Computational Modeling and Artificial Intelligence for Infectious Disease Outbreak Risk Classification in Maceió, Alagoas, Brazil

• Samila Raphaela de Oliveira IFAL
• Victor Luan de Lima Lemos IFAL
• Tarsis Marinho de Souza IFAL
• Cledja Karina Rolim da Silva IFAL

DOI: https://doi.org/10.5753/encompif.2026.20219

Resumo

Infectious diseases remain a major public health challenge and require effective strategies for surveillance, prevention, and control. In Brazil, despite the availability of epidemiological surveillance systems such as SINAN, the occurrence of outbreaks still reveals limitations in anticipating critical events. In this context, the early identification of risk patterns is essential to support timely public health action. This study analyzes and compares machine learning models for classifying outbreak risk using SINAN data from Maceió, Alagoas, with emphasis on five diseases: Hepatitis, Meningitis, Pertussis, Varicella, and Exanthematic Diseases. The proposed approach models risk as three levels (Normal, Attention, Outbreak) to support more interpretable and operationally useful surveillance outcomes.

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