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基于数学模型的 COVID-19 情景分析和干预措施综述。

A review of mathematical model-based scenario analysis and interventions for COVID-19.

机构信息

Department of Electrical Engineering, Qatar University, Qatar.

Department of Public Health, College of Health Sciences, QU Health, Qatar University, Qatar.

出版信息

Comput Methods Programs Biomed. 2021 Sep;209:106301. doi: 10.1016/j.cmpb.2021.106301. Epub 2021 Jul 27.

Abstract

Mathematical model-based analysis has proven its potential as a critical tool in the battle against COVID-19 by enabling better understanding of the disease transmission dynamics, deeper analysis of the cost-effectiveness of various scenarios, and more accurate forecast of the trends with and without interventions. However, due to the outpouring of information and disparity between reported mathematical models, there exists a need for a more concise and unified discussion pertaining to the mathematical modeling of COVID-19 to overcome related skepticism. Towards this goal, this paper presents a review of mathematical model-based scenario analysis and interventions for COVID-19 with the main objectives of (1) including a brief overview of the existing reviews on mathematical models, (2) providing an integrated framework to unify models, (3) investigating various mitigation strategies and model parameters that reflect the effect of interventions, (4) discussing different mathematical models used to conduct scenario-based analysis, and (5) surveying active control methods used to combat COVID-19.

摘要

基于数学模型的分析已被证明是抗击 COVID-19 的重要工具,通过它可以更好地了解疾病传播动态,更深入地分析各种场景的成本效益,更准确地预测干预和不干预情况下的趋势。然而,由于信息大量涌现以及报告的数学模型之间存在差异,因此需要更简洁、更统一地讨论 COVID-19 的数学建模,以克服相关的怀疑。为此,本文对 COVID-19 的基于数学模型的情景分析和干预措施进行了综述,主要目的是:(1)简要概述现有的数学模型综述,(2)提供一个集成框架来统一模型,(3)研究各种减轻策略和反映干预效果的模型参数,(4)讨论用于进行基于情景分析的不同数学模型,以及(5)调查用于抗击 COVID-19 的主动控制方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/218a/8314871/e8c94c997912/gr1_lrg.jpg

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