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适应性SIR模型的反问题:在拉丁美洲新冠疫情中的应用

Inverse problem for adaptive SIR model: Application to COVID-19 in Latin America.

作者信息

Marinov Tchavdar T, Marinova Rossitza S

机构信息

Department of Natural Sciences, Southern University at New Orleans, New Orleans, LA, 70126, USA.

Department of Mathematical and Physical Sciences, Concordia University of Edmonton, Edmonton, AB, T5B 4E4, Canada.

出版信息

Infect Dis Model. 2022 Mar;7(1):134-148. doi: 10.1016/j.idm.2021.12.001. Epub 2021 Dec 16.

Abstract

This work presents a method for solving an Adaptive Susceptible-Infected-Removed (A-SIR) epidemic model with time-dependent transmission and removal rates. Available COVID-19 data as of March 2021 are used for identifying the rates from an inverse problem. The estimated rates are used to solve the adaptive SIR system for the spread of the infectious disease. This method simultaneously solves the problem for the time-dependent rates and the unknown functions of the A-SIR system. Presented results show the spread of COVID-19 in the World, Argentina, Brazil, Colombia, Dominican Republic, and Honduras. Comparisons of the reported affected by the disease individuals from the available real data and the values obtained with the A-SIR model demonstrate how well the model simulates the dynamic of the infectious disease.

摘要

这项工作提出了一种用于求解具有随时间变化的传播率和清除率的自适应易感-感染-康复(A-SIR)传染病模型的方法。截至2021年3月的可用新冠病毒病(COVID-19)数据被用于从一个反问题中识别这些速率。所估计的速率被用于求解传染病传播的自适应SIR系统。该方法同时解决了随时间变化的速率问题以及A-SIR系统中的未知函数问题。给出的结果展示了新冠病毒病在世界、阿根廷、巴西、哥伦比亚、多米尼加共和国和洪都拉斯的传播情况。将现有实际数据中报告的受该疾病影响的个体数量与通过A-SIR模型获得的值进行比较,证明了该模型对传染病动态的模拟效果如何。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c017/8713124/4c07195c9f96/gr1.jpg

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