Simulation, optimization and decision support in dengue control: a structured literature review

Dengue fever poses relevant logistical and operational challenges to public health in Brazil, especially in urban areas characterized by spatial heterogeneity, budget constraints, and the simultaneous circulation of different virus serotypes. This paper presents a structured literature review focused on the integration of computational simulation models and optimization techniques applied to the control of the \textit{Aedes aegypti} vector. A search based on the snowballing technique was conducted to map recent contributions from 2021 to 2026, resulting in an analytical corpus of 29 studies. The analysis indicates that, although epidemiological simulation, optimal control theory, and the evaluation of intervention strategies are well-established approaches, there remains a relevant gap in the transition from predictive analyses to prescriptive decisions. In contrast to broader reviews on Operations Research applied to dengue, this work emphasizes the interface between spatial predictive models and prescriptive logistics optimization models, such as resource allocation and vehicle routing for spatial spraying. The study concludes that the development of hybrid simulation-optimization frameworks, supported by artificial intelligence techniques and digital twins, represents a promising opportunity to support operational decisions under budget constraints.