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Studies Show Social Distancing Worked

Public Health Researchers Quantify Impact on Infections, Provide Model to Predict Scope of Future Disease Outbreaks

By Kelly Blake

People social distancing in line

Photo by iStock

The University of Maryland's COVID-19 Analytic Research Group found that social distancing measures helped prevent infections in March to May 2020 in five states, which at the time had half of the nation's cases.

From outset of the COVID-19 pandemic, public health authorities called for social distancing as one of the main tools to fight infection, with varying levels of compliance. A pair of recent publications from a University of Maryland team formed to track the disease and assess strategies to combat it shows that when recommendations were followed, the tactic worked.

The first paper, published in Public Health Reports, examined how compliance with social distancing influenced the pandemic’s spread in California, Illinois, Massachusetts, New Jersey and New York, which had 50% of the overall caseload from March to May 2020. 

The team, known as the COVID-19 Analytic Research Group, measured compliance with social distancing using the Maryland Transportation Institute’s (MTI) social distancing index, which tracks mobility via cell phone data to gauge adherence with mandates, comparing it with information on the virus’s daily reproduction number and daily growth rate. 

It found that social distancing measures helped prevent infections in all the states—but then adherence to social distancing declined beginning in April 2020, with premature lifting of stay-at-home orders, opening of businesses and a growing wave of what the MTI labeled “pandemic fatigue.” 

“Our study shows that social distancing works and can reduce the epidemic scope,” said Professor Hongjie Liu, chair of the Department of Epidemiology and Biostatistics and COVID-19 Analytic Research Group leader. “But it is determined by the level of compliance with social distancing requirements. Once the level of compliance was reduced, we saw a rebound in the infection rate.”  

The second paper, led by Raul Cruz-Cano, associate research professor of epidemiology and biostatistics, describes a model the group developed to forecast Maryland's COVID-19 daily caseload based on social distancing behaviors and the state’s number of cases. Published in Disaster Medicine and Public Health Preparedness, it could help guide responses in other disease outbreaks.

Using the MTI’s social distancing index data and the number of new daily cases from The New York Times, the group developed a “time series” model that effectively predicted Maryland’s daily caseload nine days later and reinforced the need for social distancing to minimize COVID-19 cases. 

“Seeing the behavior from seven to nine days ago and the number of cases, we could accurately predict the number of future COVID-19 cases in one to two weeks,” said Cruz-Cano. “Knowing what is going to happen means that you can be better prepared with staff and resources to manage the cases.”

Although their model was optimized for the epidemic in Maryland, the researchers said the model can be applied in other regions and for other outbreaks. The findings from both papers can help guide future conversations about how quickly to reopen the economy versus controlling disease spread, the researchers said, as well as inform governments and public health authorities about how to best implement and communicate prevention measures in this and future pandemics. 



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School of Public Health

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