Predicting the second wave of COVID-19 in Washtenaw County, MI
Predicting the second wave of COVID-19 in Washtenaw County, MI
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DOI：
10.1016/j.jtbi.2020.110461
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发表时间：
2020-12-21
影响因子：
2
通讯作者：
Kirschner, Denise
中科院分区：
文献类型：
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作者：
Renardy, Marissa;Eisenberg, Marisa;Kirschner, Denise
COVID-19大流行凸显了疾病流行的拼凑性质，不同国家和美国各州之间的感染传播动态差异很大。为了探索这个问题，我们研究并预测了新冠肺炎在密歇根州沃什特诺县的传播，沃什特诺县是密歇根大学和东密歇根大学的所在地，并且靠近密歇根州底特律市，这是密歇根州疫情的主要震中。我们采用离散和随机的基于网络的建模框架，使我们能够跟踪该县的每一个人。在这个框架中，我们构建了基于合成人口数据集的联系网络，这些数据集来自美国人口普查数据集。我们将个人分配到家庭、工作场所、学校和集体宿舍（如监狱或长期护理设施）。此外，我们随机分配每个人的偶然接触。使用这个框架，我们明确模拟特定的政府授权的工作场所和学校关闭，以及社会距离的措施。我们进行了敏感性分析，以确定在密歇根州首次观察到COVID-19病例后三个月内对观察到的疾病负担有贡献的关键模型参数和机制。然后，我们考虑了几种放宽限制和重新开放工作场所的情况，以预测什么行动是最谨慎的。特别是，我们考虑了1）重新开放的不同时间，以及2）不同级别的工作场所与临时接触的影响。我们发现，推迟重开并不能降低第二个高峰的数量，而只是推迟了它;另一方面，减少偶然接触的水平，既推迟又降低了第二个高峰。通过模拟和敏感性分析，我们探索了重新开放后第二波感染的规模和时间的驱动机制。我们发现，最重要的因素是工作场所和临时接触以及寻求护理的感染者采取的保护措施。该模型可以使用合成人口数据库和特定于这些地区的数据来适应美国其他县。(C)2020爱思唯尔有限公司保留所有权利。
The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions. (C) 2020 Elsevier Ltd. All rights reserved.