Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data

IF 13.7 1区 生物学 Q1 BIOLOGY
Gerardo Chowell , Pavel Skums
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引用次数: 0

Abstract

The integration of viral genomic data into public health surveillance has revolutionized our ability to track and forecast infectious disease dynamics. This review addresses two critical aspects of infectious disease forecasting and monitoring: the methodological workflow for epidemic forecasting and the transformative role of molecular surveillance. We first present a detailed approach for validating epidemic models, emphasizing an iterative workflow that utilizes ordinary differential equation (ODE)-based models to investigate and forecast disease dynamics. We recommend a more structured approach to model validation, systematically addressing key stages such as model calibration, assessment of structural and practical parameter identifiability, and effective uncertainty propagation in forecasts. Furthermore, we underscore the importance of incorporating multiple data streams by applying both simulated and real epidemiological data from the COVID-19 pandemic to produce more reliable forecasts with quantified uncertainty. Additionally, we emphasize the pivotal role of viral genomic data in tracking transmission dynamics and pathogen evolution. By leveraging advanced computational tools such as Bayesian phylogenetics and phylodynamics, researchers can more accurately estimate transmission clusters and reconstruct outbreak histories, thereby improving data-driven modeling and forecasting and informing targeted public health interventions. Finally, we discuss the transformative potential of integrating molecular epidemiology with mathematical modeling to complement and enhance epidemic forecasting and optimize public health strategies.
利用流行病学和分子监测数据调查和预测传染病动态。
将病毒基因组数据纳入公共卫生监测已彻底改变了我们跟踪和预测传染病动态的能力。本综述探讨了传染病预测和监测的两个关键方面:流行病预测的方法流程和分子监测的变革性作用。我们首先介绍了验证流行病模型的详细方法,强调了利用基于常微分方程(ODE)的模型来研究和预测疾病动态的迭代工作流程。我们建议采用更有条理的方法进行模型验证,系统地解决一些关键阶段的问题,如模型校准、结构和实际参数可识别性评估以及预测中不确定性的有效传播。此外,我们还强调了通过应用 COVID-19 大流行的模拟和真实流行病学数据来结合多种数据流的重要性,从而生成具有量化不确定性的更可靠预测。此外,我们还强调了病毒基因组数据在追踪传播动态和病原体进化方面的关键作用。通过利用贝叶斯系统发生学和系统动力学等先进的计算工具,研究人员可以更准确地估计传播集群和重建疫情历史,从而改进数据驱动的建模和预测,并为有针对性的公共卫生干预措施提供信息。最后,我们讨论了将分子流行病学与数学建模相结合的变革潜力,以补充和加强流行病预测,优化公共卫生战略。
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
Physics of Life Reviews
Physics of Life Reviews 生物-生物物理
CiteScore
20.30
自引率
14.50%
发文量
52
审稿时长
8 days
期刊介绍: Physics of Life Reviews, published quarterly, is an international journal dedicated to review articles on the physics of living systems, complex phenomena in biological systems, and related fields including artificial life, robotics, mathematical bio-semiotics, and artificial intelligent systems. Serving as a unifying force across disciplines, the journal explores living systems comprehensively—from molecules to populations, genetics to mind, and artificial systems modeling these phenomena. Inviting reviews from actively engaged researchers, the journal seeks broad, critical, and accessible contributions that address recent progress and sometimes controversial accounts in the field.
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