624Integrating human behaviour models with epidemiology to frame complex health problems

Abstract

Abstract Focus of Presentation Health problems are complex due to multiple interactions whose outcomes are not easily predicted with traditional epidemiology methods. The problems themselves require careful evaluation. Predictive models of human behaviour are potentially powerful tools to frame health problems, especially if the models can link the attributes and behaviour of individuals with the dynamics of the social and environmental systems within which they operate. We explore this potential by proposing a framework combining two modelling approaches — social network analysis (SNA) and agent-based modelling (ABM) - with epidemiological methods. We then apply this framework to understand why measles vaccination rates are decreasing across the world. Findings These techniques allowed us to understand the etiologic implications of heterogeneity within the population, social interaction, and environmental influences simultaneously, and to explore mechanistic interactions, feedback loops, and reciprocity between exposures and outcomes. This approach allowed us to frame complex social factors of health and disease in a holistic manner. Conclusions/Implications The proposed framework allows investigators to analyse complex health problems in a holistic manner. However, both SNA and ABM, and other modelling tools, are still too compartmentalised in application, despite the strong methodological and conceptual parallels between their uses in different disciplines. Key messages A fully integrated approach is needed to understanding complex health problems, which combines modelling approaches and the disciplinary insights of epidemiology and public health.