Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race
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
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发表时间：
2021-06-15
影响因子：
11.1
通讯作者：
Patz, Jonathan A.
中科院分区：
文献类型：
--
作者：
Hou, Xiao;Gao, Song;Patz, Jonathan A.
COVID-19大流行是一个全球性威胁，对健康、经济及社会带来持续升级的挑战。集合种群流行病建模研究在为公共卫生政策制定提供信息以减缓COVID-19传播方面发挥了重要作用。这些模型通常依赖于对总体同质性的关键假设。这种假设当然不能指望在真实的情况下成立;不同的地理、社会经济和文化环境会影响推动COVID-19在不同社区传播的行为。此外，由于县域内环境的差异，造成了不同地区传播的空间异质性。为了解决这个问题，我们开发了一个人类流动流增强的随机SEIR风格的流行病建模框架，能够区分不同的区域及其相应的行为。然后，该建模框架与数据同化和机器学习技术相结合，重建了威斯康星州两个县的COVID-19确诊病例的历史增长轨迹。然后研究COVID-19传播与商业客流量、种族和民族以及年龄结构之间的关系。结果表明，在大学城（戴恩县），最重要的异质性是年龄结构，而在大城市地区（密尔沃基县），种族和民族异质性变得更加明显。情景研究进一步表明，传播率对各种重新开放政策的强烈反应，这表明政策制定者在设计减缓COVID-19持续传播和重新开放的政策时可能需要非常谨慎地考虑这些异质性。
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.