气候变化领域集成服务门户(CCPortal): Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

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DOI	10.1073/pnas.2020524118
	Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race
	Hou X.; Gao S.; Li Q.; Kang Y.; Chen N.; Chen K.; Rao J.; Ellenberg J.S.; Patz J.A.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:24
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Data assimilation; Human mobility; Neighborhood disparities; Spatial epidemiology; Stochastic COVID-19 spread modeling
语种	英语
scopus关键词	biological model; city; epidemiology; human; migration; pandemic; Wisconsin; Cities; COVID-19; Human Migration; Humans; Models, Biological; Pandemics; SARS-CoV-2; Wisconsin
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238894
作者单位	Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706, United States; Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, WI 53706, United States; Department of Life Sciences Communication, University of Wisconsin–Madison, Madison, WI 53706, United States; School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI 53706, United States
推荐引用方式 GB/T 7714	Hou X.,Gao S.,Li Q.,et al. Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race[J],2021,118(24).
APA	Hou X..,Gao S..,Li Q..,Kang Y..,Chen N..,...&Patz J.A..(2021).Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race.Proceedings of the National Academy of Sciences of the United States of America,118(24).
MLA	Hou X.,et al."Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race".Proceedings of the National Academy of Sciences of the United States of America 118.24(2021).