Dept. of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.
Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.
PLoS Comput Biol. 2022 Feb 9;18(2):e1009795. doi: 10.1371/journal.pcbi.1009795. eCollection 2022 Feb.
Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models-and, by consequence, modelers-guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.
帮助规划疾病负担、医院容量，并为非药物干预措施提供信息。这些模型在 COVID-19 大流行中发挥了关键作用，传播模型——因此，建模者——指导着针对 SARS-CoV-2 的全球、国家和地方应对措施。然而，这些模型在很大程度上没有考虑到导致社会经济、种族和地理健康差异的社会和结构性因素。在这篇文章中，我们提出并试图澄清与传染病建模研究和实践中的这一重要差距相关的几个问题：为什么新兴传染病的流行病学模型通常忽略导致健康结果差异的已知结构性驱动因素？主要关注总体结果的框架对感染公平性有什么后果？为了在大流行期间制定更全面的基于模型的决策方法，应该做些什么？在这篇综述中，我们评估了在新兴传染病模型中排除差异驱动因素的潜在历史和政治解释，尽管有充分的证据表明相反，但这些模型通常被描述为“均等机会感染源”。我们从其他疾病系统（HIV、性传播感染）中寻找例子，并成功地将社会不平等纳入急性感染传播模型，以此作为将社会联系、环境和结构性因素纳入一致、严谨和可解释的建模框架的蓝图。最后，我们概述了指导新兴传染病建模的原则，将感染不公平的原因作为核心而不是外围机制来体现。
