Xiang Yue, Jia Yonghong, Chen Linlin, Guo Lei, Shu Bizhen, Long Enshen
MOE Key Laboratory of Deep Earth Science and Engineering, Institute of Disaster Management and Reconstruction, Sichuan University, Chengdu, China.
Chongqing Safety Engineering Institute, Chongqing University of Science and Technology, Chongqing, China.
Infect Dis Model. 2021;6:324-342. doi: 10.1016/j.idm.2021.01.001. Epub 2021 Jan 7.
The coronavirus disease outbreak of 2019 (COVID-19) has been spreading rapidly to all corners of the word, in a very complex manner. A key research focus is in predicting the development trend of COVID-19 scientifically through mathematical modelling. We conducted a systematic review of epidemic prediction models of COVID-19 and the public health intervention strategies by searching the Web of Science database. 55 studies of the COVID-19 epidemic model were reviewed systematically. It was found that the COVID-19 epidemic models were different in the model type, acquisition method, hypothesis and distribution of key input parameters. Most studies used the gamma distribution to describe the key time period of COVID-19 infection, and some studies used the lognormal distribution, the Erlang distribution, and the Weibull distribution. The setting ranges of the incubation period, serial interval, infectious period and generation time were 4.9-7 days, 4.41-8.4 days, 2.3-10 days and 4.4-7.5 days, respectively, and more than half of the incubation periods were set to 5.1 or 5.2 days. Most models assumed that the latent period was consistent with the incubation period. Some models assumed that asymptomatic infections were infectious or pre-symptomatic transmission was possible, which overestimated the value of R0. For the prediction differences under different public health strategies, the most significant effect was in travel restrictions. There were different studies on the impact of contact tracking and social isolation, but it was considered that improving the quarantine rate and reporting rate, and the use of protective face mask were essential for epidemic prevention and control. The input epidemiological parameters of the prediction models had significant differences in the prediction of the severity of the epidemic spread. Therefore, prevention and control institutions should be cautious when formulating public health strategies by based on the prediction results of mathematical models.
2019年冠状病毒病（COVID-19）疫情正以非常复杂的方式迅速蔓延至世界各地。一个关键的研究重点是通过数学建模科学地预测COVID-19的发展趋势。我们通过检索Web of Science数据库，对COVID-19的疫情预测模型及公共卫生干预策略进行了系统综述。系统综述了55项关于COVID-19疫情模型的研究。发现COVID-19疫情模型在模型类型、获取方法、关键输入参数的假设和分布方面存在差异。大多数研究使用伽马分布来描述COVID-19感染的关键时间段，一些研究使用对数正态分布、爱尔朗分布和威布尔分布。潜伏期、传播间隔、传染期和代间距的设定范围分别为4.9 - 7天、4.41 - 8.4天、2.3 - 10天和4.4 - 7.5天，超过一半的潜伏期设定为5.1天或5.2天。大多数模型假设潜伏期与 incubation period一致。一些模型假设无症状感染具有传染性或存在症状前传播的可能性，这高估了R0值。对于不同公共卫生策略下的预测差异，旅行限制的影响最为显著。关于接触者追踪和社会隔离的影响有不同研究，但认为提高检疫率和报告率以及使用防护口罩对疫情防控至关重要。预测模型的输入流行病学参数在疫情传播严重程度的预测上存在显著差异。因此，防控机构在基于数学模型的预测结果制定公共卫生策略时应谨慎。
