Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
57 Pages Posted: 6 May 2020 Last revised: 30 Nov 2020
Date Written: May 18, 2020
Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. We study a spatial epidemic model, which explicitly accounts for population movements, and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We show that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline non-teleworkable employment in NYC. By contrast, uniform (city-wide) restriction policies that achieve the same policy goal permit 3.92 to 6.25 times less non-teleworkable employment. Our targeting framework gives policy makers an approach for curbing the spread of epidemics while limiting unemployment.
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