Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity
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- Loring J. Thomas
- Department of Sociology, University of California, Irvine, CA, 92697;
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- Peng Huang
- Department of Sociology, University of California, Irvine, CA, 92697;
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- Fan Yin
- Department of Statistics, University of California, Irvine, CA, 92697;
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- Xiaoshuang Iris Luo
- Department of Criminology, Law, and Society, University of California, Irvine, CA, 92697;
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- Zack W. Almquist
- Department of Sociology, Center for Studies in Demography and Ecology, Center for Statistics and Social Sciences, eScience, University of Washington, Seattle, WA, 98195;
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- John R. Hipp
- Department of Criminology, Law, and Society, University of California, Irvine, CA, 92697;
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- Carter T. Butts
- Department of Sociology, University of California, Irvine, CA, 92697;
抄録
<jats:title>Significance</jats:title> <jats:p>We examine the effects of an uneven population distribution on the spread of the COVID-19 disease spread, using a diffusion model based on interpersonal contact networks. Taking into account spatial heterogeneity, the spread of COVID-19 is much “burstier” than in standard epidemiological models, with substantial local disparities in timing and severity and long lags between local outbreaks. We show that spatial heterogeneity may produce dramatic differences in social exposures to those with the illness, and may stress health care delivery systems in ways that are not well captured by standard SIR-like models.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 117 (39), 24180-24187, 2020-09-10
Proceedings of the National Academy of Sciences