Northwestern, UT use hospitalization data to create social distancing policy tool

Researchers at the University of Texas at Austin and Evanston, Ill.-based Northwestern University developed a data-driven framework to guide policymakers on when to scale up and relax social distancing measures.

The research team designed the framework so lawmakers can make social distancing policy decisions that both protect hospitals from becoming overwhelmed by COVID-19 patients and reduce the economic effects of the pandemic. The framework, which is detailed in a July 29 paper in the Proceedings of the National Academy of Sciences, uses two data models. One focuses on estimating how COVID-19 cases will spread and the other analyzes Austin hospital admissions data. 

“While many cities have implemented alert levels and new policies, our research may be the first to provide clear guidance for exactly what to track (hospital admissions data) and exactly when to act (strict thresholds),” David Morton, PhD, a Northwestern industrial engineering and management sciences professor who helped developed the tool, said in a news release. “Communities need to act long before hospital surges become dangerous. Hospital admissions data give an early indication of rapid pandemic growth, and tracking that data will ensure that hospitals maintain sufficient capacity.”

The researchers noted that drastic surges in COVID-19 cases can be prevented by imposing strict social distancing measures for vulnerable populations and that the framework can be used by local policymakers to create multistage triggers for moving through reopening stages.

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