Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil

Authors: Sanchez-Gendriz, Ignacio; de Souza, Gustavo Fontoura; de Andrade, Ion; Doria Neto, Adrião Duarte; Tavares, Alessandre de Medeiros; Barros, Daniele Montenegro da Silva; de Morais, Antonio Higor Freire; +2 Authors

doi: 10.5281/zenodo.6408362 , 10.5281/zenodo.6035127

Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil

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Abstract

The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019).

{"references": ["Brazil (2020). Brazilian Notifiable Diseases Information System (Sinan). Home page Available from: .http://portalsinan.saude.gov.br/o-sinan"]}

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Keywords

Dengue, Deep Learning, LSTM, Ovitraps

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