Predicting Dengue Outbreaks with Explainable Machine Learning
Robson Aleixo, Fábio Kon, Rudi Rocha, Marcela Santos Camargo, Raphael Y. de Camargo
Abstract
Seasonal infectious diseases, such as dengue, have been causing great losses in many countries around the world in terms of deaths, quality of life, and economic burden. In Brazil, this is relevant not only in large cities such as Rio de Janeiro and São Paulo but, according to the Ministry of Health, in another 500 cities throughout the country. Predicting the occurrence of diseases, such as dengue bursts, can be a valuable instrument for public health management as health officials can better prepare and redirect resources to the affected areas. In this paper, we present an explainable machine learning model to forecast the number of dengue occurrences in a large metropolis, Rio de Janeiro. We focus on explainable models, which provide health authorities with the reasons for outbreak predictions, allowing them to plan their actions accordingly. We trained a gradient boosting decision tree algorithm (CatBoost) with data from the National System of Information on Notifiable Diseases (SINAN), weather data, and socio-demographic data from The Brazilian Institute of Geoaraphy and Statistics (IBGE).