Modeling and Predicting the Spread of Covid-19: Comparative Results for the United States, thePhilippines, and South Africa

A model of Covid-19 transmission among locations within a country has been developed that is(1) implementable anywhere spatially-disaggregated Covid-19 infection data are available; (2) scalable for locations ofdifferent sizes, from individual regions to countries of continental scale; (3) reliant solely on data that are freeand open to public access; (4) grounded in a rigorous, proven methodology; and (5) capable of forecasting futurehotspots with enough accuracy to provide useful alerts. Applications to the United States, the Philippines, andSouth Africa's Western Cape province demonstrate the model's usefulness. The model variables includeindicators of interactions among infected residents, locally and at a greater distance, with infection dynamics capturedby a Gompertz growth model. The model results for all three countries suggest that local infection growth is affected bythe scale of infections in relatively distant places. Forecasts of hotspots 14 and 28 days in advance, using onlyinformation available on the first day of the forecast, indicate an imperfect but nonetheless informativeidentification of actual hotspots.