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

Author

Listed:
  • Dasgupta,Susmita
  • Wheeler,David R.

Abstract

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.

Suggested Citation

  • Dasgupta,Susmita & Wheeler,David R., 2020. "Modeling and Predicting the Spread of Covid-19: Comparative Results for the United States, thePhilippines, and South Africa," Policy Research Working Paper Series 9419, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9419
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    References listed on IDEAS

    as
    1. Susmita Dasgupta & Somik Lall & David Wheeler, 2005. "Policy Reform, Economic Growth and the Digital Divide," Oxford Development Studies, Taylor & Francis Journals, vol. 33(2), pages 229-243.
    2. Walter Isard & Merton J. Peck, 1954. "Location Theory and International and Interregional Trade Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 68(1), pages 97-114.
    3. George J. Borjas, 2020. "Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods," NBER Working Papers 26952, National Bureau of Economic Research, Inc.
    4. Shima Hamidi & Sadegh Sabouri & Reid Ewing, 2020. "Does Density Aggravate the COVID-19 Pandemic?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(4), pages 495-509, October.
    5. James Truscott & Neil M Ferguson, 2012. "Evaluating the Adequacy of Gravity Models as a Description of Human Mobility for Epidemic Modelling," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    6. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    7. Walter Isard, 1954. "Location Theory and Trade Theory: Short-Run Analysis," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 68(2), pages 305-320.
    Full references (including those not matched with items on IDEAS)

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