Why is it difficult to accurately predict the COVID-19 epidemic?
Why is it difficult to accurately predict the COVID-19 epidemic?
复制标题
DOI：
10.1016/j.idm.2020.03.001
复制
发表时间：
2020-01-01
影响因子：
8.8
通讯作者：
Li, Michael Y.
中科院分区：
文献类型：
--
作者：
Roda, Weston C.;Varughese, Marie B.;Li, Michael Y.
自2019年12月武汉市爆发COVID-19疫情以来，已报告了许多关于武汉及中国其他地区COVID-19疫情的模型预测。这些模型的预测显示出各种各样的变化。在我们的研究中，我们证明了使用确认的情况下的数据模型校准的不可识别性是这种广泛的变化的主要原因。使用赤池信息准则（AIC）的模型选择，我们表明，SIR模型比SEIR模型表现得更好，在表示确认的情况下，数据中包含的信息。这表明，使用更复杂的模型进行预测可能并不比使用更简单的模型更可靠。我们提出我们对武汉市于2020年1月23日封锁和隔离后的COVID-19疫情的模型预测。我们还报告了对2月7日之后该市采取的严格隔离措施对疫情时间进程的影响进行建模的结果，并对该市复工后第二次疫情爆发的可能性进行建模。(c)2020年，任作家。Elsevier B. V.代表KeAi Communications Co.制作和主持，这是一个在CC BY-NC-ND许可证下的开放获取文章（http：//creativecommons.org/licenses/by-nc-nd/4.0/）。
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. (c) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).