Enhancing dengue time-series forecasting at the neighborhood level based on the intensity of contagion
Resumo
Este artigo mostra como modelos baseados em LSTM podem ter melhor acurácia na tarefa de prever casos de Dengue por bairro bem como identificar bairros mais suscetíveis de epidemia. Sugere-se o uso de modelos que incorporem informações heurísticas que capturam a intensidade de propagação da doença normalizada pela população. Os modelos baseados em LSTM, com e sem a heurística proposta, são avaliados quanto à capacidade de prever os casos para toda a cidade bem como para todos os bairros. Destaca-se a melhoria obtida ao se usar tal estratégia.
Palavras-chave:
rede neurais, séries temporais, dengue, previsão
Referências
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Caminha, C., Furtado, V., Pinheiro, V. & Silva, C. Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network. 2016 IEEE International Smart Cities Conference (ISC2). pp. 1-6 (2016)
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Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. Scikit-learn: Machine Learning in Python. Journal Of Machine Learning Research. 12 pp. 2825-2830 (2011)
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Araújo JLB, Oliveira EA, Lima Neto AS, Andrade JS Jr, Furtado V. Unveiling the paths of COVID-19 in a large city based on public transportation data. Sci Rep. 2023 Apr 8;13(1):5761. doi: 10.1038/s41598-023-32786-z. PMID: 37031258; PMCID: PMC10082688.
Manrique-Saide, P., Coleman, P., McCall, P., Lenhart, A., Vázquez-Prokopec, G. & Davies, C. Multi-scale analysis of the associations among egg, larval and pupal surveys and the presence and abundance of adult female Aedes aegypti (Stegomyia aegypti) in the city of Merida, Mexico. Medical And Veterinary Entomology. 28, 264-272 (2014)
Pilger, D., Lenhart, A., Manrique-Saide, P., Siqueira, J., Da Rocha, W. & Kroeger, A. Is routine dengue vector surveillance in central Brazil able to accurately monitor the Aedes aegypti population? Results from a pupal productivity survey. Tropical Medicine & International Health. 16, 1143-1150 (2011)
Caminha, C., Furtado, V., Pequeno, T., Ponte, C., Melo, H., Oliveira, E. & Andrade Jr, J. Human mobility in large cities as a proxy for crime. PloS One. 12, e0171609 (2017)
Caminha, C., Furtado, V., Pinheiro, V. & Silva, C. Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network. 2016 IEEE International Smart Cities Conference (ISC2). pp. 1-6 (2016)
He, Z. & Tao, H. Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study. International Journal Of Infectious Diseases. 74 pp. 61-70 (2018)
Kandula, S. & Shaman, J. Near-term forecasts of influenza-like illness: An evaluation of autoregressive time series approaches. Epidemics. 27 pp. 41-51 (2019)
Bomfim, R., Pei, S., Shaman, J., Yamana, T., Makse, H., Andrade Jr, J., Lima Neto, A. & Furtado, V. Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas. Journal Of The Royal Society Interface. 17, 20200691 (2020)
Schober, P., Boer, C. & Schwarte, L. Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia. 126, 1763-1768 (2018)
Shahid, F., Zameer, A. & Muneeb, M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons & Fractals. 140 pp. 110212 (2020)
Kara, A. Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm. Expert Systems With Applications. 180 pp. 115153 (2021)
Sistema de Monitoramento Diário de Agravos (SIMDA). (http://tc1.sms.fortaleza.ce.gov.br/simda), Accessed: 2019-06-18
Instituto Brasileiro de Geografia e Estatistica (IBGE). (http://www.ibge.gov.br), Accessed: 2019-06-18
Conover, W. Practical nonparametric statistics. (john wiley & sons,1999)
Verhulst, P. F. (1838). Notice sur la loi que la population poursuit dans son accroissement. Correspondance mathématique et physique, 10: p. 113-121.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. Scikit-learn: Machine Learning in Python. Journal Of Machine Learning Research. 12 pp. 2825-2830 (2011)
Chollet, F. & Others Keras. (https://keras.io, 2015)
Publicado
25/09/2023
Como Citar
BOMFIM, Rafael; ARAÚJO, J. L. B. de; LIMA NETO, Antonio S.; FURTADO, Vasco.
Enhancing dengue time-series forecasting at the neighborhood level based on the intensity of contagion. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 653-667.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234305.
