Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multiweek periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities. HighlightsO_LIContact heterogeneity and age-structured risk is represented in a metapopulation model. C_LIO_LIThe metapopulation model can be used in the near-term to make joint predictions on cases, hospitalizations, and fatalities at county levels. C_LIO_LIIn Georgia, a long-term plateauing epidemic, rather than a distinctive epidemic peak, is possible. C_LI