宿主内部 SARS-CoV-2 流行模型的参数可识别性

IF 8.8 3区医学 Q1 Medicine

Infectious Disease Modelling Pub Date : 2024-05-14 DOI:10.1016/j.idm.2024.05.004

Junyuan Yang , Sijin Wu , Xuezhi Li , Xiaoyan Wang , Xue-Song Zhang , Lu Hou

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引用次数: 0

摘要

参数识别是指根据观测数据和数学模型对系统中未披露的参数进行估计。在这项研究中，我们利用 DAISY，结合一系列可观测数据集，细致检验了宿主内 SARS-CoV-2 流行模型参数的结构可识别性。此外，还进行了蒙特卡罗模拟，对模型参数进行了全面的实际分析。最后，通过敏感性分析，确定降低 SARS-CoV-2 病毒的复制率和缩短感染期是缓解 COVID-19 在宿主间传播的最有效措施。

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

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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来源期刊

Infectious Disease Modelling Mathematics-Applied Mathematics

CiteScore

17.00

自引率

3.40%

发文量

审稿时长

17 weeks

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.