Department of Network and Data Science, Central European University, Vienna, Austria.
ISI Foundation, Turin, Italy.
Sci Adv. 2024 Oct 11;10(41):eadk4606. doi: 10.1126/sciadv.adk4606.
Variables related to socioeconomic status (SES), including income, ethnicity, and education, shape contact structures and affect the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to nonpharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socioeconomic and other dimensions into epidemic modeling.
与社会经济地位（SES）相关的变量，包括收入、种族和教育，塑造了接触结构并影响传染病的传播。然而，这些因素在传染病模型中往往被忽视，这些模型通常根据年龄和交互环境对社会接触进行分层。在这里，我们引入并研究了广义接触矩阵，这些矩阵在多个维度上对接触进行分层。我们证明了一个下限定理，证明了除了年龄和环境之外，忽略其他维度可能导致基本繁殖数的低估。通过在合成和实证数据中使用 SES 变量，我们说明了广义接触矩阵如何增强传染病模型，捕捉行为变化，例如不同群体对非药物干预措施的不同遵守程度。此外，我们强调了将 SES 特征纳入传染病模型的重要性，因为忽略它们可能会导致对传染病结果和动态的严重误解。我们的研究有助于将社会经济和其他方面纳入传染病建模的努力。
