A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorNational University of Piura Castilla s/n-
Autor(es): dc.contributorCesar Vallejo University-
Autor(es): dc.contributorUniversidade Federal de São João del-Rei C.P. 110-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCondominio Sol de Chan-Chan-
Autor(es): dc.creatorAguilar, I. Luis-
Autor(es): dc.creatorIbáñez-Reluz, Miguel-
Autor(es): dc.creatorAguilar, Juan C. Z.-
Autor(es): dc.creatorZavaleta-Aguilar, Elí W.-
Autor(es): dc.creatorAguilar, L. Antonio-
Data de aceite: dc.date.accessioned2025-08-21T22:36:33Z-
Data de disponibilização: dc.date.available2025-08-21T22:36:33Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-86970-0_22-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229584-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229584-
Descrição: dc.descriptionThe current spreading of the SARS-CoV-2 pandemic had put all the scientific community in alert. Even in the presence of different vaccines, the active virus still represents a global challenge. Due to its rapid spreading and uncertain nature, having the ability to forecast its dynamics becomes a necessary tool in the development of fast and efficient health policies. This study implements a temporal convolutional neural network (TCN), trained with the open covid-19 data set sourced by the Health Ministry of Peru (MINSA) on the Peruvian coast. In order to obtain a robust model, the data was divided into validation and training sets, without overlapping. Using the validation set the model architecture and hyper-parameters were found with Bayesian optimization. Using the optimal configuration the TCN was trained with a test and forecasting window of 15 days ahead. Predictions on available data were made from March 06, 2020 until April 13, 2021, whereas forecasting from April 14 to April 29, 2021. In order to account for uncertainty, the TCN estimated the 5%, 50% and 95% prediction quantiles. Evaluation was made using the MAE, MAD, MSLE, RMSLE and PICP metrics. Results suggested some variations in the data distribution. Test results shown an improvement of 24.241, 0.704 and 0.422 for the MAD, MSLE and RMSLE metrics respectively. Finally, the prediction interval analysis shown an average of 97.886% and 97.778% obtained by the model in the train and test partitions.-
Descrição: dc.descriptionDepartment of Mathematics National University of Piura Castilla s/n-
Descrição: dc.descriptionMedicine Faculty Cesar Vallejo University, Av. Victor Larco 1770-
Descrição: dc.descriptionDepartment of Mathematics and Statistics Universidade Federal de São João del-Rei C.P. 110-
Descrição: dc.descriptionSão Paulo State University (Unesp) Campus of Itapeva Rua Geraldo Alckmin 519-
Descrição: dc.descriptionArtificial Intelligent Research KapAITech Research Group Condominio Sol de Chan-Chan-
Descrição: dc.descriptionSão Paulo State University (Unesp) Campus of Itapeva Rua Geraldo Alckmin 519-
Formato: dc.format304-319-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectSARS-CoV-2-
Palavras-chave: dc.subjectTemporal convolutional neural networks-
Palavras-chave: dc.subjectTime series data-
Título: dc.titleA Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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