Parameter identifiability of a within-host SARS-CoV-2 epidemic model  

作　　者：Junyuan Yang Sijin Wu Xuezhi Li Xiaoyan Wang Xue-Song Zhang Lu Hou 

机构地区：[1]Complex Systems Research Center,Shanxi University,Taiyuan,030006,China [2]Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention,Shanxi University,Taiyuan,030006,China [3]School of Mathematics and Science,Henan Normal University,Xinxiang,453000,China [4]School of Information,Shanxi University of Finance and Economics,Taiyuan,030006,China [5]Agriculture and Animal Husbandry Technology Promotion Center of Xingan League,Xingan League,137400,China

出　　处：《Infectious Disease Modelling》2024年第3期975-994,共20页传染病建模（英文）

基　　金：This work is partially supported by Humanities and Social Foundation of Ministry of Education of China(22YJAZH129);the National Natural Science Foundation of China(No.12271143,No.61573016);the Shanxi Province Science Foundation(No.20210302123454);Shanxi Scholarship Council of China(2023–024).

摘　　要：Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable datasets.Furthermore,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters.Lastly,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.

关 键 词：Structural identifiability Practical identifiability Sensitivity analysis The basic reproduction number 

分 类 号：R373[医药卫生—病原生物学]