COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models

• Published in: Infectious disease modelling Vol. 6; pp. 324 - 342
• Main Authors: Xiang, Yue, Jia, Yonghong, Chen, Linlin, Guo, Lei, Shu, Bizhen, Long, Enshen
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01-01-2021; KeAi Publishing; KeAi Communications Co., Ltd
• Subjects: Compartmental model; COVID-19; Epidemic model; Public health intervention; Reproduction number; Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu; COVID-19; Public health intervention; Compartmental model; Epidemic model; Reproduction number
• ISSN: 2468-0427; 2468-2152; 2468-0427
• DOI: 10.1016/j.idm.2021.01.001

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.