非线性混合模型和传染病建模中的相关方法:系统和批判性审查
Olaiya Mathilde Adéoti1, Schadrac Agbla2, Aliou Diop3
1Laboratoire de Biomathématiques et d'Estimations Forestières, University of Abomey-Calavi, Cotonou, Benin.
Infectious Disease Modelling
|October 8, 2024
概括
非线性混合效应模型 (NLMMs) 提供了一种灵活的方法来分析传染病动态,特别是在异质数据的情况下. 本次审查强调了它们在模拟COVID-19等流行病中的日益重要和应用.
科学领域:
- 流行病学和生物统计学
- 公共卫生中的数学建模
背景情况:
- 传染病监测和准备在全球范围内有所不同,因此需要灵活的统计模型来准确分析疫情.
- 传统模型往往缺乏在流行病研究中常见的异质,不平衡数据所需的适应性.
研究的目的:
- 在传染病建模 (IDM) 中提供非线性混合效应模型 (NLMMs) 的全面概述.
- 建立一个发展指南的基础,以改善NLMMs的现实世界实施.
主要方法:
- 根据PRISMA指南进行了系统审查.
- 在过去二十年中,搜索了Research4life Access倡议计划中的科学数据库,寻找IDM中NLMM的论文.
- 包括最初3641篇中的124篇论文,重点关注NLMM应用的关键方面.
主要成果:
- 在IDM中,NLMM已经出现了快速的演变和越来越多的采用,特别是在2017-2021年期间.
- 该研究发现,从正常性假设转移,重点关注NLMM适应非正常错误和随机效应.
- 在最近的流行病 (COVID-19,埃博拉,登革热,拉萨) 中,NLMM越来越多地被应用,在放松的正常性假设下展示了强度.
结论:
- 在处理复杂数据方面,NLMM是传染病建模的强大而灵活的工具,在处理复杂数据方面表现优于一些隔间模型.
- 在IDM中有效应用NLMM的关键考虑因素包括估计方法,假设选择和随机术语规范.
- 这些发现支持NLMM的常规使用,以进行可靠的流行病分析和预测,特别是在不同的全球环境中.
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