Real-time syndromic surveillance and modeling to inform decision-making for COVID-19 Investigators: Shweta Bansal, Associate Professor, Department of Biology, Georgetown University Pej 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 unprecedented public health emergency. It is now clearly appreciated that smart policy responses to this pandemic 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 in permitting a rational comparison of alternative non-pharmaceutical intervention strategies. To fill this urgent surveillance gap and inform policy decisions, we propose to model the spatio-temporal dynamics of COVID-19 in the US from novel streams of real-time healthcare data. Our combination of sophisticated computational and statistical models, together with unique high-resolution data will allow a careful characterization of the burden of 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
The rapid spread of COVID-19 around the United States has created an unprecedented public health emergency. To fill the urgent surveillance gap and inform policy decisions, we propose to model the spatio-temporal dynamics of COVID-19 in the US from novel streams of real-time healthcare data. Our findings will help characterize the burden of disease, discriminate among alternative mitigation policies, and quantify the geographic variation in population immunity as we prepare for the Fall wave.
