Forecast of Dengue Cases based on the Deep Learning Approach: A Case Study for a Brazilian City

Luiz Sérgio de Souza, Solange Alves-Souza, Lucia Filgueiras, Leandro Velloso, Mailson Fontes de Carvalho, Luciano Garcia, Marcia Ito, Johne Jarske, Tânia Santos, Henrique Fernandes, Gabriela Araújo, Wesley Barbosa

2022

Abstract

According to the World Health Organization (WHO), dengue is an endemic disease in more than 100 countries, with about 50 million people infected each year and 2.5 billion living in risk areas. Dengue requires a major research effort in countries affected by the disease, as its incidence is strongly determined by non-linear local processes, such as climatic conditions, social characteristics and habits of populations (Falcón-Lezama, 2016). In this scenario, forecasting models can be important tools for outbreak control, allowing health institutions to anticipate the mobilization of resources. In this article, we use deep learning, including long and short-term memory (LSTM) and dense layers of perceptrons to implement a forecast model of dengue cases for 5 epidemiological weeks ahead with a mean accuracy of 93%.

Paper Citation

in Harvard Style

Sérgio de Souza L., Alves-Souza S., Filgueiras L., Velloso L., Fontes de Carvalho M., Garcia L., Ito M., Jarske J., Santos T., Fernandes H., Araújo G. and Barbosa W. (2022). Forecast of Dengue Cases based on the Deep Learning Approach: A Case Study for a Brazilian City. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 71-76. DOI: 10.5220/0011135500003277

in Bibtex Style

@conference{delta22,
author={Luiz Sérgio de Souza and Solange Alves-Souza and Lucia Filgueiras and Leandro Velloso and Mailson Fontes de Carvalho and Luciano Garcia and Marcia Ito and Johne Jarske and Tânia Santos and Henrique Fernandes and Gabriela Araújo and Wesley Barbosa},
title={Forecast of Dengue Cases based on the Deep Learning Approach: A Case Study for a Brazilian City},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={71-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011135500003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Forecast of Dengue Cases based on the Deep Learning Approach: A Case Study for a Brazilian City
SN - 978-989-758-584-5
AU - Sérgio de Souza L.
AU - Alves-Souza S.
AU - Filgueiras L.
AU - Velloso L.
AU - Fontes de Carvalho M.
AU - Garcia L.
AU - Ito M.
AU - Jarske J.
AU - Santos T.
AU - Fernandes H.
AU - Araújo G.
AU - Barbosa W.
PY - 2022
SP - 71
EP - 76
DO - 10.5220/0011135500003277