Modeling and Predicting the Spread of Covid-19

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Tác giả: Susmita Dasgupta

Ngôn ngữ: eng

Ký hiệu phân loại: 362.19 Services to patients with specific conditions

Thông tin xuất bản: World Bank, Washington, DC, 2020

Mô tả vật lý:

Bộ sưu tập: Tài liệu truy cập mở

ID: 320079

 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.
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