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

Abstract

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.

Keywords: COVID-19; Compartmental model; Epidemic model; Public health intervention; Reproduction number.

Fig. 1

Global trend chart of confirmed cases and deaths of COVID-19.

Fig. 2

Comparison of R0 in models based on data of different areas.

Fig. 3

Comparison of R0 in models based on data of China (including Hubei and Wuhan).

Fig. 4

Definition of key time periods in different COVID-19 epidemic models.

Fig. 5

Comparison of the value of time period (VTP) in different models.

Fig. 6

Assumed and estimated values of asymptomatic infection ratio in different models.

Fig. 7

Distribution of different studies on the impact of public health interventions a) Increase quarantine rate (Chen and Yu, 2020, Ferretti et al., 2020, Hauser et al., 2020, Hellewell et al., 2020, Hu et al., 2020, Kissler et al., 2020, Omori et al., 2020, Tang et al., 2020, Verity et al., 2020, Wang and Liu, 2020); b) Improve reporting rate (Anastassopoulou et al., 2020, Kucharski et al., 2020, Li et al., 2020, Liu et al., 2020b, Liu et al., 2020c, Zhao et al., 2020); c)e) Quarantine (Chinazzi & Davis, 2020, Ferretti et al., 2020, Hauser et al., 2020, Hellewell et al., 2020, Hou et al., 2020, Hu et al., 2020, Kissler et al., 2020, Koo et al., 2020, Kuniya, 2020, Liu et al., 2020c, Maier and Brockmann, 2020, Mandal et al., 2020, Munayco et al., 2020, Muniz-Rodriguez et al., 2020, Ngonghala et al., 2020, Sanche et al., 2020, Tian et al., 2020); d) Contact tracing (Choi and Ki, 2020, Ferretti et al., 2020, Hellewell et al., 2020, Maier and Brockmann, 2020, Munayco et al., 2020, Ngonghala et al., 2020, Tang et al., 2020); f) Travel restrictions (Boldog et al., 2020, Chinazzi & Davis, 2020, Kraemer et al., 2020, Kucharski et al., 2020, Tang et al., 2020, Tang et al., 2020, Tian et al., 2020, Yang et al., 2020, Zhu and Chen, 2020); g) Mask protection (Eikenberry et al., 2020, Kai and Guy-PhilippeGoldstein, 2020, Ngonghala et al., 2020); h) Other/integrated interventions (Acuna-Zegarra et al., 2020, Choi and Ki, 2020, Eikenberry et al., 2020, Fanelli and Piazza, 2020, Hauser et al., 2020, Hellewell et al., 2020, Hou et al., 2020, Hu et al., 2020, Koo et al., 2020, Kuniya, 2020, Liu et al., 2020a, Maier and Brockmann, 2020, Mandal et al., 2020, Ngonghala et al., 2020, Sanche et al., 2020, Wang and Liu, 2020, Yang et al., 2020, Zhang et al., 2020, Zhao et al., 2020).

Fig. 8

Comparison of R0 before and after implementation of public health interventions in different studies.