Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/2/540 |
| _version_ | 1797411929314033664 |
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| author | Fabio Amaral Wallace Casaca Cassio M. Oishi José A. Cuminato |
| author_facet | Fabio Amaral Wallace Casaca Cassio M. Oishi José A. Cuminato |
| author_sort | Fabio Amaral |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T04:53:28Z |
| format | Article |
| id | doaj.art-da8dbe7af8e54f439816f449a42b3b31 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:53:28Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-da8dbe7af8e54f439816f449a42b3b312023-12-03T13:08:16ZengMDPI AGSensors1424-82202021-01-0121254010.3390/s21020540Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and BrazilFabio Amaral0Wallace Casaca1Cassio M. Oishi2José A. Cuminato3Faculty of Science and Technology, São Paulo State University (UNESP), Presidente Prudente 19060-900, BrazilDepartment of Energy Engineering, São Paulo State University (UNESP), Rosana 19273-000, BrazilFaculty of Science and Technology, São Paulo State University (UNESP), Presidente Prudente 19060-900, BrazilInstitute of Mathematics and Computer Sciences, University of São Paulo (USP), São Carlos 13566-590, BrazilSão 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.https://www.mdpi.com/1424-8220/21/2/540Covid-19SIRDdata-driven modelsmachine learninginteractive platform |
| spellingShingle | Fabio Amaral Wallace Casaca Cassio M. Oishi José A. Cuminato Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil Sensors Covid-19 SIRD data-driven models machine learning interactive platform |
| title | 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_short | 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 sao paulo and brazil |
| topic | Covid-19 SIRD data-driven models machine learning interactive platform |
| url | https://www.mdpi.com/1424-8220/21/2/540 |
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