Roda Weston C, Varughese Marie B, Han Donglin, Li Michael Y
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1 Canada.
Analytics and Performance Reporting Branch, Alberta Health, Edmonton, Alberta, T5J 2N3, Canada.
Infect Dis Model. 2020;5:271-281. doi: 10.1016/j.idm.2020.03.001. Epub 2020 Mar 25.
Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
自2019年12月武汉市爆发新型冠状病毒肺炎疫情以来，已有大量关于武汉及中国其他地区新型冠状病毒肺炎疫情的模型预测报告。这些模型预测呈现出广泛的差异。在我们的研究中，我们证明了使用确诊病例数据进行模型校准时的不可识别性是造成如此广泛差异的主要原因。使用赤池信息准则（AIC）进行模型选择，我们表明在表示确诊病例数据中包含的信息方面，SIR模型比SEIR模型表现得更好。这表明与使用更简单的模型相比，使用更复杂的模型进行预测可能并不更可靠。我们给出了2020年1月23日武汉市实施封城和隔离措施后对新型冠状病毒肺炎疫情的模型预测。我们还报告了对2月7日后该市实施的严格隔离措施对疫情时间进程的影响进行建模的结果，以及对该市复工后二次爆发可能性进行建模的结果。
