Development of Intelligent Systems for the Prediction and Diagnosis of Arboviruses Transmitted by <i>Aedes aegypti</i> in the Context of Climate Change

Arboviruses spread in urban tropics under climate change. We designed intelligent systems to predict cases and breeding sites of Aedes‑borne diseases in Recife, Brazil. We linked surveillance and climate data from APAC, INMET, LIRAa, and Recife’s Open Data Portal. We modeled 2013–2021 cases and 2009–2017 breeding sites. We generated spatial fields with inverse distance weighting. We built bimonthly training grids with 5,000 points and validation grids with 50,000 points. We tested linear regression, random forests, multilayer perceptrons, support vector regressors, and extreme learning machines in Weka and PyRCN. We ran 30 repetitions with cross‑validation. Random forests performed well. Multilayer perceptrons reached very high correlations but needed longer training. Polynomial SVMs reached near‑perfect accuracy but required very high computation. Single‑layer extreme learning machines delivered the best trade‑off, with low errors, correlations near 1.0, and short training times. The models produced fine‑scale risk predictions and highlighted priority areas. The findings support earlier, targeted control and guide public health plans in Recife.