Mapping how data can help address COVID-19

Data & Policy Blog
Apr 15, 2020 · 8 min read

By Andrew J. Zahuranec and Stefaan G. Verhulst (GovLab, NYU)

The novel coronavirus disease (COVID-19) is a global health crisis the likes of which the modern world has never seen. Amid calls to action from the United Nations Secretary-General, the World Health Organization, and many national governments, there has been a proliferation of initiatives using data to address some facet of the pandemic. In March, The GovLab at NYU put out its own call to action, which identifies key steps organizations and decision-makers can take to build the data infrastructure needed to tackle pandemics. This call has been signed by over 400 data leaders from around the world in the public and private sector and in civil society.

But questions remain as to how many of these initiatives are useful for decision-makers. While The GovLab’s living repository contains over 160 data collaboratives, data competitions, and other innovative work, many of these examples take a data supply-side approach to the COVID-19 response. Given the urgency of the situation, some organizations create projects that align with the available data instead of trying to understand what insights those responding to the crisis actually want, including issues that may not be directly related to public health.

We need to identify and ask better questions to use data effectively in the current crisis. Part of that work means understanding what topics can be addressed through enhanced data access and analysis.

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Using The GovLab’s rapid-research methodology, we’ve compiled a list of 12 topic areas related to COVID-19 where data and analysis is needed. This rapid topic map — a scan of the issues meant to provide a basic overview of the situation — is not comprehensive. We realize we are not public health experts and that the crisis is complex, evolving and defined by uncertainty. Still, this listing, based on public reporting, aims to provide a brief overview of some of the topics facing decision-makers to inspire innovative data work on different parts of the crisis. Our review sees major issues facing:

Public Health Needs:

1. Tracking Disease Spread: Responses to COVID-19 face the difficult problem of identifying where cases are. As Indermit Gill, Stanford University professor and Brookings Institution fellow notes, “[m]ore than three months into the outbreak, we have no clue about how many people are infected.” Uncertainty remains on both spread in communities and in terms of individual risk factors. Data-driven methods to address these problems could involve identifying the infected — through self-reporting, improved diagnostics, and contact tracing — or to predicting future spread by extrapolating on population flows, commerce, and other data resources.

2. Developing Disease Treatment: Another serious challenge is identifying possible treatments for those infected and candidate vaccines for others. Though vaccines and other treatment research can take a decade or longer to develop, as Carolyn Johnson reports for the Washington Post, institutions have put significant resources into accelerating treatment development. The World Health Organization is coordinating over 70 countries as they research options while other organizations are testing dozens of drugs. Data and artificial intelligence has been applied to this work, with organizations creating open, machine-readable datasets to support research.

3. Identifying Availability of Supplies: With global shortages of masks and personal protective equipment, many communities lack necessary supplies to protect health workers. In the most affected communities, hospitals have run out of available beds and ventilators for sick patients, leading to difficult choices about whose treatment to prioritize. Amid increased demand, organizations need to find relevant supplies and deliver them to the appropriate authorities. Some data science efforts, such as in South Korea, have bridged this gap, identifying where supplies are and who needs them.

4. Monitoring Adherence to Non-Pharmaceutical Interventions: As institutions try to limit disease spread, many people have been asked to quarantine, observe social distancing practices, or otherwise limit their daily interactions with others. These restrictions can be tough to follow, not just due to muddled public messaging from some authorities, but because of the economic and psychological burdens they impose. Globally, some organizations have tried to determine whether people are abiding by guidelines outlined by health authorities through the use of call detail records, observation of social media, and other approaches.

Social and Political Needs

5. Understanding Public Perceptions and Behavior: There is limited research on how people feel about their situation and how coronavirus-related confinement is affecting human behavior. Amid reports of increased episodes of anxiety and depression and domestic abuse, additional information could be useful for helping institutions take meaningful action to protect those at risk of harm. It could also be useful in developing services for people to call for help and addressing the likely disparate impact the crisis will have on women. Already, some governments have begun to circulate data-driven apps and resources to support those in challenging situations.

6. Protecting Human Rights and Promoting Accountability: Governments need to expend extraordinary resources to contain this crisis, as the World Health Organization has stated, but they still need to be transparent and accountable to those that they serve. Indeed, organizations need meaningful oversight to ensure they allocate resources efficiently, competently, and according to need. Oversight can also ensure the crisis is not used to advance authoritarianism, promote digital surveillance, or excuse privacy or human rights violations. Data could address these issues by ensuring aid is delivered to those that need it or by demonstrating how governments can protect lives without infringing on the rights of the people they serve.

7. Addressing Misinformation: The crisis has also spurred renewed concern over the spread of false or misleading information. Stories about fake cures and government action have appeared in the media — propagated by trolls, opportunists, and the misinformed. More problematically, some political actors have delivered muddled, misleading, or false information about the pandemic that could harm efforts to contain it. Data could help organizations understand where these stories are coming from, disprove them in real-time, or understand their effects. Access to data can also accelerate data journalism — as we have seen in a variety of media outlets such as the New York Times.

8. Supporting Post-Pandemic Re-openings and Recovery: Once the infection rate slows, lockdowns will likely be lifted and businesses, schools, public places, and other institutions will begin to reopen. This work will likely be challenging, requiring careful sequencing to prevent another outbreak and to limit further economic and social damage. Public reporting suggests governments are beginning to think about what to do after the pandemic peaks and are using data to guide these efforts, including when they should be implemented, how they should approach the problem, and ways they can be better prepared in the future.

Economic Needs

9. Alleviating Pandemic-related Unemployment and Poverty: Government-enforced lockdowns and restrictions have caused millions to lose their sources of income permanently or temporarily. In March, the International Labour Organization estimated 25 million jobs could be lost due to the pandemic. In early April, an unprecedented 6.6 million US residents applied for unemployment and 41 percent of all US residents reported having to dip into their savings to cope with the crisis. To minimize economic hardship, some organizations have sought to use data to identify the most vulnerable communities and what aid can offer the most effective support.

10. Guaranteeing Protections for Workers: Many organizations are also concerned with ensuring that those still employed are safe, healthy, and protected. Many of the individuals employed in essential industries are low-wage workers with few protections. They have few options if they or a family member gets sick. Meanwhile, students and others working remotely face the increased strain that comes from unpaid domestic labor, increased stress, inadequate accommodations for teleworking, and their normal duties. Data-driven projects could address these issues by providing aid to essential workers or increasing access to support services for workers generally.

11. Supporting Education and Upskilling: Students and teachers have been seriously affected by worldwide closures of schools, libraries, universities, and other educational facilities. While many systems around the world have tried to shift to remote learning, this shift has exposed large inequities among students and school systems. Similarly there are various calls to leverage the lock-down to retrain and upskill workers. Widely varied experiences and outcomes have also raised questions about whether students, of all ages, are meeting academic and training goals amid quarantine. New data sources could be harnessed to study these effects.

12. Fostering Business and Government Solvency: There is also a pressing need to ensure businesses and governments can survive protracted shutdowns to avoid further unemployment and poverty. Already, reporting suggests that many independent restaurants and businesses might be permanently shuttered. Local governments expect enormous declines in revenue while large companies are turning to mass layoffs. National leaders have enacted measures such as loan programs to try and limit this damage, but further measures might be needed as the crisis deepens. Data on which businesses are most at risk could be useful in designing policies.

Next Steps

Since the early days of the pandemic, there has been enormous interest among data-driven organizations to act in response to it. As many countries enter their second month under quarantine, it is essential these efforts are responsible, comprehensible, and targeted. Though challenging, projects must also use high quality data, account for potential biases in analysis and representation, and seek input from those affected by them. Poorly designed projects that use low quality data or do not abide by data protections risk misinforming or harming those they intend to help; or fuel a data divide.

The above topic map is not likely to be fully comprehensive As stated at the outset, we are not public health experts and our topic map relies mostly on public, English-language reporting. We also recognize that the different areas identified here may differ in priority depending on what stage of the pandemic communities find themselves in. Nevertheless, we hope it can spur new ideas about ways how data can address COVID-19 and which data assets might need to be made available.

We look forward to your feedback and invite you to share comments above (or contact stefaan@thegovlab.org). With your input, we can map the priority insights needed to respond to current and future pandemics. Through responsible data collaboration, we can develop policies needed to save lives today and in the future.

The authors would like to thank Andrew Young and Alexandra Shaw, both from The GovLab, for their research support.

About the authors:

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Andrew J. Zahuranec is Research Fellow at The GovLab @NYU, where he is responsible for studying how advances in science and technology can improve governance.

Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of The GovLab, where he is responsible for building a research foundation on how to transform governance using advances in science and technology.

This is the blog for Data & Policy (cambridge.org/dap), a peer-reviewed open access journal exploring the interface of data science and governance. Read on for five ways to contribute to Data & Policy.

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Data & Policy Blog

This is the blog for Data & Policy (cambridge.org/dap).

Data & Policy Blog

Written by

Blog for Data & Policy, an open access journal dedicated to data science and governance (cambridge.org/dap). Published by CUP. Edited by Roy Cobby (KCL).

Data & Policy Blog

This is the blog for Data & Policy (cambridge.org/dap), an open access journal for the impact of data science on governance. Editors-in-Chief: Zeynep Engin (UCL, Data for Policy), Jon Crowcroft (Cambridge, Turing Institute), Stefaan Verhulst (GovLab, NYU). Published by CUP.

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