Comparing Three Agent-Based Models Implementations of Vector-Borne Disease Transmission Dynamics

Aedes aegypti, the vector responsible for transmitting diseases such as dengue, zika, and chikungunya, poses a significant public health threat in many regions. Understanding the dynamics of Aedes aegypti propagation is crucial for designing effective control and prevention strategies. Agent-Based Models (ABMs) have emerged as valuable tools for studying complex systems like vector-borne disease dynamics. Hybrid Agent-Based Models (HABMs), a variation of these models that incorporates Ordinary Differential Equations to model mosquitoes and ABMs to model humans, have been proposed by several authors. This study presents a comparative analysis of three HABMs to model Aedes aegypti propagation dynamics, with a focus on the impact of different modeling frameworks. The first model was built using Repast Simphony, a widely used ABM framework. It incorporates key factors such as mosquito life cycle, environmental conditions, and human-mosquito interactions. To enhance computational performance, the second model is migrated to a high-performance environment using Repast HPC. This migration leverages parallel computing capabilities to simulate larger populations. The third model is migrated to Mesa-Geo, a Python library specifically designed for geospatial agent-based modeling. This migration facilitates the integration of geospatial data into the model. Preliminary results show that migrating the model to a high performance environment enables more comprehensive analyses and reduces simulation runtime. Moreover, migrating to Mesa-Geo provides enhanced geospatial capabilities, and allows us to analyze the results in a graphical interface, which facilitates communication with decision makers. The main contributions of this research are: 1) insights into the trade-offs and benefits of using Repast Simphony, Repast HPC, and Mesa-Geo for modeling the transmission of viruses, and 2) a guide to researchers and stakeholders in selecting the most suitable modeling framework based on their specific requirements and available computational resources.