COVID-19 data model quantifies region-specific impact of social distancing orders

Now, a study published in the Annals of Internal Medicine by researchers from the College of Engineering and School of Medicine and Public Health at the University of Wisconsin–Madison quantifies the region-specific impact of social distancing measures on the COVID-19 caseload in three distinct areas: New York City, the Milwaukee metropolitan area, and Dane County in Wisconsin.

The research team was led by Oguzhan Alagoz, PhD, Proctor and Gamble-Bascom Professor of industrial and systems engineering, and Nasia Safdar, MD, PhD, professor of medicine and UW Health medical director of infection control. The team also included School of Medicine and Public Health researchers Ajay Sethi, PhD, MPH, professor of population health sciences, Brian Patterson, MD, MPH, assistant professor of emergency medicine, and Matthew Churpek, MD, PhD, MPH, associate professor of medicine.

Using aggregated cell phone mobility data as a way to track how people complied with social distancing policies, the team created a computational model to simulate COVID-19 cases based on when social distancing directives were implemented and eased, as well as how diligently people adhered to those orders.

The simulation shows that social distancing measures wielded major influence on case numbers, though the impact varied markedly in different areas, even within the same state.

According to their model, the timing of implementing social distancing measures was particularly crucial in New York, where the state restricted mass gatherings March 12 and introduced increasingly stringent measures over the following 10 days.

However, according to the researchers’ modeling, if the state had acted one week earlier, the number of cases in New York City would have been 80% less (41,366 instead of 203,261) by the end of May. Conversely, a week’s delay would have increased the caseload nearly seven times to more than 1.4 million.

The impact of the timing wasn’t as dramatic in Dane County, where a one-week delay would have increased its number of cases 36% by the end of July.

“Everybody knows, qualitatively, social distancing measures have made a difference, but I think this is one of the most accurate estimates of how much of a change they really led to,” said Alagoz.

“In places where you have high population density and a lot of movement in and out of the area, the impact of social distancing is significantly greater, compared to other places. Wisconsin, for example, implemented the same social distancing measures statewide, but the impact was different in Dane County, Milwaukee and other areas. Our model actually is able to tell us this quantitative estimate of how much of a difference we are going to see from one region to another.”

The model also takes into account each region’s demographics, infections imported from outside the area, asymptomatic transmission, age-specific adherence to social distancing rules, and limited availability of testing in the early months of the pandemic. The researchers note that a confluence of those factors drives infection rates in different areas, which demonstrates the need for region-specific modeling and policies.

“Context matters for prevention activities and region-specific data, and the expected effect of mitigation is invaluable to get buy-in and engage communities in mitigation efforts,” said Safdar.

Interestingly, the group’s model showed that the timing of lifting social distancing orders wasn’t nearly as consequential—provided the area’s residents largely maintained their vigilance or, in the model’s terms, only allowed their adherence to social distancing to drop by 5%. If, for example, social distancing measures in New York City had lifted a week earlier—June 1 instead of June 8—the number of cases would have increased by 6,738 by the end of July.

“The fate of this pandemic is in our hands,” said Alagoz. “As a community, it depends on how we respond. It just depends on how people behave.”