The importance to society of this timely interdisciplinary topic, approached using state-of-the-art computational physics techniques combined with epidemiological modeling concepts, should make our study of interest to the broad scientific community as it works to combat the ongoing pandemic. We also find that abruptly removing the stay-at-home order would have likely led to a significant second wave of infection, whereas a gradual return would delay the second wave and reduce its peak. Our analysis shows that the stay-at-home order and social distancing were crucial in “flattening the curve,” reducing hospital occupancy and deaths by at least an order of magnitude. We use Markov chain Monte Carlo statistical analysis techniques to take into account epidemiological estimates of model parameters such as delays between times of infection, hospitalization, and death. The novelty of our approach is that our model is calibrated against multiple time-dependent data streams, and we provide a detailed analysis of the predictive power of epidemiological modeling. We provide an accurate quantitative description of the COVID-19 epidemic dynamics capturing both government-imposed mitigation and scenarios for its eventual release. Here, we report on such an analysis, performed in real time during the progression of the epidemic. Any responsible return to normalcy must be informed by realistic epidemiological modeling not only of the resulting increased death toll but also of the stress placed upon the healthcare system. Recently, parts of the state have begun to see hints of a resurgence of the disease. Roughly two months later, the order was lifted. To combat the spread of COVID-19, the infectious disease caused by the novel coronavirus SARS-CoV-2, the governor of Illinois issued a stay-at-home order for the entire state on March 21, 2020. 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. 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. Without prior information on nonpharmaceutical interventions, the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. 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. 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. 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. Bayesian estimation of model parameters is carried out using Markov chain Monte Carlo methods. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. 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.
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