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Ítem Stochastic modeling of malaria dynamics: an agent-based simulation framework(Universidad Industrial de Santander, 2026)This study aims to analyze the epidemiological dynamics of malaria transmission in Quibdó, Colombia, using a computational modeling approach grounded in agent-based simulation principles. Leveraging discrete spatial and temporal frameworks, the research systematically represents human and mosquito populations interacting within a structured urban environment characterized by tropical climate conditions conducive to disease persistence. The model integrates empirical epidemiological data for calibration and validation, enabling the exploration of stochastic processes underlying host-vector-environment interactions. Five independent simulations were performed over a full calendar year using Monte Carlo methods to capture variability and population heterogeneity. The results reveal that targeted management of aquatic habitats—central to mosquito larval development—can induce rapid declines in malaria incidence. In addition, the simulations demonstrate that reducing larval habitats by as little as two percent leads to the effective interruption of transmission within several months, showcasing the critical impact of local interventions. The principal contribution of this work lies in the application of computational and statistical physics concepts, particularly stochastic modeling and system dynamics, to address complex biological processes in infectious disease epidemiology.