Department of Biology, Stanford University, Stanford, CA, USA.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Lancet Digit Health. 2021 Jan;3(1):e41-e50. doi: 10.1016/S2589-7500(20)30268-5. Epub 2020 Dec 7.
The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.
当前的 COVID-19 大流行导致传染病动态传播模型以前所未有的方式被纳入政策制定和公共卫生实践中。这些模型提供了一种系统的方法来研究传播动态,并产生短期和长期预测,明确整合了影响疾病传播、负担和监测的生物学、行为和流行病学过程的假设。在 COVID-19 大流行和其他传染病爆发期间,模型是非常有价值的工具,能够生成疾病负担的可能轨迹,评估干预策略的有效性,并估计关键的传播变量。特别是考虑到在紧急情况下模型开发、评估和与决策整合的快速步伐,有必要了解传播模型的优势和缺陷。我们回顾和强调了传染病动态模型历史的关键方面、严格测试和评估的作用、与数据的整合以及模型在指导公共卫生方面的成功应用。本文不是传染病模型的广泛历史回顾,而是重点介绍如何通过政策和实践以适当和谨慎的方式整合建模,以继续推进模型的发展,从而支持当前的大流行应对。