Epidemic Dynamics of COVID-19 Based on SEAIUHR Model Considering Asymptomatic Cases in Henan Province, China

Author:

Li Chunyu1,Zhu Yuchen1,Qi Chang1,Liu Lili1,Zhang Dandan1,Wang Xu1,She Kaili1,Jia Yan1,Liu Tingxuan1,Xiong Momiao2,Li Xiujun1ORCID

Affiliation:

1. Shandong University

2. University of Texas Health Science Center at San Antonio

Abstract

Abstract Background New coronavirus disease (COVID-19), an infectious disease caused by a type of novel coronavirus, has emerged in various countries since the end of 2019 and caused a global pandemic. Many infected people went undetected because their symptoms were mild or asymptomatic, but the proportion and infectivity of asymptomatic infections remained unknown. Therefore, in this paper, we analyzed the proportion and infectivity of asymptomatic cases, as we as the prevalence of COVID-19 in Henan province. Methods We constructed SEAIUHR model based on COVID-19 cases reported from 21 January to 26 February 2020 in Henan province to estimate the proportion and infectivity of asymptomatic cases, as we as the change of effective reproductive number, \({R}_{t}\). At the same time, we simulated the changes of cases in different scenarios by changing the time and intensity of the implementation of prevention and control measures. Results The proportion of asymptomatic cases among COVID-19 infected individuals was 42% and infectivity of asymptomatic cases was 10% of that symptomatic ones. The basic reproductive number\({R}_{0}\)=2.73, and \({R}_{t}\) dropped below 1 on 1 February under a series of measures. If measures were taken five days earlier, the number of cases would be reduced by 2/3, and after 5 days the number would more than triple. Conclusions In Henan Province, the COVID-19 epidemic spread rapidly in the early stage, and there were a large number of asymptomatic infected individuals with relatively low infectivity. However, the epidemic was quickly brought under control with national measures, and the earlier measures were implemented, the better.

Publisher

Research Square Platform LLC

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