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.

COVID-19 大流行凸显了疾病流行的拼凑性质，美国境内各个国家和各州的感染传播动态差异很大。为了探讨这个问题，我们研究并预测了 COVID-19 在密歇根州沃什特诺县的传播情况，该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，是密歇根州疫情的主要震中。我们应用基于离散和随机网络的建模框架，使我们能够跟踪该县的每个人。在此框架中，我们基于源自美国人口普查数据集的沃什特诺县特定的合成人口数据集构建联系网络。我们将个人分配到家庭、工作场所、学校和集体宿舍（例如监狱或长期护理机构）。此外，我们还为每个人随机分配临时联系人。使用这个框架，我们明确模拟了密歇根州特定的政府强制关闭的工作场所和学校以及社交距离措施。我们进行敏感性分析，以确定在密歇根州首次观察到 COVID-19 病例后三个月内造成观察到的疾病负担的关键模型参数和机制。然后，我们考虑放松限制和重新开放工作场所的几种情景，以预测哪些行动是最谨慎的。我们特别考虑了 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重新接触的影响。我们发现，推迟重新开放并不能减少第二次病例高峰的程度，而只是推迟了它。另一方面，减少随意接触的程度，既可以推迟并降低第二个峰值。通过模拟和敏感性分析，我们探索了重新开放后推动第二波感染的规模和时间的机制。我们发现最重要的因素是工作场所和偶然接触以及寻求治疗的感染者采取的保护措施。该模型可以使用综合人口数据库和特定于这些地区的数据来适应美国其他县。