App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning

Detalhes bibliográficos
Autor(a) principal: Dantas, Leila F.
Data de Publicação: 2021
Outros Autores: Peres, Igor T., Bastos, Leonardo S. L., Marchesi, Janaina F., Souza, Guilherme F. G. de, Gelli, João Gabriel M., Baião, Fernanda A., Maçaira, Paula, Hamacher, Silvio, Bozza, Fernando A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/46765
Resumo: Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.
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spelling Dantas, Leila F.Peres, Igor T.Bastos, Leonardo S. L.Marchesi, Janaina F.Souza, Guilherme F. G. deGelli, João Gabriel M.Baião, Fernanda A.Maçaira, PaulaHamacher, SilvioBozza, Fernando A.2021-04-19T19:14:36Z2021-04-19T19:14:36Z2021DANTAS, Leila F. et al. App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning. PloS One, v. 16, n. 3, p. 1-13, 2021.1932-6203https://www.arca.fiocruz.br/handle/icict/4676510.1371/journal.pone.0248920engPublic Library of ScienceApp-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learninginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Instituto Tecgraf. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.Background: Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing. Materials and methods: We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker. We applied machine learning techniques and provided a SARS-CoV-2 infection risk map of Rio de Janeiro city. Results: From April 28 to July 16, 2020, 337,435 individuals registered their symptoms through the app. Of these, 49,721 participants were tested for SARS-CoV-2 infection, being 5,888 (11.8%) positive. Among self-reported symptoms, loss of smell (OR[95%CI]: 4.6 [4.4-4.9]), fever (2.6 [2.5-2.8]), and shortness of breath (2.1 [1.6-2.7]) were independently associated with SARS-CoV-2 infection. Our final model obtained a competitive performance, with only 7% of false-negative users predicted as negatives (NPV = 0.93). The model was incorporated by the "Dados do Bem" app aiming to prioritize users for testing. We developed an external validation in the city of Rio de Janeiro. We found that the proportion of positive results increased significantly from 14.9% (before using our model) to 18.1% (after the model). Conclusions: Our results showed that the combination of symptoms might predict SARS-Cov-2 infection and, therefore, can be used as a tool by decision-makers to refine testing and disease control strategies.COVID-19Virus testingSARS-CoV-2Medical risk factorsForecastingFeversinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83099https://www.arca.fiocruz.br/bitstream/icict/46765/1/license.txt586c046dcfeef936e32f0323bb9a47c0MD51ORIGINALApp-based_Fernando_Bozza_etal_INI_2021.pdfApp-based_Fernando_Bozza_etal_INI_2021.pdfapplication/pdf1785907https://www.arca.fiocruz.br/bitstream/icict/46765/2/App-based_Fernando_Bozza_etal_INI_2021.pdfe458e70a441bd153f4b357b0bf6a866aMD52TEXTApp-based_Fernando_Bozza_etal_INI_2021.pdf.txtApp-based_Fernando_Bozza_etal_INI_2021.pdf.txtExtracted 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dc.title.pt_BR.fl_str_mv App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
title App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
spellingShingle App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
Dantas, Leila F.
COVID-19
Virus testing
SARS-CoV-2
Medical risk factors
Forecasting
Fevers
title_short App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
title_full App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
title_fullStr App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
title_full_unstemmed App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
title_sort App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
author Dantas, Leila F.
author_facet Dantas, Leila F.
Peres, Igor T.
Bastos, Leonardo S. L.
Marchesi, Janaina F.
Souza, Guilherme F. G. de
Gelli, João Gabriel M.
Baião, Fernanda A.
Maçaira, Paula
Hamacher, Silvio
Bozza, Fernando A.
author_role author
author2 Peres, Igor T.
Bastos, Leonardo S. L.
Marchesi, Janaina F.
Souza, Guilherme F. G. de
Gelli, João Gabriel M.
Baião, Fernanda A.
Maçaira, Paula
Hamacher, Silvio
Bozza, Fernando A.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Dantas, Leila F.
Peres, Igor T.
Bastos, Leonardo S. L.
Marchesi, Janaina F.
Souza, Guilherme F. G. de
Gelli, João Gabriel M.
Baião, Fernanda A.
Maçaira, Paula
Hamacher, Silvio
Bozza, Fernando A.
dc.subject.en.pt_BR.fl_str_mv COVID-19
Virus testing
SARS-CoV-2
Medical risk factors
Forecasting
Fevers
topic COVID-19
Virus testing
SARS-CoV-2
Medical risk factors
Forecasting
Fevers
description Pontifícia Universidade Católica do Rio de Janeiro. Departamento de Engenharia Industrial. Rio de Janeiro, RJ, Brasil.
publishDate 2021
dc.date.accessioned.fl_str_mv 2021-04-19T19:14:36Z
dc.date.available.fl_str_mv 2021-04-19T19:14:36Z
dc.date.issued.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.fl_str_mv DANTAS, Leila F. et al. App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning. PloS One, v. 16, n. 3, p. 1-13, 2021.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/46765
dc.identifier.issn.pt_BR.fl_str_mv 1932-6203
dc.identifier.doi.none.fl_str_mv 10.1371/journal.pone.0248920
identifier_str_mv DANTAS, Leila F. et al. App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning. PloS One, v. 16, n. 3, p. 1-13, 2021.
1932-6203
10.1371/journal.pone.0248920
url https://www.arca.fiocruz.br/handle/icict/46765
dc.language.iso.fl_str_mv eng
language eng
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dc.publisher.none.fl_str_mv Public Library of Science
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dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
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