建模知情的政策，通过建模评估的政策：社会和经济背景下数学流行病学的演变
Modeling-informed policy, policy evaluated by modeling: Evolution of mathematical epidemiology in the context of society and economy
摘要：
2019冠状病毒病（COVID-19）大流行自二零二零年初起席卷全球，令公众及媒体对数学流行病学领域的关注达到前所未有的水平。自从该疾病引起全世界的关注以来，已经提出了许多具有不同复杂程度的模型;其中许多模型试图预测不同时间尺度上的疾病过程。其他模式研究了为遏制和抗击这一疾病而采取的各种政策措施（包括前所未有地使用“封锁”）的效力。模型的多样性可能导致许多人对数学建模的真正能力和效用感到困惑。在这里，我们提供了一个流行病学建模的简要指南，重点是它如何成为一个工具，为知情的公共卫生政策制定，并反过来影响了干预措施的设计，旨在防止疾病暴发变成肆虐的流行病。我们表明，模型的多样性是有点虚幻的，因为它们中的大部分都植根于我们在这里描述的房室建模框架。虽然他们的基本结构似乎是一个高度理想化的描述工作中的过程，我们表明，功能，提供更多的现实主义，如社区组织的人口或战略决策的个人，可以纳入这样的模型。我们得出结论认为，模型的真正价值在于它们能够在计算机上测试流行病过程中不同政策选择的后果，这是一种比试错法优越得多的方法，试错法在生命和社会经济破坏方面代价高昂。
英文摘要：
The COronaVIrus Disease 2019 (COVID-19) pandemic that has had the world in its grip from the beginning of 2020, has resulted in an unprecedented level of public interest and media attention on the field of mathematical epidemiology. Ever since the disease came to worldwide attention, numerous models with varying levels of sophistication have been proposed; many of these have tried to predict the course of the disease over different time-scales. Other models have examined the efficacy of various policy measures that have been adopted (including the unparalleled use of "lockdowns") to contain and combat the disease. This multiplicity of models may have led to bewilderment in many quarters about the true capabilities and utility of mathematical modeling. Here we provide a brief guide to epidemiological modeling, focusing on how it has emerged as a tool for informed public-health policy-making and has in turn, influenced the design of interventions aimed at preventing disease outbreaks from turning into raging epidemics. We show that the diversity of models is somewhat illusory, as the bulk of them are rooted in the compartmental modeling framework that we describe here. While their basic structure may appear to be a highly idealized description of the processes at work, we show that features that provide more realism, such as the community organization of populations or strategic decision-making by individuals, can be incorporated into such models. We conclude with the argument that the true value of models lies in their ability to test in silico the consequences of different policy choices in the course of an epidemic, a much superior alternative to trial-and-error approaches that are highly costly in terms of both lives and socio-economic disruption.
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