期刊
出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2020524118
关键词
stochastic COVID-19 spread modeling; spatial epidemiology; neighborhood disparities; human mobility; data assimilation
资金
- NSF [DMS1700884, BCS2027375]
- National Geospatial Fellowship for Advancing COVID-19 Research Education [OAC1743184]
- Data Science Initiative
- University of Wisconsin-Madison Office of the Chancellor
- Wisconsin Alumni Research Foundation
The COVID-19 pandemic poses global threats with varied impacts on different communities due to population heterogeneity in terms of factors such as race and age. Studies have shown significant differences in transmission patterns based on geographic and demographic factors, highlighting the importance of considering these heterogeneities in policy making for managing and reopening during the pandemic.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据