wearing

Dr. Biplav Srivastava, professor of computer science at the University of South Carolina, and his team have developed a data-driven tool that helps demonstrate the effect of wearing masks on COVID-19 cases and deaths. His model utilizes a variety of data sources to create alternate scenarios that can tell us “What could have happened?” if a county in the U.S. had a higher or lower rate of mask adherence. In this interview, he explains how the model works, its limitations and what conclusions we can draw from it. Computer scientist Biplav Srivastava provides a demo of the simulation to show that earlier policies to recommend mask-wearing make a bigger difference on the spread of the coronavirus. What does this computer model do? This is a nationwide tool which can show the effect that weari...

With the advent of an infectious disease outbreak, epidemiologists and public health officials quickly try to forecast deaths and infections using complex computer models. But with a brand-new virus such as the one that causes COVID-19, these estimates are complicated by a dearth of credible information on symptoms, contagion, and those who are most at risk. My team at the Goldenson Center for Actuarial Research has developed a free, user-friendly computer model with a different goal. It demonstrates how infections and deaths progress daily over a three-month period depending on how people behave in the outbreak. This model allows the public to input data that demonstrate how changes in safety measures in their communities, including wearing face covering and social distancing, can signif...

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Wearing Masks Saves Lives, The Earlier You Start The Better, A New Data-Driven Model Shows

Yes, Keep Wearing Masks. Here’s Why