App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , |
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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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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article |
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publishedVersion |
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 |
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https://www.arca.fiocruz.br/handle/icict/46765 |
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eng |
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Public Library of Science |
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Public Library of Science |
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