Parameter identifiability of a within-host SARS-CoV-2 epidemic model
Junyuan Yang1,2, Sijin Wu2, Xuezhi Li3
1Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Abstract:
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.
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