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Open Access
Peer-reviewed
Research Article
App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
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Leila F. Dantas ,
Contributed equally to this work with: Leila F. Dantas, Igor T. Peres, Leonardo S. L. Bastos
Roles Formal analysis, Methodology, Writing – original draft
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Igor T. Peres ,
Contributed equally to this work with: Leila F. Dantas, Igor T. Peres, Leonardo S. L. Bastos
Roles Formal analysis, Writing – original draft
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Leonardo S. L. Bastos ,
Contributed equally to this work with: Leila F. Dantas, Igor T. Peres, Leonardo S. L. Bastos
Roles Methodology, Writing – original draft
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Janaina F. Marchesi ,
Roles Data curation
¶‡ These authors also contributed equally to this work.
Affiliation Instituto Tecgraf, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Guilherme F. G. de Souza ,
Roles Data curation
¶‡ These authors also contributed equally to this work.
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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João Gabriel M. Gelli ,
Roles Methodology
¶‡ These authors also contributed equally to this work.
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Fernanda A. Baião ,
Roles Formal analysis, Writing – review & editing
¶‡ These authors also contributed equally to this work.
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Paula Maçaira ,
Roles Formal analysis
¶‡ These authors also contributed equally to this work.
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Silvio Hamacher ,
Roles Formal analysis, Writing – review & editing
¶‡ These authors also contributed equally to this work.
Affiliation Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
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Fernando A. Bozza
Roles Project administration, Writing – review & editing
* E-mail: bozza.fernando@gmail.com, fernando.bozza@ini.fiocruz.br
¶‡ These authors also contributed equally to this work.
Affiliations National Institute of Infectious Diseases Evandro Chagas (INI), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
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App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
- Leila F. Dantas,
- Igor T. Peres,
- Leonardo S. L. Bastos,
- Janaina F. Marchesi,
- Guilherme F. G. de Souza,
- João Gabriel M. Gelli,
- Fernanda A. Baião,
- Paula Maçaira,
- Silvio Hamacher,
- Fernando A. Bozza
- Published: March 25, 2021
- https://doi.org/10.1371/journal.pone.0248920