Knowledge discovery and dengue forecasting applied in a four-year dataset collected at Natal/RN – Brazil

Abstract

Dengue is recognized as a health problem, it causes significant impacts on health worldwide, affecting millions of people each year. A useful method of dengue vector surveillance is to count Aedes aegypti eggs deposited in spatially distributed ovitraps. The present work uses a database collected in 397 ovitraps distributed in the municipality of Natal/RN – Brazil. The number of eggs in each ovitrap was counted weekly, for four years (2016 - 2019) and was analyzed jointly with the dengue incidence in the same period. Our results confirms that dengue incidence seems to be related to socioeconomic status on Natal’s municipality. Using a deep learning model, we predict the incidence of new dengue cases based on data obtained from the previous week of dengue or in the number of eggs present in the ovitraps. The analysis shows that ovitrap data allows earlier detection (4-6 weeks) than dengue cases itself (1 week). The results confirm that quantifying Aedes aegypti eggs can be valuable for planning health actions.