A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-86970-0_22 http://hdl.handle.net/11449/229584 |
Resumo: | The 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. |
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A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian CoastDeep learningSARS-CoV-2Temporal convolutional neural networksTime series dataThe 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.Department of Mathematics National University of Piura Castilla s/nMedicine Faculty Cesar Vallejo University, Av. Victor Larco 1770Department of Mathematics and Statistics Universidade Federal de São João del-Rei C.P. 110São Paulo State University (Unesp) Campus of Itapeva Rua Geraldo Alckmin 519Artificial Intelligent Research KapAITech Research Group Condominio Sol de Chan-ChanSão Paulo State University (Unesp) Campus of Itapeva Rua Geraldo Alckmin 519National University of Piura Castilla s/nCesar Vallejo UniversityUniversidade Federal de São João del-Rei C.P. 110Universidade Estadual Paulista (UNESP)Condominio Sol de Chan-ChanAguilar, I. LuisIbáñez-Reluz, MiguelAguilar, Juan C. Z.Zavaleta-Aguilar, Elí W. [UNESP]Aguilar, L. Antonio2022-04-29T08:33:19Z2022-04-29T08:33:19Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject304-319http://dx.doi.org/10.1007/978-3-030-86970-0_22Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12951 LNCS, p. 304-319.1611-33490302-9743http://hdl.handle.net/11449/22958410.1007/978-3-030-86970-0_222-s2.0-85115691423Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-29T08:33:19Zoai:repositorio.unesp.br:11449/229584Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:08:00.349935Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
title |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
spellingShingle |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast Aguilar, I. Luis Deep learning SARS-CoV-2 Temporal convolutional neural networks Time series data |
title_short |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
title_full |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
title_fullStr |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
title_full_unstemmed |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
title_sort |
A Deep Learning Approach to Forecast SARS-CoV-2 on the Peruvian Coast |
author |
Aguilar, I. Luis |
author_facet |
Aguilar, I. Luis Ibáñez-Reluz, Miguel Aguilar, Juan C. Z. Zavaleta-Aguilar, Elí W. [UNESP] Aguilar, L. Antonio |
author_role |
author |
author2 |
Ibáñez-Reluz, Miguel Aguilar, Juan C. Z. Zavaleta-Aguilar, Elí W. [UNESP] Aguilar, L. Antonio |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
National University of Piura Castilla s/n Cesar Vallejo University Universidade Federal de São João del-Rei C.P. 110 Universidade Estadual Paulista (UNESP) Condominio Sol de Chan-Chan |
dc.contributor.author.fl_str_mv |
Aguilar, I. Luis Ibáñez-Reluz, Miguel Aguilar, Juan C. Z. Zavaleta-Aguilar, Elí W. [UNESP] Aguilar, L. Antonio |
dc.subject.por.fl_str_mv |
Deep learning SARS-CoV-2 Temporal convolutional neural networks Time series data |
topic |
Deep learning SARS-CoV-2 Temporal convolutional neural networks Time series data |
description |
The 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-29T08:33:19Z 2022-04-29T08:33:19Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-86970-0_22 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12951 LNCS, p. 304-319. 1611-3349 0302-9743 http://hdl.handle.net/11449/229584 10.1007/978-3-030-86970-0_22 2-s2.0-85115691423 |
url |
http://dx.doi.org/10.1007/978-3-030-86970-0_22 http://hdl.handle.net/11449/229584 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12951 LNCS, p. 304-319. 1611-3349 0302-9743 10.1007/978-3-030-86970-0_22 2-s2.0-85115691423 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
304-319 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808129492839825408 |