Towards providing effective data-driven responses to predict the covid-19 in são paulo and brazil

Detalhes bibliográficos
Autor(a) principal: Amaral, Fabio [UNESP]
Data de Publicação: 2021
Outros Autores: Casaca, Wallace [UNESP], Oishi, Cassio M. [UNESP], Cuminato, José A.
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.
id UNSP_13d458bb9a4b5796692e97ff4c20b96f
oai_identifier_str oai:repositorio.unesp.br:11449/205737
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling 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
_version_ 1803046459376926720