Real-time syndromic surveillance and modeling to inform decision-making for COVID-19

Real-time syndromic surveillance and modeling to inform decision-making forCOVID-19 Investigators:Shweta Bansal, Associate Professor, Department of Biology, Georgetown UniversityPej Rohani, Professor, Odum School of Ecology & Dept of Infectious Diseases, University of Georgia Project Summary:The rapid spread of COVID-19 around the United States has created an unprecedentedpublic health emergency. It is now clearly appreciated that smart policy responses to thispandemic require the utilization of reliable, validated transmission models. Models are critical both in terms of forecasting the spatio-temporal spread of the virus, but also inpermitting a rational comparison of alternative non-pharmaceutical intervention strategies.To fill this urgent surveillance gap and inform policy decisions, we propose to model thespatio-temporal dynamics of COVID-19 in the US from novel streams of real-timehealthcare data. Our combination of sophisticated computational and statistical models,together with unique high-resolution data will allow a careful characterization of the burdenof COVID-19 beyond tested cases, discriminate among alternative mitigation policies, and quantify the geographic variation in population immunity as we prepare for the Fall wave.1