Open AccessThreshold dynamics of a stochastic SIHR epidemic model of COVID-19 with general population-size dependent contact rate
Author(s) -
Tianfang Hou,
AUTHOR_ID,
Guijie Lan,
Sanling Yuan,
Tonghua Zhang,
AUTHOR_ID
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022195
Subject(s) - sensitivity (control systems) , transmission rate , basic reproduction number , extinction (optical mineralogy) , covid-19 , transmission (telecommunications) , mathematics , epidemic model , statistics , noise (video) , population , statistical physics , biology , physics , disease , demography , computer science , medicine , infectious disease (medical specialty) , engineering , telecommunications , electronic engineering , artificial intelligence , sociology , image (mathematics) , paleontology , pathology
In this paper, we propose a stochastic SIHR epidemic model of COVID-19. A basic reproduction number $ R_{0}^{s} $ is defined to determine the extinction or persistence of the disease. If $ R_{0}^{s} < 1 $, the disease will be extinct. If $ R_{0}^{s} > 1 $, the disease will be strongly stochastically permanent. Based on realistic parameters of COVID-19, we numerically analyze the effect of key parameters such as transmission rate, confirmation rate and noise intensity on the dynamics of disease transmission and obtain sensitivity indices of some parameters on $ R_{0}^{s} $ by sensitivity analysis. It is found that: 1) The threshold level of deterministic model is overestimated in case of neglecting the effect of environmental noise; 2) The decrease of transmission rate and the increase of confirmed rate are beneficial to control the spread of COVID-19. Moreover, our sensitivity analysis indicates that the parameters $ \beta $, $ \sigma $ and $ \delta $ have significantly effects on $ R_0^s $.