Predicting high dengue incidence in municipalities of Brazil using path signatures.

Predicting the likelihood of high infectious disease incidence is critical for effective health surveillance. In the epidemiology of dengue, environmental conditions can significantly impact the transmission of the virus. Utilizing epidemiological indicators in conjunction with environmental variables can enhance predictions of dengue incidence risk. This study analyzed a dataset of weekly case numbers, temperature, and humidity across Brazilian municipalities to forecast the risk of high dengue incidence using data from 2014 to 2024. The framework involved constructing path signatures and applying lasso regression for binary outcomes. Sensitivity reached 75%, while specificity was extremely high, ranging from 75 to 100%. The best performance was observed with information gathered after 35 weeks of observations using data augmentation via embedding techniques. Path signatures effectively capture information from epidemiological and climate variables that influence dengue transmission. This framework can be applied in other countries, and its predictions can help optimize resource allocation for disease control.