结构因果影响（SCI）在社会不平等的疾病动力学模型中捕捉社会力量
Structural causal influence (SCI) captures the forces of social inequality in models of disease dynamics
AI总结：
本研究引入结构因果影响（SCI）指标，将社会不平等作为传播动力学的正式驱动因素，量化易感人群中子社区的相对脆弱性，并在丙肝和智能体模型中验证了社区间传播对疫情持续的影响。
摘要：
数学建模在理解传染病传播如何在人群中表现方面发挥了核心作用。这些模型已经证明了关键社区层面因素在构建流行病风险中的重要性，如今在公共卫生决策支持中被常规使用。其更广泛效用的一个障碍是，现有体系通常不将社会不平等视为传播动力学变异性的独立正式驱动因素。鉴于数十年的证据支持不平等在构建更广泛社会结构中的组织效应，以及更具体地在传染病风险方面的组织效应，解决这一建模空白至关重要。在本研究中，我们通过引入一个度量指标——结构因果影响（SCI），在将社会力量整合到计算流行病学的先前努力基础上进行了拓展。SCI使用因果分析来提供易感人群中子社区相对脆弱性的量度，这种脆弱性是由获取治疗的机会、疾病暴露以及由社会力量驱动的其他决定因素等特征差异所塑造的。我们在一个简单案例中开发了该指标，并将其应用于一个具有公共卫生重要性的背景：注射吸毒人群中的丙型肝炎病毒。此外，我们使用传染病智能体模型展示了SCI的灵活性。我们对SCI的使用表明，在多社区模型的特定参数下，“较不脆弱”的社区可能实现低于1的基本再生数，从而确保疾病消亡。然而，即使是社区之间的最小传播也能增加该数值，导致两个社区内持续流行。
英文摘要：
Mathematical modeling has played a central role in understanding how infectious disease transmission manifests in populations. These models have demonstrated the importance of key community-level factors in structuring epidemic risk, and are now routinely used in public health for decision support. One barrier to their broader utility is that the existing canon does not often accommodate social inequalities as distinct formal drivers of variability in transmission dynamics. Given decades of evidence supporting the organizational effects of inequalities in structuring society more generally, and infectious disease risk more specifically, addressing this modeling gap is of critical importance. In this study, we build on previous efforts to integrate social forces into computational epidemiology by introducing a metric, the structural causal influence (SCI). The SCI uses causal analysis to provide a measure of the relative vulnerability of sub-communities within a susceptible population, shaped by differences in characteristics such as access to therapy, exposure to disease, and other determinants driven by social forces. We develop our metric in a simple case and apply it to a context of public health importance: Hepatitis C virus in a population of persons who inject drugs. In addition, we demonstrate the flexibility of the SCI using an agent-based model of an infectious disease. Our use of the SCI reveals that, under specific parameters in a multi-community model, the "less vulnerable" community may achieve a basic reproduction number below one, ensuring disease extinction. However, even minimal transmission between communities can increase this number, leading to sustained epidemics within both communities.
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