Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Xiao Hou, Song Gao (), Qin Li, Yuhao Kang, Nan Chen, Kaiping Chen, Jinmeng Rao, Jordan S. Ellenberg and Jonathan A. Patz
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Xiao Hou: Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706
Song Gao: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, WI 53706
Qin Li: Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706
Yuhao Kang: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, WI 53706
Nan Chen: Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706
Kaiping Chen: Department of Life Sciences Communication, University of Wisconsin–Madison, Madison, WI 53706
Jinmeng Rao: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, WI 53706
Jordan S. Ellenberg: Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706
Jonathan A. Patz: School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI 53706

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 24, e2020524118

Abstract: The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Keywords: stochastic COVID-19 spread modeling; spatial epidemiology; neighborhood disparities; human mobility; data assimilation (search for similar items in EconPapers)
Date: 2021
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