Abstract for: Community-Led Systems Dynamics Modeling to Understand Sea Level Rise-Related Health Impacts

As sea levels rise in Miami, increased flooding, saltwater intrusion, and environmental damage pose serious public health risks. These changes increase the chance of the spread of waterborne diseases, worsen respiratory conditions, and contribute to mental health stress, particularly in vulnerable coastal communities. Addressing these challenges requires a comprehensive approach integrating system dynamics modeling and AI-driven data analysis to assess and mitigate health impacts effectively. This study uses a community-led approach to system dynamics modeling to evaluate the health impacts of sea-level rise. The authors developed the initial model after reviewing the existing literature. Later, community representatives participated in a workshop to refine and finalize the model, ensuring it accurately reflected local concerns and perspectives. This study utilizes a community-led system dynamics modeling approach to evaluate the health impacts of sea-level rise. Initial modeling highlights the need for a multidimensional dataset incorporating demographic, socioeconomic, environmental, and health data to develop accurate predictive simulations. Given the complexity of these data and system interactions, AI-enabled modeling is the optimal approach to integrate stakeholder feedback and construct dynamic feedback models effectively. This research underscores the importance of integrating AI, system dynamics, and community engagement to address Miami’s public health risks from sea-level rise. It lays the groundwork for adaptive health strategies by incorporating diverse data and stakeholder insights. The findings guide policymakers toward data-driven, community-informed solutions. Future efforts will refine predictive models, expand stakeholder participation, and apply this approach to other vulnerable coastal regions facing climate-related health threats. editing the text