the first movies I ever saw, and the image of the earth splintering into pieces planted its roots in my brain. I believed that, even in my own backyard, I could easily become the tiny Triceratops separated from her family, on the other side of the chasm, as everything crumbled into chaos.

Some 30 years later, I marvel at the eerie, unexpected ways that cartoonish nightmare came to life – not just for me and my family, but for all of us. The landscape was already covered in fissures well before COVID-19 made its way across the planet, but the pandemic applied pressure, and the cracks broke wide open, separating us from each other physically and ideologically. Under the weight of the crisis, we scattered and landed on such different patches of earth we could barely see each other’s faces, even when we squinted. We disagreed viciously with each other, about how to respond, but also about what was true.

Recently, someone asked me if we’ve learned anything from the pandemic, and my first thought was a flat no. Nothing. There was a time when I thought it would be the very thing to draw us together and catapult us – as a capital “S” Society – into a kinder future. It’s surreal to remember those early days when people rallied together, sewing masks for health care workers during critical shortages and gathering on balconies in cities from Dallas to New York City to clap and sing songs like “Yellow Submarine.” It felt like a giant lightning bolt shot across the sky, and for one breath, we all saw something that had been hidden in the dark – the inherent vulnerability in being human or maybe our inescapable connectedness .

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Read More: The Family Time the Pandemic Stole

But it turns out, it was just a flash. The goodwill vanished as quickly as it appeared. A couple of years later, people feel lied to, abandoned, and all on their own. I’ve felt my own curiosity shrinking, my willingness to reach out waning , my ability to keep my hands open dwindling. I look out across the landscape and see selfishness and rage, burnt earth and so many dead bodies. Game over. We lost. And if we’ve already lost, why try?

Still, the question kept nagging me. I wondered, am I seeing the full picture? What happens when we focus not on the collective society but at one face, one story at a time? I’m not asking for a bow to minimize the suffering – a pretty flourish to put on top and make the whole thing “worth it.” Yuck. That’s not what we need. But I wondered about deep, quiet growth. The kind we feel in our bodies, relationships, homes, places of work, neighborhoods.

Like a walkie-talkie message sent to my allies on the ground, I posted a call on my Instagram. What do you see? What do you hear? What feels possible? Is there life out here? Sprouting up among the rubble? I heard human voices calling back – reports of life, personal and specific. I heard one story at a time – stories of grief and distrust, fury and disappointment. Also gratitude. Discovery. Determination.

Among the most prevalent were the stories of self-revelation. Almost as if machines were given the chance to live as humans, people described blossoming into fuller selves. They listened to their bodies’ cues, recognized their desires and comforts, tuned into their gut instincts, and honored the intuition they hadn’t realized belonged to them. Alex, a writer and fellow disabled parent, found the freedom to explore a fuller version of herself in the privacy the pandemic provided. “The way I dress, the way I love, and the way I carry myself have both shrunk and expanded,” she shared. “I don’t love myself very well with an audience.” Without the daily ritual of trying to pass as “normal” in public, Tamar, a queer mom in the Netherlands, realized she’s autistic. “I think the pandemic helped me to recognize the mask,” she wrote. “Not that unmasking is easy now. But at least I know it’s there.” In a time of widespread suffering that none of us could solve on our own, many tended to our internal wounds and misalignments, large and small, and found clarity.

Read More: A Tool for Staying Grounded in This Era of Constant Uncertainty

I wonder if this flourishing of self-awareness is at least partially responsible for the life alterations people pursued. The pandemic broke open our personal notions of work and pushed us to reevaluate things like time and money. Lucy, a disabled writer in the U.K., made the hard decision to leave her job as a journalist covering Westminster to write freelance about her beloved disability community. “This work feels important in a way nothing else has ever felt,” she wrote. “I don’t think I’d have realized this was what I should be doing without the pandemic.” And she wasn’t alone – many people changed jobs , moved, learned new skills and hobbies, became politically engaged.

Perhaps more than any other shifts, people described a significant reassessment of their relationships. They set boundaries, said no, had challenging conversations. They also reconnected, fell in love, and learned to trust. Jeanne, a quilter in Indiana, got to know relatives she wouldn’t have connected with if lockdowns hadn’t prompted weekly family Zooms. “We are all over the map as regards to our belief systems,” she emphasized, “but it is possible to love people you don’t see eye to eye with on every issue.” Anna, an anti-violence advocate in Maine, learned she could trust her new marriage: “Life was not a honeymoon. But we still chose to turn to each other with kindness and curiosity.” So many bonds forged and broken, strengthened and strained.

Instead of relying on default relationships or institutional structures, widespread recalibrations allowed for going off script and fortifying smaller communities. Mara from Idyllwild, Calif., described the tangible plan for care enacted in her town. “We started a mutual-aid group at the beginning of the pandemic,” she wrote, “and it grew so quickly before we knew it we were feeding 400 of the 4000 residents.” She didn’t pretend the conditions were ideal. In fact, she expressed immense frustration with our collective response to the pandemic. Even so, the local group rallied and continues to offer assistance to their community with help from donations and volunteers (many of whom were originally on the receiving end of support). “I’ve learned that people thrive when they feel their connection to others,” she wrote. Clare, a teacher from the U.K., voiced similar conviction as she described a giant scarf she’s woven out of ribbons, each representing a single person. The scarf is “a collection of stories, moments and wisdom we are sharing with each other,” she wrote. It now stretches well over 1,000 feet.

A few hours into reading the comments, I lay back on my bed, phone held against my chest. The room was quiet, but my internal world was lighting up with firefly flickers. What felt different? Surely part of it was receiving personal accounts of deep-rooted growth. And also, there was something to the mere act of asking and listening. Maybe it connected me to humans before battle cries. Maybe it was the chance to be in conversation with others who were also trying to understand – what is happening to us? Underneath it all, an undeniable thread remained; I saw people peering into the mess and narrating their findings onto the shared frequency. Every comment was like a flare into the sky. I’m here! And if the sky is full of flares, we aren’t alone.

I recognized my own pandemic discoveries – some minor, others massive. Like washing off thick eyeliner and mascara every night is more effort than it’s worth; I can transform the mundane into the magical with a bedsheet, a movie projector, and twinkle lights; my paralyzed body can mother an infant in ways I’d never seen modeled for me. I remembered disappointing, bewildering conversations within my own family of origin and our imperfect attempts to remain close while also seeing things so differently. I realized that every time I get the weekly invite to my virtual “Find the Mumsies” call, with a tiny group of moms living hundreds of miles apart, I’m being welcomed into a pocket of unexpected community. Even though we’ve never been in one room all together, I’ve felt an uncommon kind of solace in their now-familiar faces.

Hope is a slippery thing. I desperately want to hold onto it, but everywhere I look there are real, weighty reasons to despair. The pandemic marks a stretch on the timeline that tangles with a teetering democracy, a deteriorating planet , the loss of human rights that once felt unshakable . When the world is falling apart Land Before Time style, it can feel trite, sniffing out the beauty – useless, firing off flares to anyone looking for signs of life. But, while I’m under no delusions that if we just keep trudging forward we’ll find our own oasis of waterfalls and grassy meadows glistening in the sunshine beneath a heavenly chorus, I wonder if trivializing small acts of beauty, connection, and hope actually cuts us off from resources essential to our survival. The group of abandoned dinosaurs were keeping each other alive and making each other laugh well before they made it to their fantasy ending.

Read More: How Ice Cream Became My Own Personal Act of Resistance

After the monarch butterfly went on the endangered-species list, my friend and fellow writer Hannah Soyer sent me wildflower seeds to plant in my yard. A simple act of big hope – that I will actually plant them, that they will grow, that a monarch butterfly will receive nourishment from whatever blossoms are able to push their way through the dirt. There are so many ways that could fail. But maybe the outcome wasn’t exactly the point. Maybe hope is the dogged insistence – the stubborn defiance – to continue cultivating moments of beauty regardless. There is value in the planting apart from the harvest.

I can’t point out a single collective lesson from the pandemic. It’s hard to see any great “we.” Still, I see the faces in my moms’ group, making pancakes for their kids and popping on between strings of meetings while we try to figure out how to raise these small people in this chaotic world. I think of my friends on Instagram tending to the selves they discovered when no one was watching and the scarf of ribbons stretching the length of more than three football fields. I remember my family of three, holding hands on the way up the ramp to the library. These bits of growth and rings of support might not be loud or right on the surface, but that’s not the same thing as nothing. If we only cared about the bottom-line defeats or sweeping successes of the big picture, we’d never plant flowers at all.

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Essay on COVID-19 Pandemic

As a result of the COVID-19 (Coronavirus) outbreak, daily life has been negatively affected, impacting the worldwide economy. Thousands of individuals have been sickened or died as a result of the outbreak of this disease. When you have the flu or a viral infection, the most common symptoms include fever, cold, coughing up bone fragments, and difficulty breathing, which may progress to pneumonia. It’s important to take major steps like keeping a strict cleaning routine, keeping social distance, and wearing masks, among other things. This virus’s geographic spread is accelerating (Daniel Pg 93). Governments restricted public meetings during the start of the pandemic to prevent the disease from spreading and breaking the exponential distribution curve. In order to avoid the damage caused by this extremely contagious disease, several countries quarantined their citizens. However, this scenario had drastically altered with the discovery of the vaccinations. The research aims to investigate the effect of the Covid-19 epidemic and its impact on the population’s well-being.

There is growing interest in the relationship between social determinants of health and health outcomes. Still, many health care providers and academics have been hesitant to recognize racism as a contributing factor to racial health disparities. Only a few research have examined the health effects of institutional racism, with the majority focusing on interpersonal racial and ethnic prejudice Ciotti et al., Pg 370. The latter comprises historically and culturally connected institutions that are interconnected. Prejudice is being practiced in a variety of contexts as a result of the COVID-19 outbreak. In some ways, the outbreak has exposed pre-existing bias and inequity.

Thousands of businesses are in danger of failure. Around 2.3 billion of the world’s 3.3 billion employees are out of work. These workers are especially susceptible since they lack access to social security and adequate health care, and they’ve also given up ownership of productive assets, which makes them highly vulnerable. Many individuals lose their employment as a result of lockdowns, leaving them unable to support their families. People strapped for cash are often forced to reduce their caloric intake while also eating less nutritiously (Fraser et al, Pg 3). The epidemic has had an impact on the whole food chain, revealing vulnerabilities that were previously hidden. Border closures, trade restrictions, and confinement measures have limited farmer access to markets, while agricultural workers have not gathered crops. As a result, the local and global food supply chain has been disrupted, and people now have less access to healthy foods. As a consequence of the epidemic, many individuals have lost their employment, and millions more are now in danger. When breadwinners lose their jobs, become sick, or die, the food and nutrition of millions of people are endangered. Particularly severely hit are the world’s poorest small farmers and indigenous peoples.

Infectious illness outbreaks and epidemics have become worldwide threats due to globalization, urbanization, and environmental change. In developed countries like Europe and North America, surveillance and health systems monitor and manage the spread of infectious illnesses in real-time. Both low- and high-income countries need to improve their public health capacities (Omer et al., Pg 1767). These improvements should be financed using a mix of national and foreign donor money. In order to speed up research and reaction for new illnesses with pandemic potential, a global collaborative effort including governments and commercial companies has been proposed. When working on a vaccine-like COVID-19, cooperation is critical.

The epidemic has had an impact on the whole food chain, revealing vulnerabilities that were previously hidden. Border closures, trade restrictions, and confinement measures have limited farmer access to markets, while agricultural workers have been unable to gather crops. As a result, the local and global food supply chain has been disrupted, and people now have less access to healthy foods (Daniel et al.,Pg 95) . As a consequence of the epidemic, many individuals have lost their employment, and millions more are now in danger. When breadwinners lose their jobs, the food and nutrition of millions of people are endangered. Particularly severely hit are the world’s poorest small farmers and indigenous peoples.

While helping to feed the world’s population, millions of paid and unpaid agricultural laborers suffer from high levels of poverty, hunger, and bad health, as well as a lack of safety and labor safeguards, as well as other kinds of abuse at work. Poor people, who have no recourse to social assistance, must work longer and harder, sometimes in hazardous occupations, endangering their families in the process (Daniel Pg 96). When faced with a lack of income, people may turn to hazardous financial activities, including asset liquidation, predatory lending, or child labor, to make ends meet. Because of the dangers they encounter while traveling, working, and living abroad; migrant agricultural laborers are especially vulnerable. They also have a difficult time taking advantage of government assistance programs.

The pandemic also has a significant impact on education. Although many educational institutions across the globe have already made the switch to online learning, the extent to which technology is utilized to improve the quality of distance or online learning varies. This level is dependent on several variables, including the different parties engaged in the execution of this learning format and the incorporation of technology into educational institutions before the time of school closure caused by the COVID-19 pandemic. For many years, researchers from all around the globe have worked to determine what variables contribute to effective technology integration in the classroom Ciotti et al., Pg 371. The amount of technology usage and the quality of learning when moving from a classroom to a distant or online format are presumed to be influenced by the same set of variables. Findings from previous research, which sought to determine what affects educational systems ability to integrate technology into teaching, suggest understanding how teachers, students, and technology interact positively in order to achieve positive results in the integration of teaching technology (Honey et al., 2000). Teachers’ views on teaching may affect the chances of successfully incorporating technology into the classroom and making it a part of the learning process.

In conclusion, indeed, Covid 19 pandemic have affected the well being of the people in a significant manner. The economy operation across the globe have been destabilized as most of the people have been rendered jobless while the job operation has been stopped. As most of the people have been rendered jobless the living conditions of the people have also been significantly affected. Besides, the education sector has also been affected as most of the learning institutions prefer the use of online learning which is not effective as compared to the traditional method. With the invention of the vaccines, most of the developed countries have been noted to stabilize slowly, while the developing countries have not been able to vaccinate most of its citizens. However, despite the challenge caused by the pandemic, organizations have been able to adapt the new mode of online trading to be promoted.

Ciotti, Marco, et al. “The COVID-19 pandemic.”  Critical reviews in clinical laboratory sciences  57.6 (2020): 365-388.

Daniel, John. “Education and the COVID-19 pandemic.”  Prospects  49.1 (2020): 91-96.

Fraser, Nicholas, et al. “Preprinting the COVID-19 pandemic.”  BioRxiv  (2021): 2020-05.

Omer, Saad B., Preeti Malani, and Carlos Del Rio. “The COVID-19 pandemic in the US: a clinical update.”  Jama  323.18 (2020): 1767-1768.

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  • Published: 08 May 2024

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

  • Baining Zhao   ORCID: 1 , 2   na1 ,
  • Xuzhe Wang 1   na1 ,
  • Tianyu Zhang 1 ,
  • Rongye Shi   ORCID: 3 ,
  • Fengli Xu 4 ,
  • Fanhang Man 1 ,
  • Erbing Chen 5 ,
  • Yang Li   ORCID: 1 ,
  • Yong Li   ORCID: 4 ,
  • Tao Sun   ORCID: 2 &
  • Xinlei Chen 1 , 2 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  591 ( 2024 ) Cite this article

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  • Health humanities
  • Information systems and information technology
  • Science, technology and society
  • Social anthropology

Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

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As one of the most infectious pandemics, COVID-19 has resulted in a devastating toll of over 7 million lives lost and an estimated $8 trillion in economic damage (Clark et al., 2020 ; Taskinsoy, 2020 ; Weiss et al., 2020 ; Zhang et al., 2022 ). Research on the COVID-19 pandemic and its impact holds the potential to offer valuable insights for addressing unforeseen large-scale, highly infectious diseases in the future (Chen et al., 2022 ; Menkir et al., 2021 ; Sibley et al., 2020 ).

Due to rapid urbanization, more than 55% of the global population resides in urban areas which serve as a hotbed for infectious diseases due to dense population (United Nations, 2018 ). It is imminent to study the response and recuperation of urban mobility during the influence of COVID-19 (Arellana et al., 2020 ; Atkinson-Clement and Pigalle, 2021 ; Gkiotsalitis and Cats, 2021 ; Levin et al., 2021 ) to facilitate understanding of the spread of the pandemics (Chang et al., 2021 ; Wei et al., 2021 ). Simultaneously, the change in urban mobility profoundly influences economic dynamics and mental health outcomes, among other societal aspects (Wang et al., 2021 , 2022 ). Existing research has primarily investigated how governments proactively implement mandatory or advisory stay-at-home orders to change people’s mobility behavior and contain virus transmission (Martínez and Short, 2021 ; Shen et al., 2020 ; Tirachini and Cats, 2020 ; Wang et al., 2022 ; Zhang, 2021 ). However, there is limited knowledge regarding the spontaneous change in citizens’ mobility behavior during the COVID-19 pandemic. The spontaneous change consists of individuals’ voluntary adaption of their travel behavior concerning the infection rate of disease and perceived threats, without the restriction of government policies (Han et al., 2021 ). In the case of COVID-19, the self-driven behavior involves engaging in home-based care while infected, limiting travel to essential trips only, avoiding crowded places, shifting on-site work to remote work to reduce potential exposure to the virus, etc. (Balmford et al., 2020 ; Tisdell, 2020 ). A better understanding of these spontaneous mobility changes can provide valuable insights into the necessary mobility behavior of the citizens and in turn facilitate the design of urban mobility-related policies during pandemics.

In this paper, we investigate spontaneous mobility changes without stay-at-home orders throughout a highly infectious pandemic, from its emergence through large-scale proliferation to eventual stabilization. The mobility changes are manifested as fluctuations in the number of individuals traveling, which are microscopically composed of varying purpose-specific mobility behavior. We aim to answer the following research questions:

Q1: At the temporal and spatial scale, how does urban mobility evolve in response to a pandemic’s lifecycle?

Q2: Given distinct regions corresponding to varied functional zones and data pertaining to various modes of transportation within a city, what is the underlying mobility behavior, and how do they react to the pandemic?

Q3: How to establish a dynamic model to deduce the spatio-temporal mobility changes of the entire city based on the behavior of different travel purposes and modes?

To answer these questions, we utilize origin-destination (OD) mobility data involving 148 million occurrences before and after the cancellation of the "Zero-COVID" policy in Shenzhen subway, bus, and taxi systems, China, as shown in Fig. 1 a, b. The OD data logs the time and quantity of individuals traveling from areas proximal to the origin to regions near the destination in the urban public transport system. Such data encapsulates the spatio-temporal mobility of urban populations, focusing solely on the volume of individuals moving between two regions, while avoiding the disclosure of individual-specific information. This ensures a high degree of privacy preservation.

figure 1

a Trips in the Shenzhen bus, subway, and taxi systems from December 8, 2022, to January 6, 2023. China adhered to a rigorous "Zero-COVID" policy and implemented strong non-pharmaceutical measures from the beginning of the COVID-19 outbreak until early Dec. 2022. As a result, over 99% of the population in China had never been infected by any variant of SARS-CoV-2. Even though China heavily distributed the COVID-19 vaccine, the vaccine merely prevents people from serious symptoms without comprehensive immunity due to the fast variability of COVID-19. On December 7, 2022, the "Zero-COVID" policy, along with the travel restrictions, was rescinded. The travel volume immediately returned to a level approximating normalcy. Thereafter, the spread of SARS-CoV-2 precipitated a substantial decrease in urban mobility. A progressive recovery would then succeed. To highlight the impact of the pandemic more effectively, we conducted normalization separately for weekdays, Saturdays, and Sundays, ultimately showcasing the trip rate. b The lines bridging the origin and destination maps symbolize the human movement between two corresponding regions. Due to the dense nature of OD data, we depict flows exceeding a daily average of 200 for clarity. Three time periods are highlighted: before the COVID-19 pandemic outbreak, during the peak impact, and during the gradual stabilization period. These periods correspond to three different levels of mobility.

The period before and after the cancellation of the "Zero-COVID" policy provides a unique opportunity to observe the spontaneous evolution of urban mobility without mobility-restricting measures, facing the pandemic spreading on a large scale in a short period. The "Zero-COVID" policy focuses on promptly identifying and containing localized outbreaks using advanced technologies such as big data analysis and nucleic acid screening (China CDC, 2022 ). Through strict quarantine measures, identification of infection sources, and high-risk group identification, China aims to swiftly end outbreaks with minimal societal and economic impacts. China’s implementation of the "Zero-COVID" policy has been highly successful. Despite its massive population of 1.4 billion people, the country has reported relatively low numbers of COVID-19 cases and deaths before the cancellation of the "Zero-COVID" policy. Impressively, China has recorded 1,655,477 cases (less than 0.2% of the population) and 13,524 deaths (less than 0.1%% of the population) (Burki, 2022 ). In late 2022, the Omicron variant of SARS-CoV-2 became predominant worldwide and it was known for its high transmissibility (basic reproduction number ≈ 9.5) and penetration rate. Yet, compared to previous variants, it also demonstrated a relatively lower infection fatality rate (<1%) (Liu et al., 2022 ). Under such a circumstance, on Dec. 7, 2022, China lifted the "Zero-COVID" policy along with the travel restrictions. However, the Omicron variant showed a high rate of immune escape from vaccines which merely prevented people from serious symptoms without delivering comprehensive immunity. Therefore, the lift of the policy led to a rapid and massive spread of the Omicron variant of SARS-CoV-2. It is estimated that within a month, the proportion of infected individuals in cities sharply escalated from nearly zero to surpassing 70% (Leung et al., 2023 ). Besides, Shenzhen is a megacity with a permanent population exceeding 17 million individuals and a population density reaching 8,800 individuals per square kilometer (Shenzhen Government, 2023 ). The research on mobility behavior during the pandemic in Shenzhen is representative and informative.

An analytic framework and a dynamic model are proposed with the following key designs. In terms of spatial distribution, mobility in central business districts (CBDs) and their adjacent areas is significantly impacted. The mobility changes can be quantitatively represented by a time series and evaluated by features about the decline and recovery of trips. To explore the spatial disparities, four mobility patterns are found among the mobility changes corresponding to various OD pairs through the K -means++ clustering algorithm (answering Q1 ). We overlay an urban land use (ULU) map on origins and destinations, thus elucidating the intent behind mobility behavior, such as commuting, recreation, schooling, and more. The differences in mobility changes among subways, buses, and taxis are also discussed (answering Q2 ). Inspired by this, we try to devise a dynamic model of changes in passenger flow, predicated on the impact of COVID-19 on different mobility behavior. We model distinct mobility behavior by combining physical infection and the willingness influence, which subsequently deduces the effects of ULU embodying travel intentions. As a result, we can infer the mobility within the geographic regions housing these lands with different uses (answering Q3 ). Our model allows us to not only fit observed changes in trips but also to conduct detailed analysis at a granularity of less than 500m. We believe the proposed insight and model could be leveraged to provide public health officials with a holistic recommendation as they decide on mobility-related policies under similar pandemic conditions.

Spatial distributions of urban mobility level

The spatial distribution of the decline and recovery in mobility levels can be approximately observed based on Fig. 2 . This observation is made at the granularity of a 1km × 1km grid level. For subways, buses, and taxis in a grid, the initial travel volumes for departures and arrivals are aggregated and compared with the passenger flow from the pre-pandemic period to assess the level of mobility. It is observed that the mobility in CBDs was more significantly impacted by COVID-19 compared to areas farther from the CBDs.

figure 2

a The departure and arrival passenger flows for each region were aggregated and then divided by their pre-pandemic values to ascertain the mobility levels in each area. Notably, Day 15 was observed to have one of the most significant reductions in passenger flow. Day 7 marked a period of decline, whereas Days 21 and 28 were identified as recovery phases. Furthermore, a disparity in the impact on mobility levels was observed between the CBD and its adjacent areas compared to regions located further from the CBD. b The spontaneous mobility changes consist of the reduction, trough, and recovery stages. These three stages are quantitatively measured and a set of corresponding features are formed, as listed in Table 1 .

CBDs are typically characterized by high density, both in terms of population and infrastructure. This density leads to more congested public spaces and transportation systems. During the pandemic, such conditions increased the risk of virus transmission, leading to more stringent movement restrictions and a greater reluctance among the public to travel in these areas. Furthermore, this may be related to the attributes of land use, which will be discussed in the section “Population behavior behind mobility patterns”.

Identifying mobility patterns

To investigate mobility changes at a finer granularity (street level), bus and subway data were processed based on station information and passenger flow, while taxi data were analyzed by urban areas (see Methods for a detailed description of the data). The OD data derived from the public bus, subway, and taxi systems in Shenzhen include both spatial and temporal information on urban mobility. In the bus system, bus routes establish connectivity between two distinct urban regions corresponding to the origin station and destination station, and passenger flow is influenced by the gradual spread of the epidemic, resulting in temporal variations. This principle is similarly applicable to subway and taxi systems.

Following the large-scale outbreak of the COVID-19 pandemic (post-December 7, 2022), the mobility for almost all OD pairs swiftly plummeted from pre-pandemic levels to a markedly low volume, eventually showing signs of gradual recovery, as shown in Fig. 2 . This change in trips can be delineated via a temporal variation curve for each OD pair, as shown in Fig. 3 a. We normalize each time series of OD passenger flow using historical data (i.e., typical passenger flow levels before November 25, 2022) and subject the data to preprocessing.

figure 3

a This time series chart shows the changes in OD trip rate during the period from December 8, 2022, to January 6, 2023. The clustering algorithm identifies four distinct groups with different mobility patterns of decline and recovery. The middle line in each plot represents the average trip rate for each cluster. b Through the spatial attractiveness of ULU, we can infer prominent mobility behavior under each cluster. The changes in mobility are related to mobility behavior between different origins and destinations. c indicates the total impact on the overall passenger volumes of subway, bus, and taxi services.

To quantify the initial decline and subsequent recovery in mobility during the COVID-19 pandemic (Fig. 2 ), we establish a set of features to assess how trips evolve, as listed in Table 1 . The concept of resilience is about how the system responds to disturbances (Qian et al., 2022 ; Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ): does it withstand the shock and remain unchanged, does it adapt and transform into a new state, or does it collapse (Forzieri et al., 2022 ; Kumpfer, 2002 ; Zhao et al., 2021 ). Inspired by this, we concentrate on the resilience of mobility to the pandemic—a characteristic of mobility decreasing and rapidly recovering from large-scale infectious diseases. Features are devised to quantitatively measure the three stages—reduction, trough, and recovery— capturing the mobility changes throughout the COVID-19 pandemic.

Once the features of changes in mobility for OD pairs are extracted, the mobility trends for different modes of transportation can be uniformly analyzed. Based on the aforementioned properties, we employ unsupervised machine learning technology, i.e. K-means++ clustering algorithm (Arthur and Vassilvitskii, 2006 ), to discern the similarities and disparities in passenger flow among various OD pairs (see Methods for detail). As depicted in Fig. 3 a, the K -means++ algorithm categorizes the OD trip trends into four distinct patterns.

The OD mobility in Cluster 1 exhibits the most notable decline, with a total magnitude of impact of -13.47(%  ⋅  days), as presented in Table 1 . In the initial phase of the COVID-19 pandemic, a precipitous decrease in passenger flow was observed, with the average value plummeting to less than 50% by the 12th day. While the subsequent descent in passenger flow exhibited a slower pace, a persistently low level or downward trend was maintained. Certain OD passenger flows reached a standstill till the conclusion of the statistical period. This suggests a high degree of travel flexibility within this cluster, or significant aversion to infection, implying that this demographic endeavors to avoid travel throughout the pandemic. Furthermore, the resurgence of travel intent tends to be protracted, with a near-zero recovery rate in the short term.

Cluster 2 exhibits a pronounced U-shape curve with a comparable decline rate, as shown in Fig. 3 a. The nadir of OD passenger flow appeared around the 15th day, hovering at approximately 50%. Thereafter, the travel volume exhibited a gradual resurgence, culminating in an average travel volume recovery of 82.2%. The graphical representation illustrated a significantly slower pace of travel recovery in the pandemic’s later stages, in contrast to the rapid decline observed during the initial phases. The mobility behavior characterizing Cluster 2 aligns with the epidemiological patterns of infection and recovery. During the initial phase of the COVID-19 pandemic, citizens refrained from traveling, either due to active infection or as a preventive measure against contagion. However, as the infection peaked and those infected began to recover, travel volume followed a trajectory of gradual recovery. This pattern is representative of the majority of mobility behavior.

The mobility change curve for Cluster 3 also exhibits a U-shaped pattern; however, its recovery speed significantly outpaces that of Cluster 2. Following the onset of the pandemic, there was a rapid decrease in passenger flow, averaging approximately a 48.8% reduction. Contrasting with the sluggish recovery observed in Cluster 2, the resurgence pace in Cluster 3 mirrors the speed of passenger flow decline experienced during the reduction stage. Consequently, by the conclusion of the observation period, the OD mobility level essentially reverted to its pre-epidemic benchmark. In the end, the mobility behavior corresponding to this cluster rapidly surmounted the impacts of the COVID-19 pandemic, facilitating, and even accelerating, the return to pre-epidemic levels.

The mobility patterns of OD bus stations in Cluster 4 are minimally impacted by the COVID-19 pandemic, with the total impact merely a third of that experienced by Cluster 1. The corresponding curve demonstrated minor fluctuations, with a peak declining amplitude of 38.2%. Across the tripartite stages of mobility alteration (reduction, trough, recovery), the pandemic’s impact on passenger volume was relatively insubstantial. This suggests that within this cluster, the corresponding origins and destinations exhibit robust travel demand, complemented by relatively inflexible mobility behavior.

Population behavior behind mobility patterns

The two crucial facets of population behavior are where to go and how to get there. Consequently, we delve into discussions on insights regarding travel purposes and modes during the processes of mobility decline and recovery.

Travel purpose

Mobility between two areas is aggregated from population behavior with various travel purposes. Lands with specific uses around each origin and destination have the potential to reveal the land-use characteristics of the places where passengers most visit (Chang et al., 2021 ; Sun et al., 2007 ). Therefore, the integration of OD data and the ULU map has the potential to reveal the travel purposes behind OD trips. For example, if a passenger departs from a station (or pick-up point of a taxi trip) surrounded by lands with the category of residential and alights at lands with the category of the company at night o’clock, it can be inferred that the passenger is most likely commuting to work. Therefore, the ULU categories clearly indicate people’s travel purposes.

More generally, we establish a ULU feature vector to represent the probability of departure or arrival at each nearby urban land category (Xing et al., 2020 ). We have collected nine common types of ULU information from Gaode Maps and Baidu Maps, including the residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery. The OD pairs are mapped to OD land pairs according to the corresponding ULU feature vectors (see Methods for details). We further delineate the primary ULU visited and the key travel objectives within each cluster, as presented in Fig. 3 b.

Travel behavior to schools is mainly classified within Cluster 1 and Cluster 2. Following COVID-19’s onset, passenger flow experienced a precipitous decline, with no short-term recovery trend in sight. Firstly, the health of minors is often perceived as more vulnerable, prompting parents to exercise added caution in safeguarding their children from the COVID-19 virus (She et al., 2020 ). Parents likely prefer to refrain from sending their children to school, owing to concerns regarding viral transmission. Subsequently, as infection rates escalated, schools transitioned to online or remote learning modalities (Betthäuser et al., 2023 ). This shift obviated the need for students, teachers, and staff to commute daily, thereby perpetuating the decrease in passenger flow. The passenger volume at school-associated stations was anticipated to remain low until the pandemic stabilizes. As for colleges, numerous students were sent home ahead of the lifting of the "Zero-COVID" policy, resulting in a significant decline in passenger flow.

The majority of trips from residential, company lands to park/scenery, cultural/sports lands are concentrated within clusters with a more significant impact on mobility. Analyzing travel sentiments reveals that these journeys are deemed non-essential, and the inclination to embark on them significantly diminishes following the COVID-19 pandemic outbreak (Han et al., 2021 ). Given the increased risk of COVID-19 infection, older populations exhibit heightened caution when using buses and visiting crowded locations, resulting in highly affected resilience. After contracting COVID-19 (Wang et al., 2022 ), individuals experience compromised systemic and pulmonary functions (Mulcahey et al., 2021 ), leading to a reluctance to engage in strenuous physical activity at sports halls for a short duration. Consequently, there was a diminished recovery in passenger flow to sports halls. Although there is no lockdown ban, citizens avoid visiting by bus and maintain social distance.

Intriguingly, despite the overarching epidemic conditions, passenger counts for the transport serving major transport hubs such as airports, train stations, and ferry terminals exhibit comparatively minor reductions and rapid recovery, mainly aligning with the trends of Cluster 3 and Cluster 4. The relative stability of these numbers can largely be attributed to the inherent nature of long-distance travel across cities. Unlike short-range transit, which may be supplanted by walking, cycling, or personal vehicles, alternatives for long-haul journeys are notably limited, thereby maintaining a baseline demand for buses servicing these transport hubs even during the height of the COVID-19 pandemic.

The alterations in mobility patterns concerning residential—hospital are primarily observed within clusters characterized by a comparatively modest reduction in mobility. The OD trips exhibited fluctuation, characterized by a decline and a gradual recovery within 30 days. In the initial stages, individuals with chronic conditions are advised to minimize hospital visits to lower their exposure risk to COVID-19. However, the pandemic triggered an escalation in healthcare demand, as numerous individuals sought medical care, testing, and treatment (Birkmeyer et al., 2020 ; Peiffer-Smadja et al., 2020 ). The heightened need for infectious disease services counterbalanced the reduction in visits to other outpatient clinics in hospitals, thereby contributing to the recovery of passenger flow towards hospitals. Moreover, the surge in hospital visits necessitated that healthcare workers and other essential personnel continue to commute to and from hospitals, irrespective of the pandemic situation. Their unwavering travel patterns help maintain a basic level of passenger flow of over 40% trip rate.

The behavior disparity between residential—company exhibits various patterns, with a significant proportion found in various clusters. Overall, they are relatively less affected, serving as primary driving factors for urban mobility recovery. It is possibly related to the various industries of companies. On one hand, the characteristics of various industries significantly influence the patterns of public transport use. Employees in industries of internet technology and electric communication demonstrate greater adaptability to remote work during the COVID-19 pandemic. Due to the pressing demand for healthcare products, drugs, and research, stations located near pharmaceutical companies are expected to experience a faster recovery in passenger flow. The nature of manufacturing and factory work typically requires on-site participation, rendering remote work impractical. On the other hand, income levels within industries also influence travel patterns. Employees in the internet and telecommunications industries tend to have higher incomes and have more flexibility in choosing their transport modes, potentially opting for private vehicles over public transport during the COVID-19 pandemic to reduce exposure risks. In contrast, lower-income employees might be more reliant on public transportation to commute to their workplaces, maintaining the demand for buses. The income disparity between industries further contributes to the observed differences in passenger flow trends during the pandemic.

Travel behavior involving commercial service falls within Cluster 3 and Cluster 4. The effect on the service industry essentially aligns with the proportion of the population infected with the virus. Following about three years of "Zero-COVID" policy, the associated panic has largely dissipated. As people yearn to return to normalcy and recreational activities, customer flow at shopping malls stages a swift recovery in later phases. This observation appears comprehensible, given the fundamental nature of work activities as a pivotal component of economic endeavors. With the resumption of work activities, a consequential surge in demands for commercial activities is anticipated (Ma et al., 2023 ).

Overall, during the COVID-19 pandemic, commuting, commercial, and healthcare demand constituted the largest components of urban mobility. It is crucial to maintain and promptly restore the supply of buses among communities, transport hubs, hospitals, and companies. Integrating ULU data with OD data enables governments and transport operators to thoroughly analyze and elucidate shifts in mobility behavior during the pandemic. This comprehensive approach offers a solid foundation for well-informed policy development and implementation against unforeseen pandemics with high infection and low case fatality rates.

Travel mode

The total impact for the overall passenger volumes of subway, bus, and taxi services are computed, as presented in Fig. 3 c. The data indicates varying degrees of reduction in passenger flow, with subways (−9.42%  ⋅  days) experiencing the most significant decline, followed by buses (−8.63%  ⋅  days), and taxis (−7.90%  ⋅  days). This trend can be primarily attributed to the perceived risk of COVID-19 transmission in different transportation environments and the adaptive responses of urban populations to the pandemic.

Subways, typically characterized by high passenger densities and closed environments, represent the apex of perceived transmission risk. The significant drop in subway usage can be attributed to people’s avoidance of crowded spaces and potential virus hotspots. Furthermore, the role of subways as connectors of various urban hubs rendered them particularly vulnerable to reduced usage as individuals sought to minimize travel and potential exposure to the virus. In contrast, buses, while also experiencing a notable decline in passenger flow, were marginally less impacted than subways. This difference might be due to the varied nature of bus routes, some of which cater to essential travel less feasible via other means, and the slightly lower passenger densities compared to subways.

Taxis, offering more individualized and controlled travel environments, demonstrated the least reduction in passenger flow. This trend suggests a public preference for modes of transportation perceived as safer and less conducive to virus spread. Nonetheless, the overall decline in taxi usage reflects broader patterns of reduced mobility, driven by lockdown measures, the shift to remote work, and heightened public health awareness. Economic factors also played a role, as the financial impacts of the pandemic might have influenced individuals’ transportation choices, with taxis being a costlier option compared to public transit. Collectively, these observations underscore the multifaceted impact of the COVID-19 pandemic on urban transportation, shaped by an interplay of health, lifestyle, and economic considerations.

Dynamic model of urban mobility

We develop a dynamic OD mobility model to quantitatively simulate the fine-grained impact of the COVID-19 pandemic on OD passenger flow. The mobility changes can be ascribed to the rate of COVID-19 infection and the willingness to travel. We employ an epidemic transmission model, the susceptible-infectious-removed (SIR) model (Cooper et al., 2020 ; Keeling and Eames, 2005 ), to simulate the rise in infection cases in the city. In general, people who got afflicted with COVID-19 would recuperate at home for days, leading to a plunge in overall city-wide human mobility flow. However, some patients might choose to crowd into the hospitals which leads to an unusual increase in travel needs between urban lands. Therefore, we establish the willingness factors to represent the emotional effects of the COVID-19 pandemic on various types of mobility behavior. The disparity in OD passenger flow under high spatial granularity can be elucidated by the travel purposes and travel modes (refer to Methods for details).

Specifically, people in the dynamic OD model have three distinct states: usual (U), infectious (I), and recovered (R). (Fig. 4 ). In contrast to the classic SIR model, the usual state in the proposed model refers to passengers who remained uninfected by the COVID-19 pandemic, with unaffected travel willingness. Infectious passengers encompass those who have contracted the COVID-19 virus or have been driven by panic to abstain from bus travel. Over time, if affected passengers decide to resume travel, they transition out of the recovered state. The model has merely four free parameters that scale: (1) transmission rate of the COVID-19 pandemic, (2) recovery rate of the COVID-19 pandemic, (3) willingness factors for transmission, and (4) willingness factors for recovery; all four parameters persist as constants over time. The first two parameters, which are determined by the infectivity and virulence of the epidemic itself, remain consistent for all OD pairs. The latter two categories of parameters are determined by travel purposes, which can be inferred by the ULU surrounding OD bus stations.

figure 4

The model inputs encompass the pandemic’s transmission and recovery rates, as well as people’s willingness factors. Furthermore, the model requires the ULU information near bus stations and the pre-outbreak historical passenger flow levels. In the model, the passengers have usual (U), infectious (I), and recovered (R) states. We fit models to all OD pairs in bus, subway, and taxi systems, which shows full model fits of four clusters corresponding to Fig. 3 . The blue line represents the model predictions. As the trips of the OD passenger flow tend to have great variability, we also show the smoothed average (line of a different color from blue). Shaded regions denote the 2.5th and 97.5th percentiles across stochastic realizations. The green dashed line represents the predictions without considering willingness factors (In this case, the predictions for all clusters are consistent). We sample 100 parameter sets of willingness factors and perform stochastic realizations for each set.

The willingness factors serve as a measure of how much the panic mentality contributes to the decline and recovery in mobility. They reflect the degree of anxiety and perceived risk associated with traveling during the COVID-19 pandemic. For example, as the number of infectious passengers increases, other passengers perceive a higher risk of infection when taking the subway, which leads them to seek alternative means of transport or avoid traveling altogether. This corresponds to a negative willingness factor. Conversely, the surge in public health demand during the COVID-19 pandemic leads to positive willingness factors associated with medically related travel. The willingness factors collectively illustrate the various responses of different travel purposes to the COVID-19 pandemic.

Our model precisely matches the observed trips of OD pairs in Shenzhen from December 8, 2022, to January 6, 2023, as shown in Fig. 4 . As shown in Supplementary Fig. 1 , the average values of willingness factors correspond to the analysis of mobility patterns in section “Population behavior behind mobility patterns”. For example, the value of the willingness factor for recovery associated with the travel behavior of school-age children exhibits a significantly negative trend, indicating a slow recovery in their travel activities. This observation aligns well with the predominant mobility pattern observed within Cluster 1. The willingness factors for the recovery of “residential-transport hub” and “company-transport hub” are the two highest positive values, indicating that there are fewer viable alternatives for long-distance travel, and as a result, passenger flow is less affected. The distinctions between subway, bus, and taxi services can also be discerned from the willingness factors.

At a micro level, we can forecast the decline and recovery of mobility between any two stations or taxi-operating regions in Shenzhen. At a macro level, our model precisely captures the average mobility changes for the four clusters (Fig. 4 ). The dynamic OD model enables even a relatively straightforward UIR model to accurately fit observed passenger flow, despite mobility behavior during that period. This model offers valuable insights into urban mobility during potential future outbreaks of infectious diseases with high infection, empowering policymakers to deduce alterations in urban mobility and population behavior during the initial stages of an epidemic outbreak.

Although COVID-19 has become a familiar presence in our lives, it continues to pose a significant global threat. As we write this paper, the virus still claims a life every three minutes (WHO, 2023 ). The suffering and the painful lessons learned from the COVID-19 pandemic must not be in vain. The analytical framework and model proposed in this research contribute to the long-term management of the COVID-19 pandemic and offer tools to confront potential future viral epidemics.

Based on 148 million travel records, this paper examines the fine-grained spatio-temporal characteristics of urban mobility during the COVID-19 pandemic. It integrates these characteristics with an urban land use map to elucidate the heterogeneity in the decline and recovery of mobility. In terms of travel purposes, trips originating from schools and colleges experienced a sharp decline attributable to class suspensions and risk aversion to infections. Non-essential travel, such as visits to museums, sports halls, and parks, saw a substantial decrease and a gradual recovery. In contrast, travel associated with commuting, commercial services, and healthcare exhibited relatively modest declines and a quicker rebound. Regarding transportation modes, the most affected to least affected were subway, bus, and taxi. Subsequently, the UIR model with willingness factors is built, which comprehensively captures the influence of COVID-19 on travel willingness. In contrast to previous research that primarily focused on macro-level mobility analysis, we propose a set of indicators to measure the temporal variations in urban mobility and uncover the heterogeneity of mobility changes through clustering. There were rare urban mobility models at the block level encompassing various modes of transportation.

Our dataset has limitations as it does not encompass all modes of urban mobility. The model is also minimalist, neglecting regional differences in epidemic spread. Nevertheless, our findings are valuable for revealing spatial differences in the decline and recovery of travel. The proposed analytical framework and dynamic model are adaptable and extensible, which can be applied to different pandemics of other cities or other travel modes, capturing detailed aspects of real-world urban mobility.

This paper offers potential implications for future application and research aimed at formulating more targeted and effective public health policies and strategies. In the context of evaluating public health policies, our results can guide policymakers in assessing the opportunity cost of urban mobility limitations during large-scale pandemic outbreaks. The dynamic OD urban mobility model provides a benchmark for mobility changes without mobility-restricting intervention, enabling the precise evaluation of the impacts on the transmission rate and economic productivity. Additionally, in the realm of policy formulation, policy decisions can influence mobility behavior by altering travel willingness and emotions associated with different POI types, significantly affecting the outcome of the epidemic infection. Improved paid leave policy or income support can reduce mobility for essential staff during illness. Strengthening hygiene and disinfection, maintaining proper ventilation, and increasing routes between origins and destinations of high travel demand contribute to enhanced passenger safety. Lastly, in terms of future academic research, fusing large language models with computational, interactive agents seems to be a path to realistic simulations of human behavior (Xu et al., 2023 ). Based on emerging urban sensing technologies (e.g., drones (Wang et al., 2022 ) and crowdsensing (Chen et al., 2019 ; Guo et al., 2021 ; Xia et al., 2023 ), diverse and heterogeneous human behavioral data are continuously mined. Integrating travel behavior and large models to construct urban individual profiles for simulating travel behaviors appears to be a promising direction. Additionally, we will continue to investigate the changes and resilience of urban elements when faced with restricted urban mobility, including variations in air pollution (Chen et al., 2020 ), unmanned delivery (Chen et al., 2022 ), and emergency communication (Ren et al., 2023 ).

Origin-destination data of urban mobility

Thanks to the extensive deployment of mobile devices (Chen et al., 2020 ), the travel data are collected by a public transportation service company in Shenzhen. The original data, gathered by 663 bus routes, 16 subway routes, and 17,826 taxis during operation, total comprises approximately 148 million trips. Through advanced data collection (Li et al., 2022 ) and processing techniques (Chen et al., 2018 ), aggregated OD data can be obtained. The data collection period for bus-related information spans from October 15, 2022, to January 6, 2023, while that for subway and taxi data extends from November 1, 2022, to January 6, 2023. The OD data for buses has a shape of (3010, 3010, 98), where the first and second dimensions correspond to 3010 bus stations representing origins and destinations, respectively, and the third dimension represents dates. Similarly, the OD data for subways has a shape of (240, 240, 67) with a similar interpretation. The original data for taxis consists of individual trip records, including vehicle ID, pick-up time, drop-off time, pick–up latitude and longitude, and drop-off latitude and longitude, among other details. Based on the urban land use (ULU) map in Shenzhen (Fig. 5 a), which can be divided into 11362 regions (Gong et al., 2020 ), we perform individual matching of each order’s latitude and longitude to the corresponding urban region or the nearest urban region based on proximity to the region’s boundaries. The taxi OD data has been organized into a matrix of shapes (11362, 11362, 67). Among them, we specifically focused on the data of 56 million urban mobility records spanning from December 8, 2022, to January 6, 2023. The data preceding December 7, 2022, are utilized to calculate the baseline level of mobility and assist in obtaining the spatial attractiveness of ULU.

figure 5

a This map shows urban land use categories in Shenzhen. b This figure illustrates the process of obtaining the urban land use feature vector.

This dataset, capturing changes in passenger flow before and after China’s cancellation of the "Zero-COVID" policy, is distinct from other datasets. China adhered to the "Zero-COVID" policy from 2020 to 2022, with over 99% of Chinese residents never having been infected with COVID-19. After the policy’s cancellation on December 7, 2022, Shenzhen experienced the entire process of large-scale COVID-19 spread without mobility-restricting intervention. As of July 23rd, 2023, China has implemented universal and free COVID-19 vaccination for its population. The reported vaccination rates are as follows (Xinhua, 2022 ): a cumulative coverage rate of 92.1% for the first dose, a completion rate of 89.7%, and an enhanced immunization rate of 71.7%. Among individuals aged 60 and above, the rates are 89.6% for at least one dose, 84.7% for completion, and 67.3% for enhanced immunization. The widespread administration of vaccines has significantly reduced the mortality rate, facilitating recovery from COVID-19. However, the transmissibility of SARS-CoV-2 remains high. This data offers insights into the effects of highly infectious and low-toxicity viruses on social mobility and public transport use under natural transmission conditions in the city. This information provides a baseline for assessing the potential impact of future infectious diseases on urban mobility in the absence of government intervention.

This dataset encompasses the entire process of trip changes, from the initial stage to a sharp decline, and ultimately to a gradual recovery during large-scale infectious disease outbreaks in cities. From November 1, 2022, to November 25, 2022, Shenzhen experienced fewer than 50 confirmed cases per day, and the daily lives of citizens remained largely unaffected (Shenzhen Government, 2022 ). The data from this period can be considered the baseline passenger volume for Shenzhen’s bus system. As the number of confirmed cases progressively increased, the government implemented stringent lockdown measures from November 25, 2022, to December 7, 2022, resulting in a substantial decrease in passenger flow. As of December 7, 2022, the cumulative number of confirmed cases constituted less than 0.1% of the total population. The official outbreak of the COVID-19 pandemic transpired after the cancellation of the "Zero-COVID" policy. The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly (Leung et al., 2023 ), which is evident in the drastic reduction of passenger flow within the public transport system. Subsequently, as a majority of urban residents became infected and recovered, the public transport passenger flow gradually rebounded, as depicted in Fig. 1 .

Assuming the passenger flow from origin i to destination j on day t is denoted as f i , j , t , the entire dataset can be denoted as

where M represents the sum of the number of bus stations, subway stations, and the total number of taxi-operating urban regions. T is the number of days.

Point of interest data and urban land use data

As online social media and mobile communication continue to flourish, location-based service (LBS) systems such as Google Places, Gaode Maps (China Satellite Maps), and Facebook are gaining popularity across various sectors (Han et al., 2021 ; Xing et al., 2020 ). These LBS systems enable users to search for points of interest (POIs) to access better services and share experiences from places they have visited (Chang et al., 2021 ). Generally, a POI is a specific location that individuals may find useful or intriguing. The term is commonly used to refer to commercial services, schools, subway stations, or other categories found in digital maps (Wang et al., 2019 ; Zhu et al., 2020 ).

A detailed land use map of Chinese cities can be derived by combining POI data with 10-meter satellite imagery, OpenStreetMap, nighttime lights, and Tencent social big data (Gong et al., 2020 ). This map serves as a valuable resource for inferring travel intentions. As the data is from 2018, we complement the newly developed land use attributes based on the latest available POI data. The data sources include Gaode Maps and Baidu Maps, which are leading providers of digital map content, navigation, and location service solutions in China. Important urban land use categories for travel are selected, including residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery.

OD data preprocessing

For each OD pair ( i ,  j ) in bus, subway, and taxi systems, the time series of mobility can be represented as:

Since the passenger flow at a single OD pair exhibits randomness, f i , j experiences significant fluctuations over time, as shown in Supplementary Fig. 2 . To better illustrate the impact of COVID-19 on public transport, we apply Kalman filtering to smooth the data.

Kalman filtering is a recursive algorithm utilized for estimating the state of a dynamic system by combining noisy measurements with a mathematical model of the system (Chui et al., 2017 ). When applied to noisy time series data, the Kalman filter can provide a smoothed version of the data by recursively estimating the underlying state of the system that generated the data. The filter accounts for uncertainties in both the measurements and the system’s model, rendering it particularly effective at reducing noise while preserving the true signal. Upon applying Kalman filtering, the resulting sequence for the OD station pair ( i ,  j ) is denoted as:

The mobility between different OD pairs exhibits significant variation, with a maximum difference spanning several orders of magnitude. To uncover the general patterns impact of large-scale infectious diseases on passenger flow, it is essential to perform normalization on the time series \({{{{{\bf{f}}}}}^{{\prime} }}_{i,j}\) . Specifically, for any OD pair ( i ,  j ), the passenger flow on December 8, 2022 is considered a normal level unaffected by the epidemic. The normalized time series is calculated by:

Identifying mobility patterns via clustering algorithm

Measuring of mobility changes.

The features of changes in mobility are developed to facilitate description and subsequent clustering analysis. Drawing inspiration from the resilience in the fields of biology and engineering (Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ), we develop a set of features to comprehensively quantify the decline and recovery patterns of travel before, during, and after a large-scale infectious disease outbreak. These features can characterize the temporal changes in the response of mobility behavior to the pandemic, which reflects the interplay between human behavior, public policy, and the built environment, and provides insights into the dynamics of urban mobility under extraordinary circumstances.

Firstly, for any OD time series \({{{{{\bf{f}}}}}^{{\prime}{\prime}}}_{i,j}\) , we can identify several basic features, including the maximum value R top before the passenger flow decreases, the minimum value R bottom of passenger flow, the final passenger flow level R end , and their corresponding time moments t 0 , t 1 , t 2 , t 3 . The definition and significance of the features are as follows.

Declining speed Γ: The speed at which trips decrease serves as a measure of how quickly urban populations adapt their mobility behavior in response to public health crises. It is calculated by:

Declining amplitude Λ: The maximum amplitude of the decrease in trips directly mirrors the influence of the epidemic on mobility behavior.

Trough duration Π: The third indicator of mobility changes we define pertains to the trough duration of mobility. This indicator captures the persistence of the reduced trips during the COVID-19 pandemic, reflecting the extent to which the usual travels are disrupted.

Recovery speed Θ: When infected individuals gradually recover and the overall panic levels subside, this metric captures the speed at which urban mobility bounces back to normalcy. The determination of the indicator is achieved through:

Recovery amplitude ϒ: The amplitude of mobility recovery highlights the degree to which trips rebound after the initial impact of the pandemic. As infected citizens gradually recover and no longer reject traveling, this indicator provides insights into the interplay between public health measures, human behavior, and the ability of societies to regain their normal functioning after experiencing a large-scale infectious disease outbreak.

Total impact Δ: We finally introduce a comprehensive metric that captures the decline and recovery process of the pandemic’s impact on mobility. This total impact indicator effectively represents the degree to which the pandemic affects mobility, calculated as the area between the curve and the usual level of trips using the formula:

A lower value indicates a more substantial influence of the epidemic on mobility.

Therefore, we extract features of mobility change from each OD array ( i ,  j ):

K-means++ clustering

In order to identify change patterns of OD mobility during the pandemic, we perform the K-means++ clustering algorithm on the OD dataset. K -means++ clustering is an unsupervised machine learning algorithm used for partitioning a given dataset into a specified number ( K ) of clusters.

K-means++ clustering aims to minimize the within-cluster sum of squared (WCSS) distances from the data points to their respective cluster centroids by iteratively updating the centroids and assigning the data points. Specifically, the input of the K-means++ clustering algorithm is

where each OD time series is represented by the features.

The algorithm first needs to initialize the cluster center. A centroid c 1 is randomly chosen from X . For each x i , j   ∈   X , compute the squared distance to its nearest centroid:

where \({{{\mathcal{C}}}}={\left\{{c}_{p}\right\}}_{p = 1,...,k-1}\) represents the set of centroids chosen so far. Then, the next centroid c k is determined from the data points with probability proportional to \(D{({x}_{i})}^{2}\) :

Repeat the two steps until we have taken K centers altogether.

After selecting the K initial centroids, the algorithm proceeds with the standard iterative updates of centroids. Step 1 is assigning x i , j to the closest centroid using ( 13 ). Suppose that at iteration n , the dataset is divided into K clusters:

In step 2, calculate new centroid positions for each cluster by minimizing the WCSS distance:

The two steps are repeated until convergence is reached, i.e., when the assignment of samples to clusters no longer changes.

The elbow method is utilized to determine the optimal number of clusters, denoted as K , in K-means clustering (Bholowalia and Kumar, 2014 ). The elbow method involves plotting the variance explained or the sum of squared distances (SSE) of the data points to their cluster centroids against different values of K . The elbow method suggests selecting the value of K at the “elbow" or bend in the SSE plot (Supplementary Fig. 3a) . This point represents the optimal trade-off between minimizing SSE and avoiding excessive complexity in the clustering model.

After establishing a preliminary range for K using the elbow method, Silhouette Analysis is employed to finalize the value of K (Lleti et al., 2004 ). For each K -value, silhouette coefficient is computed for each data point, which measures the similarity between each data point and the cluster to which it is assigned, with values ranging from –1 to 1. Subsequently, the average silhouette coefficient is calculated for all data points at each K -value. This average value serves as an indicator of cluster cohesion and separation, with higher values indicating better clustering results. As depicted in Supplementary Fig. 3b , the analysis determined that the optimal value for K  = 4.

Finally, we derive K clusters of OD mobility, as shown in Fig. 3 a. The labels for each OD pair have been obtained. Furthermore, the OD pairs can be associated with spatial information, which leads to subsequent analysis.

Spatial attractiveness of urban land use

Our goal is to illuminate the variation in mobility for OD pairs during a widespread infectious disease outbreak. Solely analyzing the geographical locations of the starting and ending stations of travel, however, is inadequate. As a result, we endeavor to extract travel purposes by integrating the urban land use map.

Lands in close proximity to bus stations, subway stations, and taxi pick-up and drop-off points provide insight into passengers’ potential travel motivation. For example, when a bus station is located in a residual land, there is a high probability that passengers disembarking at this station intend to commute to their residences. Accordingly, a ULU feature vector is established for each origin and destination, which is represented as:

where r  = 1, 2, . . . ,  R represents urban land use categories. s i , r is defined as the spatial attractiveness of land use category r to the citizens boarding or disembarking from the station or urban regions i ( i  = 1, 2, . . . ,  M ).

For the bus stations and subway stations, a common method (Xing et al., 2020 ) is to use the number of lands to determine s i , r :

where z i , r denotes the number of lands belonging to category r within a certain distance range of the stations. Taking into account the varying importance of stations, the distance range for bus stations is set at 200 meters, while the distance range for subway stations is set at 500 meters, as presented in Fig. 5 b.

As mentioned in “Origin-destination data of urban mobility”, the taxi OD data represents the mobility between 11,362 distinct regions within the city. These urban regions have been delineated with nearly homogeneous land use attributes. Therefore, s i , r can be derived directly:

For an urban region with the land use attribute r , the s i , r value corresponds to 1, while the remaining values of the land feature vector are set to 0.

As each urban land use category can be mapped to an activity, identifying the most probable land use category corresponds to determining the most likely activity to be undertaken. To further analyze the mobility behavior from the origin i to the destination j , we compute the travel transition matrix:

The elements in the matrix S i , j represent the likelihood of passengers transitioning from a land use category at the origin station (region) to a ULU category at the destination station (region). For instance, s i ,1 s j ,2 in the bus system corresponds to passengers departing from land use category 1 near bus station i and arriving at land use category 2 near bus station j . From this, we can infer the passengers’ mobility behavior and purposes. The travel transition matrix for the subway and taxi follows the same principle.

We combine the K clusters of change patterns with the spatial attractiveness of urban land use. For each cluster, we sum the corresponding S i , j values to obtain a total transition matrix. Considering daily frequency changes in passenger flow, the passenger flow from station i to station j is approximately equal to the passenger flow from station j to station i on a daily timescale. Based on the symmetry of OD passenger flow, we add the lower and upper triangular parts of the total transition matrix. Finally, we identify the top values in the matrix that correspond to the primary OD land pairs and their corresponding proportions of mobility behavior within each cluster.

Dynamic OD mobility model

We develop a dynamic model to capture the fine-grained urban mobility changes. The decline in trips can be attributed to two factors: physiological infection with viruses and emotional influence. Inspired by the classic epidemic transmission model SIR (Cooper et al., 2020 ; Keeling and Eames, 2005 ), an OD passenger flow model is proposed, comprehensively considering the spread of the COVID-19 pandemic and the willingness to travel via public transportation, as shown in Fig. 4 .

The model incorporates pandemic transmission and recovery rates, willingness factors of mobility behavior, urban land use map, and pre-pandemic OD passenger flow as input. It outputs the changes in trips for every OD pair during the COVID-19 pandemic with high spatial resolution.

Physiological infection module

Prior to the outbreak of large-scale infectious diseases, we assume that the daily number of population from station i to station j is W i , j . Following the epidemic outbreak, all passengers can be categorized into three states: usual (U), infected (I) by the virus or panic, and recovered (R). The relationship among passengers associated with the three states is shown in the subsequent ( 22 ).

Specifically, initially uninfected people may become infected with the virus through contact with infected individuals. Eventually, the infected people transition to a recovery state. For each citizen, the probability state transition process of physiological infection is as follows:

where U i , j , t ,  I i , j , t ,  R i , j , t denote passenger numbers of usual state, infected state, and recovery state, respectively on the t day. β and γ represent the transmission and recovery rates, respectively, which are inherent attributes of the pandemic spread.

Willingness influence module

During the pandemic, urban residents tend to reduce their travel activities to mitigate the risk of infection. This tendency is particularly pronounced when there is a higher reported or perceived number of infections and potential cases through news outlets, social media, or personal observations, leading to increased levels of anxiety. During the recovery phase, mobility gradually rebounds over time, and confidence in travel is steadily restored. Furthermore, the decline and recovery of different travel behaviors exhibit heterogeneity. From the clustering results in the section “Population behavior behind mobility patterns”, it is evident that the decrease and recovery of trips are associated with travel purposes.

Willingness factors are proposed to model the willingness influence. It is assumed that the willingness factors of travel from ULU type r to ULU type v follow normal distributions:

where r ,  v  = 1, 2, , . . . ,  R . \({\mu }_{r,v},{\sigma }_{r,v}^{2}\) and \({{\mu }^{{\prime} }}_{r,v},{\sigma }_{r,v}^{{\prime} 2}\) are distribution parameters (mean and variance) of willingness factors for transmission and recovery, respectively.

In terms of travel mode choices, individuals tend to opt for modes associated with lower exposure to viral risks. Consequently, the willingness factor for transmission and recovery in the context of subway, bus, and taxi are denoted as \({\mu }_{q},{{\mu }^{{\prime} }}_{q}(q=1,2,3)\) .

The probability state transition process of emotional influence on travel is as follows:

Put it together

For an OD pair ( i ,  j ) of transport mode q , the travel transition matrix S i , j decomposes travel for a given OD pair into combinations of various trip purposes. Consequently, it enables the synthesis of willingness factors, thereby generating the willingness factor for that specific OD pair. The willingness factors can be estimated by linearly stacking the sampled willingness factors for travel purposes:

The mobility change for OD pair ( i ,  j ) can be determined through the following equation.

The time series of the passenger flow from origin i to destination j is:

W i , j is determined by the trips of OD ( i ,  j ) prior to the pandemic outbreak. α i , j and \({\alpha }_{i,j}^{{\prime} }\) denote willingness factors for transmission and recovery, respectively. These willingness factors depend on the ULU feature arrays of the origin i and the destination j , accounting for the diversity of OD passenger flow. The initial values of U i , j , t ,  I i , j , t ,  R i , j , t are determined by the initial infection rate in the city.

By obtaining the changes in passenger flow at all OD pairs, we can also derive the overall change in trips under the impact of the pandemic. This model characterizes the response of an entire city’s public transport system to the pandemic and the shifts in mobility behavior.

Model validation

The alterations in actual OD passenger flow within the Shenzhen bus, subway, and taxi systems during the COVID-19 pandemic serve to verify the effectiveness of the dynamic model. Research (Cai et al., 2022 ; Leung et al., 2023 ; Ren et al., 2022 ; Ribeiro Xavier et al., 2022 ) indicates that the transmission rate of SARS-CoV-2 ranges from approximately 0.2 to 0.4, while the recovery rate spans from approximately 0.05 to 0.15. These values are determined by the inherent transmission characteristics of COVID-19. This paper primarily concentrates on modeling the variations in mobility for different OD stations. Without loss of generality, we employ β  = 0.3,  γ  = 0.1.

When calculating the willingness factor for recovery, we consider only the main ULU pairs depicted in Fig 3 b. Consequently, the parameter estimation target constitutes the mean values and standard deviations of willingness factors for OD ULU pairs. Based on the maximum likelihood estimation (MLE) method, we find the optimal parameter values of the willingness factor distributions, enabling the predictive changes in the mobility model to approximate the actual trend. Specifically, we employed the grid search method to identify the values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) that correspond to the minimum root mean squared error (RMSE) between the true trips and predicted trips. These values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) are treated as sampling data for the distribution of willingness factors to be estimated. Subsequently, we determined the parameter values that maximize the probability of sampling data occurrence, resulting in the optimal mean values and standard deviations of the empirical factors.

The optimization results show that the values of willingness factors for transmission are similar for all OD station pairs, whereas the willingness factors for recovery primarily contribute to the diversity observed in mobility behavior, as displayed in Supplementary Fig. 1 . Under the optimal parameters, we sample and derive 100 sets of dynamic model parameters. The predictive trips in Shenzhen during the COVID-19 pandemic are shown in Fig. 4 .

Besides, an analysis is conducted to evaluate the robustness of the UIR model. The values of β and γ vary by approximately ± 30% and observe the model performance while keeping other parameters constant. This analysis focused on calculating the average root mean square error (RMSE) between the actual and predicted values of passenger flow across various OD pairs in the city. The RMSE for each trip series provided a daily prediction error. As shown in Supplementary Fig. 4 , our findings indicate that for every 1% deviation in β , the prediction error increased by approximately 0.61%, and for every 1% deviation in γ , the prediction error increased by around 0.77%. These results suggest a certain level of sensitivity of the model to these parameters.

Data availability

Urban land use categories are available at . Points of interest (POIs) data can be openly accessed via the APIs of Gaode Maps ( ) and Baidu Maps ( ). Raw origin-destination (OD) data of buses and taxis are available with the permission of Shenzhen Bus Group Co., Ltd. ( ) upon request for academic cooperation. The de-identified data are available at .

Arellana J, Márquez L, Cantillo V (2020) COVID-19 outbreak in Colombia: an analysis of its impacts on transport systems. J Adv Transp 2020:1–16.

Article   Google Scholar  

Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. Soda 7:1027–1035

MathSciNet   Google Scholar  

Atkinson-Clement C, Pigalle E (2021) What can we learn from COVID-19 pandemic’s impact on human behaviour? The case of France’s lockdown. Humanit Soc Sci Commun 8(1):81.

Balmford B, Annan JD, Hargreaves JC, Altoè M, Bateman IJ (2020) Cross-country comparisons of COVID-19: policy, politics and the price of life. Environ Resour Econ 76:525–551.

Betthäuser BA, Bach-Mortensen AM, Engzell P (2023) A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav 7:375–385.

Article   PubMed   Google Scholar  

Bholowalia P, Kumar A (2014) EBK-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl 105(9):17–24

Google Scholar  

Birkmeyer JD, Barnato A, Birkmeyer N, Bessler R, Skinner J (2020) The impact of the COVID-19 pandemic on hospital admissions in the United States: study examines trends in US hospital admissions during the COVID-19 pandemic. Health Affairs 39(11):2010–2017.

Burki T (2022) Dynamic zero COVID policy in the fight against COVID. Lancet Respir Med 10(6):e58–e59.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cai J, Deng X, Yang J et al. (2022) Modeling transmission of SARS-CoV-2 omicron in China. Nat Med 28(7):1468–1475.

Chang S, Pierson E, Koh P, Gerardin J, Redbird B, Grusky D, Leskovec J (2021) Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589(7840):82–87.

Article   ADS   CAS   PubMed   Google Scholar  

Chen L, Xu F, Han Z, Tang K, Hui P, Evans J, Li Y (2022) Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity. Nat Hum Behav 6:1503–1514.

Chen X, Wang H, Li Z et al. (2022) DeliverSense: efficient delivery drone scheduling for crowdsensing with deep reinforcement learning. In: UbiComp/ISWC’22 Adjunct, Cambridge, United Kingdom, 11–15 September, pp. 403–408

Chen X, Xu S, Fu H, Joe-Wong C, Zhang L, Noh H.Y., Zhang P (2019) Asc: Actuation system for city-wide crowdsensing with ride-sharing vehicular platform. In: Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, Montreal, Quebec, Canada, 15 April, pp. 19–24

Chen X, Xu S, Han J et al. (2020) Pas: prediction-based actuation system for city-scale ridesharing vehicular mobile crowdsensing. IEEE Internet Things J 7(5):3719–3734.

Chen X, Xu S, Liu X, Xu X, Noh HY, Zhang L, Zhang P (2020) Adaptive hybrid model-enabled sensing system (HMSS) for mobile fine-grained air pollution estimation. IEEE Trans Mobile Comput 21(6):1927–1944.

Chen X, Xu X, Liu X, Pan S, He J, Noh HY, Zhang L, Zhang P (2018) Pga: Physics guided and adaptive approach for mobile fine-grained air pollution estimation. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore, 08–12 October, pp. 1321–1330

China Center for Disease Control and Prevention (2022) China Daily Perspectives: the dynamic COVID-zero strategy in China. . Accessed 24 May, 2023

Chui CK, Chen G et al. (2017) Kalman filtering. Springer

Clark A, Jit M, Warren-Gash C et al. (2020) Global, regional, and national estimates of the population at increased risk of severe covid-19 due to underlying health conditions in 2020: a modelling study. Lancet Glob Health 8(8):e1003–e1017.

Cooper I, Mondal A, Antonopoulos CG (2020) A SIR model assumption for the spread of COVID-19 in different communities. Chaos Soliton Fract 139:110057.

Article   MathSciNet   Google Scholar  

Forzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A (2022) Emerging signals of declining forest resilience under climate change. Nature 608(7923):534–539.

Gkiotsalitis K, Cats O (2021) Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transp Rev 41:374–392.

Gong P, Chen B, Li X et al. (2020) Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Sci Bull 65(3):182–187.

Guo B, Wang S, Ding Y, Wang G, He S, Zhang D, He T (2021) Concurrent order dispatch for instant delivery with time-constrained actor-critic reinforcement learning. In: 2021 IEEE Real-Time Systems Symposium (RTSS), pp. 176–187

Han Z, Fu H, Xu F, Tu Z, Yu Y, Hui P, Li Y (2021) Who will survive and revive undergoing the epidemic: Analyses about POI visit behavior in Wuhan via check-in records. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, NY, USA, vol. 5, pp. 1–20

Keeling MJ, Eames KT (2005) Networks and epidemic models. J R Soc Interface 2(4):295–307.

Article   PubMed   PubMed Central   Google Scholar  

Kumpfer KL (2002) Factors and processes contributing to resilience: the resilience framework. Resilience and development: positive life adaptations. 179–224.

Leung K, Lau EH, Wong CK, Leung GM, Wu JT (2023) Estimating the transmission dynamics of SARS-CoV-2 Omicron BF. 7 in Beijing after the adjustment of zero-COVID policy in November-December 2022. Nat Med 29:579–582.

Article   CAS   PubMed   Google Scholar  

Levin R, Chao DL, Wenger EA, Proctor JL (2021) Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning. Nat Comput Sci 1(9):588–597.

Li Z, Man F, Chen X, Zhao B, Wu C, Chen X (2022) TRACT: Towards large-scale crowdsensing with high-efficiency swarm path planning. In: UbiComp/ISWC’22 Adjunct, Cambridge, United Kingdom, 11–15 Sept, pp. 409–414.

Liu Y, Yu Y, Zhao Y, He D (2022) Reduction in the infection fatality rate of Omicron variant compared with previous variants in South Africa. Int J Infect Dis 120:146–149.

Lletı R, Ortiz MC, Sarabia LA, Sánchez MS (2004) Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Anal Chim Acta 515(1):87–100

Ma S, Li S, Zhang J (2023) Spatial and deep learning analyses of urban recovery from the impacts of COVID-19. Sci Rep 13(1):2447.

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Martínez L, Short J (2021) The pandemic city: urban issues in the time of COVID-19. Sustainability 13(6):3295.

Menkir TF, Chin T, Hay JA et al. (2021) Estimating internationally imported cases during the early COVID-19 pandemic. Nat Commun 12(1):311.

Mulcahey MK, Gianakos AL, Mercurio A, Rodeo S, Sutton KM (2021) Sports medicine considerations during the COVID-19 pandemic. Am J Sports Med 49(2):512–521.

Peiffer-Smadja N, Lucet J, Bendjelloul G et al. (2020) Challenges and issues about organizing a hospital to respond to the COVID-19 outbreak: experience from a French reference centre. Clin Microbiol Infect 26(6):669–672.

Qian T, Chen X, Xin Y, Tang W, Wang L (2022) Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks. Energy 239:122158.

Ren J, Xu Y, Li Z, Hong C, Zhang XP, Chen X (2023) Scheduling UAV swarm with attention-based graph reinforcement learning for ground-to-air heterogeneous data communication. In: UbiComp/ISWC’23 Adjunct, Cancun, Quintana Roo, Mexico, 08–12 October, pp. 670–675. (2023)

Ren SY, Wang WB, Gao RD, Zhou AM (2022) Omicron variant (B. 1.1. 529) of SARS-CoV-2: mutation, infectivity, transmission, and vaccine resistance. World J Clin Cases 10(1):1

Ribeiro XC, Sachetto OR, da FVV et al. (2022) Characterisation of Omicron variant during COVID-19 pandemic and the impact of vaccination, transmission rate, mortality, and reinfection in South Africa, Germany, and Brazil. BioTech 11(2):12.

Article   CAS   Google Scholar  

Schwarz S (2018) Resilience in psychology: a critical analysis of the concept. Theory Psychol 28(4):528–541.

She J, Liu L, Liu W (2020) COVID-19 epidemic: disease characteristics in children. J Med Virol 92(7):747–754.

Shen J, Duan H, Zhang B et al. (2020) Prevention and control of COVID-19 in public transportation: experience from China. Environ Pollut 266(2):115291.

Shenzhen Government (2023) Notice of the Shenzhen municipal people’s government on issuing the overall plan and three year action plan for park city construction in Shenzhen (2022–2024). . Accessed 3 Aug 2023

Shenzhen Open Data Platform (2022) COVID-19 in Shenzhen—statistics of daily confirmed cases. . Accessed 12 Aug 2023

Sibley C, Greaves L, Satherley N et al. (2020) Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am Psychol 75(5):618–630.

Standish RJ, Hobbs R, Mayfield M et al. (2014) Resilience in ecology: Abstraction, distraction, or where the action is? Biol Conserv 177:43–51.

Sun H, Forsythe W, Waters N (2007) Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Netw Spat Econ 7:353–376.

Tabatabaei NM, Ravadanegh SN, Bizon N (2018) Power systems resilience. Springer

Taskinsoy J (2020) COVID-19: Is the great outbreak a sign of what the future has stowed for the human race? Available at SSRN 3597434.

Tirachini A, Cats O (2020) COVID-19 and public transportation: current assessment, prospects, and research needs. J Public Transp 22(1):1–21.

Tisdell CA (2020) Economic, social and political issues raised by the COVID-19 pandemic. Econ Anal Policy 68:17–28.

United Nations (2018) 68% of the world population projected to live in urban areas by 2050, says UN. . Accessed 20 Aug 2022

Wang D, Tayarani M, He BY, Gao J, Chow JYJ, Gao HO, Ozbay K (2021) Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit. Transp Res Part A Policy Pract 153:151–170.

Wang H, Chen X, Cheng Y et al. (2022) H-SwarmLoc: efficient scheduling for localization of heterogeneous MAV swarm with deep reinforcement learning. In: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, Boston, Massachusetts, US, November, pp. 1148–1154

Wang J, Huang J, Yang H, Levinson D (2022) Resilience and recovery of public transport use during COVID-19. npj Urban Sustain 2(1):18.

Wang S, He T, Zhang D et al. (2019) Towards efficient sharing: a usage balancing mechanism for bike sharing systems. In: WWW’19, San Francisco, CA, USA, 13-17 May, pp. 2011–2021

Wei Y, Wang J, Song W, Xiu C, Ma L, Pei T (2021) Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model. Cities 110:103010.

Weiss M, Schwarzenberg A, Nelson R, Sutter K, Sutherland M (2020) Global economic effects of COVID-19. Congressional Research Service. pp. 21–35

World Health Organization (WHO) (2023) WHO Director-General’s opening remarks at the media briefing—5 May 2023. . Accessed 22 Jun 2023

Xia K, Lin L, Wang S, Wang H, Zhang D, He T (2023) A predict-then-optimize couriers allocation framework for emergency last-mile logistics. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA, 6–10 August, pp. 5237–5248.

Xing Y, Wang K, Lu JJ (2020) Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China. J Transp Geogr 87:102787.

Xinhua News Agency (2022) The overall coverage rate of COVID-19 vaccine in China. . Accessed 11 Jul 2023

Xu F, Zhang J, Gao C, Feng J, Li Y (2023) Urban generative intelligence (UGI): a foundational platform for agents in embodied city environment.

Zhang A, Zhang K, Li W, Wang Y, Li Y, Zhang L (2022) Optimising self-organised volunteer efforts in response to the COVID-19 pandemic. Humanit Soc Sci Commun 9(1):134.

Zhang J (2021) People’s responses to the COVID-19 pandemic during its early stages and factors affecting those responses. Humanit Soc Sci Commun 8:37.

Zhao P, Cao Z, Zeng DD, Gu C, Wang Z, Xiang Y, Qadrdan M, Chen X, Yan X, Li S (2021) Cyber-resilient multi-energy management for complex systems. IEEE Trans Industr Inform 18(3):2144–2159.

Zhu X, Wang S, Guo B et al. (2020) SParking: A win-win data-driven contract parking sharing system. In: UbiComp/ISWC ’20 Adjunct, Virtual Event, Mexico, 12–16 September, pp. 596–604

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This paper was supported by the National Key R&D Program of China No. 2022YFC3300703, the Natural Science Foundation of China under Grant No. 62371269. Guangdong Innovative and Entrepreneurial Research Team Program No. 2021ZT09L197, Shenzhen 2022 Stabilization Support Program No. WDZC20220811103500001, Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan No. JC20220011, the Project from Science and Technology Innovation Committee of Shenzhen (Grant No. KCXST20221021111201002), the Major Key Project of PCL (Peng Cheng Laboratory) under Grants PCL2023A09, and Meituan.

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These authors contributed equally: Baining Zhao, Xuzhe Wang.

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Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Baining Zhao, Xuzhe Wang, Tianyu Zhang, Fanhang Man, Yang Li & Xinlei Chen

Peng Cheng Laboratory, Shenzhen, China

Baining Zhao, Tao Sun & Xinlei Chen

Institute of artificial intelligence, Beihang University, Beijing, China

Department of Electronic Engineering, Tsinghua University, Beijing, China

Fengli Xu & Yong Li

Department of Mathematics, Shenzhen University, Shenzhen, China

Erbing Chen

RISC-V International Open Source Laboratory, Shenzhen, China

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Fellow researchers and authors contributed as follows: Conceptualization: BZ, RS, YL, & XC; methodology: XW, BZ, & TS; formal analysis: RS, FX, & XC; writing/original draft preparation: BZ, XW, FM, EC, & TZ; writing/review and editing: TS, XW, XC, BZ, & YL; BZ, XW, & TS contributed equally to this work and share first authorship. Correspondence to XC.

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