Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, 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 of different sizes, from individual re...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/533861601575025228/Modeling-and-Predicting-the-Spread-of-Covid-19-Comparative-Results-for-the-United-States-the-Philippines-and-South-Africa http://hdl.handle.net/10986/34590 |
| Summary: | 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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