Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges

Area of Science:

• Urban planning and public health
• Environmental health and exposome research
• Computational modeling and simulation

Background:

• Increasing urbanization necessitates understanding urban habitat impacts on health and livability.
• Urban exposome interventions (UEIs) require thorough assessment of health, cost, and social impacts due to potential long-term consequences.
• Spatial agent-based modeling (ABM) offers a framework to analyze complex interactions within urban systems.

Purpose of the Study:

• To discuss model architectures and methodological challenges for spatial ABM in assessing UEIs.
• To review the potential and limitations of ABM for UEI evaluation.
• To propose strategies for improving ABM implementation in exposome research.

Main Methods:

• Spatial agent-based modeling (ABM) to capture behavior-environment interactions, exposure dynamics, and social outcomes.
• Review of model components for active/passive exposure and intervention effects.
• Integration of human-environment interactions into health impact and cost-benefit analyses.
• Discussion of model calibration strategies.

Main Results:

• Spatial ABM can model complex urban systems for UEI assessment.
• Key challenges include designing realistic behavioral models, exposure-response function mismatches, scalability for long-term effects, and data/computational complexity for calibration.
• Despite challenges, strategies exist to enhance ABM application in exposome research.

Conclusions:

• Spatial ABM is a valuable tool for assessing urban exposome interventions.
• Addressing methodological challenges is crucial for accurate and scalable UEI evaluation.
• Further development of ABM strategies will improve its utility in urban health research.