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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 of different sizes, from individual regions to countries of continental scale; (3) reliant solely on data that are free and open to public access; (4) grounded in a rigorous, proven methodology; and (5) capable of forecasting future hotspots with enough accuracy to provide useful alerts. Applications to the United States, the Philippines, and South Africa's Western Cape province demonstrate the model's usefulness. The model variables include indicators of interactions among infected residents, locally and at a greater distance, with infection dynamics captured by a Gompertz growth model. The model results for all three countries suggest that local infection growth is affected by the scale of infections in relatively distant places. Forecasts of hotspots 14 and 28 days in advance, using only information available on the first day of the forecast, indicate an imperfect but nonetheless informative identification of actual hotspots.

Coronavirus --- COVID-19 --- Disease Control and Prevention --- Epidemic Prediction --- Epidemic Spread --- Gompertz Growth Model --- Gravity Model --- Hotspots --- Infection Data --- Pandemic --- Population Sciences --- Public Health Promotion

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The World Health Organization attributes about 3.3 million annual premature deaths to outdoor air pollution in low- and middle-income countries. Comprehensive pollution monitoring in urban areas has been too costly for many developing countries; yet sparse information has hindered cost-effective pollution management strategies. Global information technologies offer a potential escape from this information trap, but their accuracy remains uncertain. This paper uses ground-based measures of fine particulates and nitrogen dioxide, provided by the CAMS-3 Darussalam monitoring station in Dhaka, Bangladesh, to test three global technologies: the European Space Agency's Sentinel-5P, the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, and Google Traffic. The results indicate that all three global technologies can provide useful information for extension of air pollution measurement beyond the few areas that are currently monitored by ground stations. Each technology tracks ground-based fine particulates measures with high significance, and the European Space Agency's Sentinel-5P and Google Traffic perform similarly for ground-based nitrogen dioxide measures. Google Traffic can provide accurate tracking at higher spatial and temporal resolution than the satellite sources, but only for emissions from motor vehicles in major metro areas. The Moderate Resolution Imaging Spectroradiometer and the European Space Agency's Sentinel-5P capture the effects of emissions from other sources at all locations.

Air Pollution --- Air Quality and Clean Air --- Brown Issues and Health --- Fine Particles --- Google Traffic --- Nitrogen Dioxide --- Pollution Control --- Pollution Management and Control --- Satellite Imagery --- Traffic Congestion --- Urban Environment

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