Open AccessTransmission dynamics and high infectiousness of Coronavirus Disease 2019
Author(s) -
Shunxiang Huang,
Lin Wu,
Jing Li,
Ming-Zhen Xin,
Yingying Wang,
Xingjie Hao,
Zhongyi Wang,
Qihong Deng,
Bin-Guo Wang
Publication year - 2023
Publication title -
communications on pure and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.077
H-Index - 42
eISSN - 1553-5258
pISSN - 1534-0392
DOI - 10.3934/cpaa.2021155
Subject(s) - transmission (telecommunications) , pandemic , basic reproduction number , covid-19 , asymptomatic , demography , reproduction , disease , biology , medicine , geography , computer science , environmental health , infectious disease (medical specialty) , population , ecology , telecommunications , sociology
Coronavirus disease 2019 (COVID-19) has rapidly spread around the world since the early 2020. Recently, a second wave of COVID-19 has resurged in many countries. The transmission dynamics and infectiousness of the COVID-19 pandemic remain unclear, and developing strategies to mitigate the severity of the pandemic is a top priority for global public health. According to the infection mechanism of COVID-19, a novel susceptible-asymptomatic-symptomatic-recovered (SASR) model with control variables in a patchy environment was proposed not only to consider the key characteristics of asymptomatic infection and the effects of seasonal variation but also to incorporate different control measures for multiple transmission routes. The basic reproduction number \begin{document}$ R_{0} $\end{document} was established to describe the spreading behavior in the natural state over a long time horizon, and the natural reproduction number \begin{document}$ R_{n} $\end{document} , which describes the development trend of the disease during a short time in the future, was defined according to the actual propagation characteristics. In addition, the effective reproduction number \begin{document}$ R_{e} $\end{document} considering the control strategies was proposed to evaluate the impact of non-pharmaceutical interventions. The results of numerical simulations for COVID-19 cases in Wuhan, China, based on the SASR model indicate that \begin{document}$ R_{0} $\end{document} was 3.58, \begin{document}$ R_{n} $\end{document} ranged from 2.37 to 4.91, and \begin{document}$ R_{e} $\end{document} decreased gradually from 4.83 on December 8, 2019 to 0.31 on March 8, 2020, reaching 1.40 on January 23, 2020, when the lockdown was lifted in Wuhan. We further concluded that the total number of infections, including asymptomatic infections, was approximately 301, 804 as of March 8, 2020, in Wuhan, China. In particular, this article proposes a dynamic method to distinguish the impact of natural factors and human interventions on the development of the pandemic, and provides a theoretical basis for fighting the global COVID-19 pandemic.