A major collaborative data science effort now underway at the University of Wisconsin–Madison to aid understanding and response to COVID-19 began with an email from a yoga instructor.
Brian Yandell, horticulture professor and interim director of the American Family Insurance Data Science Institute at UW–Madison, says his yoga instructor reached out to ask him about an article he’d read about the sneaky exponential growth of cases of the disease in other parts of the world. It highlighted the ways in which mathematical modeling could help inform policymakers and the general public about the pandemic.
Inspired, Yandell began to look for data. He started developing an app to assess COVID-19 case projections in the Midwest and — after an impromptu conversation with his neighbor, Mary Bottari, chief of staff to Madison’s mayor — he realized that to gather enough data to create meaningful models of the disease he was going to need more help.
So, Yandell reached out to researchers at UW Health, the School of Medicine and Public Health, and others on campus, such as Michael Ferris, director of the Data Science Hub at the Wisconsin Institute for Discovery. He contacted Ajay Sethi, associate professor of population health sciences, and Malia Jones, assistant scientist at the Applied Population Laboratory, along with several colleagues at the College of Engineering. His list of connections grew longer every day.
Within three weeks, Yandell had heard from more than 100 people throughout the U.S. who are now participating in various ways. The COVID-19 Data Science Research Group works under a charter focusing them in three areas: interpreting data, using that data to create models, and sharing information and findings.
Early modeling results from the research coalition show that the speed of viral transmission has slowed since Wisconsin Governor Tony Evers issued the first “Safer at Home” executive order on March 25. The results demonstrate that, in the absence of other options, social distancing (sometimes referred to as physical distancing) is necessary to stop the spread of COVID-19.
“While the human costs of COVID-19 are clear, so are the steps we must take to protect our families, neighbors and community,” wrote team member Jonathan Patz, director of the Global Health Institute, in a recent op-ed. “Physical distancing must be our top priority to stop new cases of COVID-19 from overwhelming our health care system.”
Information, not assumptions
The Safer at Home order was recently extended until May 26, but Patz, Sethi and Yandell stress that physical distancing will continue to be necessary until broad-scale testing and contact tracing becomes available.
“We need an overflow of information from testing,” Yandell says. “The data we get from testing can then be used to refine our models, rather than making assumptions.”
The data science research group was recently asked by the State Emergency Operations Center and the Wisconsin Department of Health Services to assist in developing and implementing a contact tracing data system, which could be used to help inform relaxation of physical distancing policies.
Ferris says data on COVID-19 is quickly evolving, with increasing detail from counties and from city and regional levels. Epidemiology and statistical models take this kind of data and extrapolate it out to arrive at future predictions in a process called calibration.
No predictive models are perfect, but with adequate data and fine-tuned mathematical parameters, they can be useful tools for helping anticipate the future. Or, as Yandell likes to point out, it was George Box, founder of the UW–Madison Statistics Department, who said: “All models are wrong, but some models are useful.”
The right resources
With calibration, researchers can add new drivers — such as an updated number of cases, or changes to the availability of ventilators at regional hospitals — to enhance the fit of the data and improve predictions. Major drivers of COVID-19 spread include lack of physical distancing and asymptomatic disease transmission, Ferris says.
“We want these models to be effective and help decision-makers and the general public understand the evolution of this system and how we can use interventions to affect that evolution,” Ferris says.
Among the team’s goals is to help ensure the right resources are available in the right places at the right times, he adds. And researchers are using data visualization tools to track infectious disease trends.
Incomplete or missing data limit modeling efforts for response. This includes a lack of adequate testing and unanticipated changes to resource availability.
“We have to continually refine our models to reflect changes in the supply chain, such as when new nurses and doctors may become available,” Ferris says.
It also includes limits to the types of health data that are available, due to the privacy considerations and laws that guard some kinds of health information.
“This isn’t a perfectly defined procedure, but modeling is iterative,” Ferris says. “We collect data, build a model informed by that data, and run that model to make some inferences about how we would change that system. Then, we rerun them until we are confident in our model and can suggest action based upon it.”
Each time, he adds, “we grow more confident in our conclusions.”
Opportunities to brainstorm
The research group has added several data dashboards and ongoing efforts to a website and shares information as appropriate and necessary.
Several members of the COVID-19 data science group also participate in ongoing discussion over the messaging platform Slack. It was started by Mikhail Kats, associate professor of electrical and computer engineering, and provides UW–Madison faculty, staff and students opportunities to brainstorm and to apply their skills and resources to the pandemic.
Discussions have included data mining, data visualization, and building data repositories to evaluate the spread of COVID-19 in Wisconsin. They’ve also looked at new methods to disinfect vehicles, design personal protective equipment (PPE), and identify existing drugs that may be used to treat COVID-19.
One member of the Slack group, Lennon Rodgers, director of the Grainger Engineering Design Innovation Lab, is working with Madison-area manufacturers, a design consulting firm, and campus colleagues to help meet the urgent demand for producing medical face shields — key PPE for health care workers treating coronavirus patients.
Yandell says he now sees his role as “the traffic cop,” making connections and providing space for others to do their work. For instance, he connected Song Gao, assistant professor of geography, to a researcher at the University of Chicago and together they are examining aggregated cellphone data to understand people’s movement across the United States.
Yandell learned of the Chicago research team, led by health geographer Marynia Kolak, through another one of his connections: his brother-in-law, Carmi Neiger, a geography professor at Elmhurst College.
“I’d rather be here in calm times,” Yandell says. “I think that is true for all of us. But this is what I — what we — need to be doing now, together, to stop this pandemic and minimize its impacts.”
This story was originally published on the UW–Madison News site.