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Authors: Luiz Sérgio de Souza 1 ; Solange Nice Alves-Souza 2 ; Lucia Vilela Leite Filgueiras 2 ; Leandro Manuel Reis Velloso 3 ; Mailson Fontes de Carvalho 4 ; Luciano Anísio Garcia 5 ; Marcia Ito 1 ; Johne Marcus Jarske 6 ; Tânia Letícia dos Santos 1 ; Henrique Mathias Fernandes 7 ; Gabriela Momberg Araújo 3 and Wesley Lourenço Barbosa 2

Affiliations: 1 Faculdade de Tecnologia do Estado de São Paulo (FATEC), Centro Estadual de Educação Tecnológica Paula Souza, Brazil ; 2 Departamento de Engenharia de Computação e Sistemas Digitais (PCS), Universidade de São Paulo (USP), Brazil ; 3 Faculdade de Arquitetura e Urbanismo (FAU), Universidade de São Paulo (USP), Brazil ; 4 Universidade Federal do Piauí (UFPI), Brazil ; 5 Universidade de São Paulo (USP), Programa de Pós-graduação em Sistemas de Informação, Brazil ; 6 Universidade de São Paulo (USP), Programa de Pós-graduação em Engenharia Elétrica, São Paulo (SP), Brazil ; 7 Universidade de São Paulo (USP), Curso de Biblioteconomia, São Paulo (SP), Brazil

Keyword(s): Forecasting, Time Series, Dengue, Deep Learning, LSTM, MLP.

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%.

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Paper citation in several formats:
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 - DeLTA; ISBN 978-989-758-584-5; ISSN 2184-9277, SciTePress, pages 71-76. DOI: 10.5220/0011135500003277

@conference{delta22,
author={Luiz {Sérgio de Souza}. and Solange Nice Alves{-}Souza. and Lucia Vilela Leite Filgueiras. and Leandro Manuel Reis Velloso. and Mailson {Fontes de Carvalho}. and Luciano Anísio Garcia. and Marcia Ito. and Johne Marcus Jarske. and Tânia Letícia dos Santos. and Henrique Mathias Fernandes. and Gabriela Momberg Araújo. and Wesley Louren\c{C}o 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 - DeLTA},
year={2022},
pages={71-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011135500003277},
isbn={978-989-758-584-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - 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
IS - 2184-9277
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
PB - SciTePress

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