疫苗接种统计物理学
Statistical physics of vaccination
摘要：
从历史上看，传染病对人类社会造成了巨大的破坏，今天仍然如此。为了帮助减少其影响，研究了疾病传播的数学模型，以帮助了解疾病动态并为预防策略提供信息。疫苗接种-现代最重要的预防措施之一-在理论和经验上都具有极大的意义。与传统方法相反，最近的研究越来越多地探索个体行为和人群中异质接触模式的关键影响。我们的报告回顾了理论流行病学的发展弧，重点是疫苗接种，因为它导致从经典模型假设均匀混合（平均场）的人口，忽略人类行为，最近的模型，占行为反馈和/或人口空间/社会结构。所使用的许多方法起源于统计物理学，例如晶格和网络模型及其相关的分析框架。同样，接种行为和疾病传播之间的反馈回路形成了一个耦合的非线性系统，具有物理学上的类似性。我们还回顾了数字流行病学的新范式，其中挖掘在线社交媒体等数字数据源，以获取流行病学相关个人行为的高分辨率信息。借助统计物理学的工具和概念，并进一步借助新的数字数据来源，捕捉行为和疾病动态之间非线性相互作用的模型提供了一种模拟现实世界现象的新方法，并有助于改善健康结果。我们通过讨论该领域的开放问题和未来研究的方向来结束审查。
英文摘要：
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.
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