As the world faces the most profound and deeply disruptive public health crisis in modern history—COVID-19—our scientific research communities are navigating a series of unknowns against a complex backdrop where responsible data science is more important than ever, write four leading University of California, Berkeley experts in a recent paper.
Published in the June 2020 Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, "The Road for Recovery: Aligning COVID-19 efforts and building a more resilient future" describes how careful use of past, current, and emerging data can help move from responding to the pandemic to long-term recovery.
"The rising demand for data and the emerging efforts to responsibly collect, share, and analyze information across traditional boundaries play a vital role in our next steps," wrote Meredith M. Lee, Alicia D. Johnson, Katherine A. Yelick, and Jennifer T. Chayes, all of UC Berkeley.
Lee is the Executive Director of the West Big Data Innovation Hub headquartered on campus; Johnson is Director of the university's Office of Emergency Management; Yelick is a Professor of Computer Science and Associate Dean for Research for the Division of Computing, Data Science, and Society (CDSS); and Chayes is Associate Provost for the CDSS.
Since the paper was published, there has been a dramatic surge in cases across the country, a problem that has demonstrated the critical need for coordinated planning and action. “As the pandemic extends, it compounds the strain and damage to our communities,” said Lee, who led the White House Innovation for Disaster Response & Recovery Initiative during President Obama’s second term. “We must work together across neighborhoods, cities, regions, and the nation to consistently take informed steps forward.”
Data lead the way toward virus management and treatment
Acknowledging that the battle is being waged in an ever-changing landscape, the authors write that emerging research in data analysis and modeling that bridges statistical methods, machine learning, and artificial intelligence with policy development and risk communication are helping hospitals address medical supply needs, identifying and correcting COVID-related death rates, and noting problems in current tracking methods.
"Such efforts can help communities visualize the nature of fluid events and iteratively explore reasonable response strategies when faced with unprecedented scenarios," they write, especially "as communities across the globe adjust their behavior against a backdrop of changing policies, guidance, and tactics."
Data are also helping advance the search for medical treatments by identifying drug candidates that may be used against COVID-19, as well as sharing research results through open online reports rather than relying on traditional, longer-range publication schedules. Helping clinical groups work together in new ways may help improve diagnostics, vaccine development, and outcomes.
But progress cannot come at all costs, the authors note. Privacy must be incorporated into the design of data collection and sharing, such as in sharing existing data and developing new contact tracing approaches.
The Whole Story: Recognizing missing data and the impacts
Analysis of data on the spread and effects of COVID-19, combined with demographic information, has shown disproportionate impacts on Black, Indigenous, Latinx, and other marginalized groups. Research has shown how current approaches to measure the spread of the disease can obscure rather than illuminate inequities. "For example, communities with less access to testing for SARS-CoV-2 will have fewer diagnosed cases, making the epidemic look less severe," write the authors. "This can lead to disparities in attention and funding, and distort algorithms meant to help."
The authors also warn that the effects of the pandemic are likely to exacerbate the impacts of other disasters such as wildfires, hurricanes, earthquakes, or tornadoes. Local emergency response agencies typically face one crisis at a time, and the effects of a natural disaster on a community hit hard by the pandemic could make it even harder to recover.
"Local economies strained by COVID-19 create an inability for communities to truly prepare and protect themselves from all impending hazards," the authors write. "Communities unable to protect themselves may suffer additional strain and damage that makes recovery from the compounded disaster even further from reach."
Another danger comes from the growing amount of questionable information surrounding the pandemic. "In a world where the opportunity for technology-driven situational awareness during a crisis is strikingly juxtaposed with an 'infodemic' of extensive misinformation and disinformation, we are starving for transparent, appropriately vetted, and curated information in context."
"While our community will surely grapple with unanticipated complexities over the next weeks, months, and years, we have a call-to-action in this moment and hope for building a more resilient future," the authors conclude. "The future needs us to learn from the past and present—to deploy an inherently human-centered approach that connects research with the realities of crisis management, long-term recovery, and the vulnerabilities of being human."
About the Division of Computing, Data Science, and Society
The Division of Computing, Data Science, and Society launched in July 2019 to leverage Berkeley’s preeminence in research and excellence across disciplines to propel data science discovery, education, and impact. Core to the Division is an understanding of how the digital revolution affects equality, equity, and opportunity—and the capacity to respond to related challenges.
The Division’s dynamic structure connects Data Science Education, the School of Information; the departments of Electrical Engineering and Computer Sciences and Statistics; and includes the Berkeley Institute for Data Science, the Data Science Commons, and the West Big Data Innovation Hub. It’s designed to meet the opportunities and demands of a world increasingly informed and shaped by data, machine learning, and artificial intelligence in virtually every arena, from health to business to politics; from our cities to our climate to the cosmos.