Identifying spatial clusters of risk for dengue cases according to social vulnerability constitutes a powerful tool for effective epidemiological and urban management. In this way, this work carries out an ecological study that considered confirmed cases of dengue and actions of endemic agents in the municipality of São Carlos-SP, in the year 2019, through the application of the spatial scan technique for classification of the risk areas, computing the relative risk (RR), with a confidence interval of 95% (CI95%:) and the São Paulo Social Vulnerability Index (IPVS) to characterize these areas. Seven clusters were identified, two of which were high risk (RR=37.54 / RR=33.39), with the highest risk located in a region with high vulnerability and the second in a region with very low vulnerability. These results provide information that allows the targeting of specific control actions from the early detection of cases in places with greater dengue transmissibility.
The decision-making of complex problems, such as epidemics monitoring and control, involves multiple heterogeneous data and spatial and temporal aspects. Most problems cannot be reduced to one objective, characterized as multi-criteria decision-making (MCDM) problems. Adding temporal and spatial aspects further increases the complexity of addressing those problems. This paper proposed a framework that uses evolutionary algorithms and map algebra for addressing spatial and temporal multidimensional complex problems. It was evaluated in a case study of dengue and tuberculosis diseases in an urban environment, considering multi-resolution data and a genetic algorithm. Several analyses were conducted, generating maps and information essential to generate insights into the problem and a better understanding of the spatial relations between the variables. The framework and the code implemented could be applied to different problems, spatial resolutions, and objectives.
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