Agent-based Modeling of Urban Exposome Interventions: Prospects, Methodological Considerations and Challenges

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Submission Summary
With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on health behaviours and health outcomes. However, implementing interventions that tackle the Exposome in complex urban systems can be costly, has long-term impacts and can have unforeseen consequences. Hence, it is important to assess the health impact, cost-effectiveness and social distributional impacts of possible Urban Exposome Interventions (UEIs) before implementing them. Spatial Agent-based modeling (ABM) has the ability to capture complex behaviour-environment interactions, exposure dynamics and social outcomes in a spatial context. In this paper, we discuss methodological considerations and challenges for successfully modelling UEIs using Spatial ABM. We discuss the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and and a social costs benefit analysis. We also discuss strategies for model calibration. Major challenges for a successful application of ABM to UEI-assessment are (1) the design of a realistic behavioural model that is able to capture different types of exposure and that responds to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting ABM. This paper suggests possible strategies to addressing these challenges and provides a roadmap for further implementation of Spatial ABM in Urban Exposome Intervention Assessment.
Submission ID :
RTC-64

Associated Sessions

PhD Candidate
,
Utrecht University
Utrecht University
Amsterdam UMC
Utrecht University

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