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Objectives. To understand what levels of herd immunity are required in the COVID-19 pandemic, given spatial population heterogeneity, to best inform policy and action. Methods. Using a network of counties in the United States connected by transit data we considered a set of coupled differential equations for susceptible-infectious-removed populations. We calculated the classical herd immunity level plus a version reflecting the heterogeneity of connections in the network by running the model forward in time until the epidemic completed. Results. Necessary levels of herd immunity vary greatly from county to county. A population weighted average for the United States is 47.5% compared to a classically estimated level of 77.1%. Conclusions. Common thinking argues that the nation needs to achieve at least 60% herd immunity to emerge from the COVID-19 pandemic. Heterogeneity in contact structure and individual variation in infectivity, susceptibility, and resistance are key factors that reduce the disease-induced herd immunity levels to 34.2-47.5% in our models. Looking forward toward vaccination strategies, these results suggest we should consider not just who is vaccinated but where those vaccinations will do the most good.
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10.1101/2020.10.05.20207100
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Updating Herd Immunity Models for the U.S. in 2020: Implications for the COVID-19 Response
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