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Design of cost-effective COVID-19 surveillance

Abstract

COVID-19 will be circulating in the United States for many months to come. However, many states are still relying on biased data and/or downstream indicators to inform their reopening or mitigation decisions. Modelers have not quantified the public health impact of investment in surveillance leading to improved model forecasts and hence decision-making. Using Illinois as a test case, we propose to integrate mathematical models of SARS-CoV2 transmission with costing models of sentinel surveillance to estimate the health impacts of each dollar spent on surveillance. Sentinel surveillance of newly symptomatic cases is being piloted in Illinois, providing an opportunity to assess costs throughout implementation. The successful completion of this research will link SARS-CoV2 transmission models with cost estimates and responsive decision-making to identify the most cost-effective sentinel surveillance programs and provide a quantitative basis for policy. This framework can form the basis of further modeling of health and economic impacts of COVID-19 policies.