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...
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World Bank, Washington, DC
2020
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| 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 |
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okr-10986-345902022-09-20T00:11:48Z Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa Dasgupta, Susmita Wheeler, David CORONAVIRUS COVID-19 PANDEMIC INFECTION DATA EPIDEMIC SPREAD EPIDEMIC PREDICTION GRAVITY MODEL GOMPERTZ GROWTH MODEL HOTSPOTS 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. 2020-10-08T13:24:18Z 2020-10-08T13:24:18Z 2020-10 Working Paper 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 English Policy Research Working Paper; No. 9419 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper East Asia and Pacific |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
CORONAVIRUS COVID-19 PANDEMIC INFECTION DATA EPIDEMIC SPREAD EPIDEMIC PREDICTION GRAVITY MODEL GOMPERTZ GROWTH MODEL HOTSPOTS |
| spellingShingle |
CORONAVIRUS COVID-19 PANDEMIC INFECTION DATA EPIDEMIC SPREAD EPIDEMIC PREDICTION GRAVITY MODEL GOMPERTZ GROWTH MODEL HOTSPOTS Dasgupta, Susmita Wheeler, David Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| geographic_facet |
East Asia and Pacific |
| relation |
Policy Research Working Paper; No. 9419 |
| description |
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. |
| format |
Working Paper |
| author |
Dasgupta, Susmita Wheeler, David |
| author_facet |
Dasgupta, Susmita Wheeler, David |
| author_sort |
Dasgupta, Susmita |
| title |
Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| title_short |
Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| title_full |
Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| title_fullStr |
Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| title_full_unstemmed |
Modeling and Predicting the Spread of Covid-19 : Comparative Results for the United States, the Philippines, and South Africa |
| title_sort |
modeling and predicting the spread of covid-19 : comparative results for the united states, the philippines, and south africa |
| publisher |
World Bank, Washington, DC |
| publishDate |
2020 |
| url |
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 |
| _version_ |
1764481226665623552 |