Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

社会和文化力量影响着传染病在人群中传播的几乎所有方面，以及我们衡量、理解和应对流行病的能力。对于直接传播的感染，病原体传播依赖于人与人之间的接触，亲属关系、家庭和社会结构塑造接触模式，进而决定流行病动态。社会、经济和文化力量也会影响暴露模式、求医行为、感染结果、诊断和报告病例的可能性以及干预措施的采用。尽管流行病学的这些社会方面难以量化并且限制了建模框架在政策背景下的普遍性，但有关人类行为相关方面的新数据源越来越多。研究人员已经开始接受来自移动设备和其他技术的数据作为疾病传播行为驱动因素的有用指标，但要衡量和验证这些方法，尤其是在决策方面，还有很多工作要做。在这里，我们讨论如何将本地知识整合到模型框架的设计和新数据流的解释中，为公共卫生决策制定政策相关模型以及发展关于人类行为相关的稳健、可推广的理论提供了可能性。传染性疾病。