论文标题
建模的政策，通过建模评估的政策：在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
论文作者
论文摘要
从2020年初开始抓住世界的2019年冠状病毒病（Covid-19）大流行，引起了前所未有的公共利益和媒体对数学流行病学领域的关注。自从该疾病引起全球关注以来，已经提出了许多具有不同水平的模型。其中许多试图通过不同的时间量表来预测疾病的过程。其他模型研究了已采用的各种政策措施的功效（包括无与伦比的使用“锁定”）来遏制和应对疾病。模型的这种多重性可能导致许多方面关于数学建模的真正能力和实用性的困惑。在这里，我们为流行病学建模提供了简短的指南，重点是它如何作为知情的公共卫生政策制定的工具，并影响了旨在防止疾病爆发变成愤怒的流行病的干预措施的设计。我们表明，模型的多样性有些虚幻，因为它们的大部分植根于我们在这里描述的隔离建模框架。尽管它们的基本结构似乎是对工作过程的高度理想化的描述，但我们表明，可以将提供更多现实主义的特征（例如人口社区组织或个人的战略决策）纳入此类模型中。我们以这样的论点结论：模型的真实价值在于他们在硅中测试不同政策选择的后果的能力，在流行病的过程中，这是反复试验方法的一种优越的替代方法，在生活和社会经济上的破坏方面，这是高昂代价的。
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.
