Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

Arboviruses spread in urban tropics under climate change. At Aedes aegypti breeding sites in Recife, Brazil, we linked surveillance and climate data from the Pernambuco Water and Climate Agency (APAC), the Brazilian National Institute of Meteorology (INMET), Rapid Survey of Indices for Aedes aegypti (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 5000 points and validation grids with 50,000 points. We tested linear regression, random forests, multilayer perceptrons, support vector regressors, and extreme learning machines in the Weka platform and Python Reservoir Computing Networks (PyRCNs). We ran 30 repetitions with cross-validation. The random forests performed well. Multilayer perceptrons reached very high correlations but needed longer training. Polynomial Support Vector Machines (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.

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.