Junyuan Yang , Sijin Wu , Xuezhi Li , Xiaoyan Wang , Xue-Song Zhang , Lu Hou
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引用次数: 0
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.
期刊介绍：
Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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