A Hybrid Agent-Based Model of Urban Dengue Transmission: City Specific Adaptation and Validation in Santa Marta, Colombia

Urban transmission of dengue and other Aedes aegypti-borne diseases is shaped by the interaction of vector ecology, human mobility, climate, and spatial heterogeneity. Capturing these interactions in city-specific settings requires models that are detailed enough to represent local transmission processes, while remaining computationally feasible for calibration, validation, and sensitivity analysis. This study presents a hybrid agent-based modeling and simulation (HABMS) approach, supported by high-performance computing (HPC), for simulating urban vector-borne disease transmission. Human residents are represented as mobile agents with stochastic infection dynamics, while mosquito populations are represented at the patch level through discrete-time equations. The model incorporates geospatial structure, temperature, land-use-based human movement, and local contextual information to represent transmission within urban environments. The framework was applied to Santa Marta, Colombia, as a city-specific case study. Transmission parameters were calibrated using surrogate-based Bayesian optimization, and their influence was assessed through sensitivity analysis. High-performance computing made the large number of stochastic simulations required for calibration and sensitivity analysis feasible. The calibrated model reproduced the magnitude and main seasonal shape of the observed dengue epidemic, including the peak and early decline. However, the model did not fully reproduce the late low-incidence tail of the season, indicating the need to incorporate external introductions of infection and rainfall-driven seasonal forcing of vector recruitment in future versions. A control scenario run on the calibrated baseline, a 30% reduction in larval carrying capacity, lowered the simulated seasonal attack rate by about three quarters and moved the system below the threshold at which local transmission is self-sustaining, illustrating the relative comparisons the calibrated model supports. This study contributes an adaptable hybrid model architecture and a high-performance implementation that make city-specific calibration and sensitivity analysis computationally feasible, demonstrated through a case study in Santa Marta.