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

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorAmaral, Fabio [UNESP]-
Autor(es): dc.creatorCasaca, Wallace [UNESP]-
Autor(es): dc.creatorOishi, Cassio M. [UNESP]-
Autor(es): dc.creatorCuminato, José A.-
Data de aceite: dc.date.accessioned2022-02-22T00:45:09Z-
Data de disponibilização: dc.date.available2022-02-22T00:45:09Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/s21020540-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/205737-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/205737-
Descrição: dc.descriptionSão 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.-
Descrição: dc.descriptionFundação para a Ciência e a Tecnologia-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFaculty of Science and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Energy Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionInstitute of Mathematics and Computer Sciences University of São Paulo (USP)-
Descrição: dc.descriptionFaculty of Science and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Energy Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionCNPq: 305383/2019-1-
Descrição: dc.descriptionCAPES: 88882.441642/2019-01-
Formato: dc.format1-25-
Idioma: dc.languageen-
Relação: dc.relationSensors (Switzerland)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCovid-19-
Palavras-chave: dc.subjectData-driven models-
Palavras-chave: dc.subjectInteractive platform-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSIRD-
Título: dc.titleTowards providing effective data-driven responses to predict the covid-19 in são paulo and brazil-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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