Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/s21020540 http://hdl.handle.net/11449/205737 |
Resumo: | São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. |
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Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazilCovid-19Data-driven modelsInteractive platformMachine learningSIRDSão Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.Fundação para a Ciência e a TecnologiaUniversidade Estadual PaulistaFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Faculty of Science and Technology São Paulo State University (UNESP)Department of Energy Engineering São Paulo State University (UNESP)Institute of Mathematics and Computer Sciences University of São Paulo (USP)Faculty of Science and Technology São Paulo State University (UNESP)Department of Energy Engineering São Paulo State University (UNESP)FAPESP: 2013/07375-0CNPq: 305383/2019-1CAPES: 88882.441642/2019-01Universidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Amaral, Fabio [UNESP]Casaca, Wallace [UNESP]Oishi, Cassio M. [UNESP]Cuminato, José A.2021-06-25T10:20:27Z2021-06-25T10:20:27Z2021-01-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-25http://dx.doi.org/10.3390/s21020540Sensors (Switzerland), v. 21, n. 2, p. 1-25, 2021.1424-8220http://hdl.handle.net/11449/20573710.3390/s210205402-s2.0-85099341870Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSensors (Switzerland)info:eu-repo/semantics/openAccess2021-10-22T16:54:00Zoai:repositorio.unesp.br:11449/205737Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-22T16:54Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
title |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
spellingShingle |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil Amaral, Fabio [UNESP] Covid-19 Data-driven models Interactive platform Machine learning SIRD |
title_short |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
title_full |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
title_fullStr |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
title_full_unstemmed |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
title_sort |
Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil |
author |
Amaral, Fabio [UNESP] |
author_facet |
Amaral, Fabio [UNESP] Casaca, Wallace [UNESP] Oishi, Cassio M. [UNESP] Cuminato, José A. |
author_role |
author |
author2 |
Casaca, Wallace [UNESP] Oishi, Cassio M. [UNESP] Cuminato, José A. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Amaral, Fabio [UNESP] Casaca, Wallace [UNESP] Oishi, Cassio M. [UNESP] Cuminato, José A. |
dc.subject.por.fl_str_mv |
Covid-19 Data-driven models Interactive platform Machine learning SIRD |
topic |
Covid-19 Data-driven models Interactive platform Machine learning SIRD |
description |
São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:20:27Z 2021-06-25T10:20:27Z 2021-01-02 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/s21020540 Sensors (Switzerland), v. 21, n. 2, p. 1-25, 2021. 1424-8220 http://hdl.handle.net/11449/205737 10.3390/s21020540 2-s2.0-85099341870 |
url |
http://dx.doi.org/10.3390/s21020540 http://hdl.handle.net/11449/205737 |
identifier_str_mv |
Sensors (Switzerland), v. 21, n. 2, p. 1-25, 2021. 1424-8220 10.3390/s21020540 2-s2.0-85099341870 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sensors (Switzerland) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-25 |
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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1803046459376926720 |