| Autor: |
Junyuan Yang, Sijin Wu, Xuezhi Li, Xiaoyan Wang, Xue-Song Zhang, Lu Hou |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Infectious Disease Modelling, Vol 9, Iss 3, Pp 975-994 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2468-0427 |
| DOI: |
10.1016/j.idm.2024.05.004 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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