Understanding LLMs' Capabilities to Support Spatially-disaggregated Epidemiological Simulations

Evaluating public health interventions during disease outbreaks requires an understanding of the spatial patterns underlying epidemiological processes. In this study, we explore how Large Language Models (LLMs) can leverage spatial understanding and contextual reasoning to support spatially-disaggregated epidemiological simulations. We present an approach in which a system dynamics model queries an LLM at key decision points to determine appropriate mitigation strategies, informed by local profiles and the current outbreak status, and incorporates these strategies into the simulations. Through a series of experiments with COVID-19 data from San Diego County, we show how different LLMs perform in tasks requiring spatial adaptation of mitigation strategies, and how incorporating connectivity information through Retrieval-Augmented Generation (RAG) enhances the performance of these customizations. The results reveal significant differences among LLMs in their ability to account for spatial structure and optimize mitigation strategies accordingly. This highlights the importance of selecting the right model and enhancing it with relevant contextual information for effective public health interventions.