We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a stay-at-home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov chain Monte Carlo methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its subregions in order to account for the wide disparities in population size and density. Without prior information on nonpharmaceutical interventions, the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. Forward predictions of the model provide robust estimates of the peak position and severity and also enable forecasting the regional-dependent results of releasing stay-at-home orders. The resulting highly constrained narrative of the epidemic is able to provide estimates of its unseen progression and inform scenarios for sustainable monitoring and control of the epidemic.

中文翻译：

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在非药物干预下模拟伊利诺伊州COVID-19动力学

我们介绍了在美国伊利诺伊州COVID-19流行病的建模情况，记录了在家中用餐的实施情况以及最终发布的情况。我们使用非马尔可夫感染年龄模型，该模型能够处理较长且可变的时间延迟，而无需更改其模型拓扑。使用马尔可夫链蒙特卡洛方法进行模型参数的贝叶斯估计。该框架使我们能够以统一的方式处理所有可用的输入信息，包括先前发布的流行病参数和可用的本地数据。为了准确地模拟死亡和医疗保健系统的需求，我们针对COVID-19患者的总和住院死亡以及医院和ICU病床占用率，对我们的预测进行了校准。为了解决人口规模和密度的巨大差异，我们不仅将这种模型应用于整个州，还将其应用于子地区。在没有有关非药物干预措施的事先信息的情况下，该模型会独立再现与Google和Unacast报告的流动性数据非常匹配的缓解趋势。该模型的前瞻性预测提供了对峰值位置和严重性的可靠估计，并且还可以预测释放居家订单的区域相关结果。由此产生的高度受限的流行病叙述能够提供其未见进展的估计，并为可持续监测和控制流行病提供参考。该模型独立地再现了缓解趋势，该趋势与Google和Unacast报告的移动数据非常匹配。该模型的前瞻性预测提供了对峰值位置和严重性的可靠估计，并且还可以预测释放居家订单的区域相关结果。由此产生的高度受限的流行病叙述能够提供其未见进展的估计，并为可持续监测和控制流行病提供参考。该模型独立地再现了缓解趋势，该趋势与Google和Unacast报告的移动数据非常匹配。该模型的前瞻性预测提供了对峰值位置和严重性的可靠估计，并且还可以预测释放居家订单的区域相关结果。由此产生的高度受限的流行病叙述能够提供其未见进展的估计，并为可持续监测和控制流行病提供参考。