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Modeling and Predicting the Spread of Covid-19: Comparative Results for the United States, the Philippines, and South Africa (English)

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.


  • Author

    Dasgupta,Susmita, Wheeler,David R.

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  • Document Type

    Policy Research Working Paper

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  • Country

    United States,


    South Africa,

  • Region

    Rest Of The World, East Asia and Pacific, Africa East,

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

  • Keywords

    development research group; infection rate; spatial distribution of population; law of large number; use of travel time; infection data; public health intervention; likelihood of infection; Population Density; disaster risk management; public health policy; degrees of freedom; demand computer; Epidemic; model of growth; emerging infectious disease; movement of person; transmission of infection; animal population growth; exploratory data analysis; gravity model; regression results; spatial autocorrelation; econometric estimation; random selection; available data; infection dynamic; future research; resident population; infection transmission; infected person; public source; first stage; parameter estimate; natural disaster; Technology Diffusion; recent studies; parameter value; predicted change; statistical model; measurement error; human mobility; simulation model; research community; environmental science; open access; routing machine; collaborative work; present exercise; rural transportation infrastructure; good information; multiple sources; development policy; Time of Use; road information; regression model; econometric result; Road Networks; interregional trade; population movement; poultry science; national university; dynamic process; population migration; empirical study; population model; data requirement; contingency planning; geographic domains; computational demands; random error; high probability; composite measure; model result; time t; data selection; demographic determinants; population mobility; measurement standard; technological change; forecast period; digital divide; pandemic spread; research strategy; recent experiences; empirical research; model prediction; estimation bias; econometric model; life expectancy; health condition; travel restriction; comparative analysis; statistical significance; human interaction; new infections; historical data; temperature range; subsequent section; sustainable city; data availability; geographic range; Research Support; estimation result; random sample; epidemic prediction; disease characteristics; percent change; trade theory; development study; small sample; Municipalities



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Dasgupta,Susmita Wheeler,David R.

Modeling and Predicting the Spread of Covid-19: Comparative Results for the United States, the Philippines, and South Africa (English). Policy Research working paper,no. WPS 9419,COVID-19 (Coronavirus) Washington, D.C. : World Bank Group.