Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic

IF 8.8 3区医学 Q1 Medicine

Infectious Disease Modelling Pub Date : 2024-12-03 DOI:10.1016/j.idm.2024.12.001

Wei-Hua Hu , Hui-Min Sun , Yong-Yue Wei , Yuan-Tao Hao

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引用次数: 0

Abstract

An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe. Unlike previous review studies, this review encompasses newly developed approaches such as the combined model and Hawkes model after the COVID-19 pandemic, providing a thorough evaluation of their current application status and development prospects for the first time. These models not only rely on conventional surveillance data but also incorporate information from various sources. We aim to provide valuable insights for enhancing global infectious disease surveillance and early warning systems, as well as informing future research in this field, by summarizing the underlying modeling concepts, algorithms, and application scenarios of each model.

查看原文本刊更多论文

全球传染病早期预警模型：最新回顾和2019冠状病毒病大流行的教训。

传染病早期预警模型是及时监测、预防和控制疾病暴发的重要工具。利用大数据和人工智能技术整合各种多源数据已成为推进这些预警模型的关键途径。本文对全球广泛使用的传染病预警模型进行了综述。与以往的综述研究不同，本次综述纳入了新冠肺炎大流行后新发展的联合模型、Hawkes模型等方法，首次对其应用现状和发展前景进行了全面评价。这些模型不仅依赖于传统的监测数据，而且还纳入了各种来源的信息。我们的目标是通过总结每个模型的基本建模概念、算法和应用场景，为加强全球传染病监测和预警系统提供有价值的见解，并为该领域的未来研究提供信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.