{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:37:52Z","timestamp":1787909872454,"version":"build-2784847793"},"reference-count":27,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"crossref","award":["169049\/2017-5"],"award-info":[{"award-number":["169049\/2017-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["306802\/2015-5"],"award-info":[{"award-number":["306802\/2015-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["403863\/2016-3"],"award-info":[{"award-number":["403863\/2016-3"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["310523\/2016-8"],"award-info":[{"award-number":["310523\/2016-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["144636\/2019-0"],"award-info":[{"award-number":["144636\/2019-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88887.178844\/2018-00"],"award-info":[{"award-number":["88887.178844\/2018-00"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      We performed a retrospective analysis of individuals registered in \"\n                      <jats:italic>Dados do Bem<\/jats:italic>\n                      ,\" 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u20134.9]), fever (2.6 [2.5\u20132.8]), and shortness of breath (2.1 [1.6\u20132.7]) 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 \"\n                      <jats:italic>Dados do Bem<\/jats:italic>\n                      \" 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).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0248920","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T15:56:10Z","timestamp":1616687770000},"page":"e0248920","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":25,"title":["App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-0961","authenticated-orcid":true,"given":"Leila F.","family":"Dantas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-4690","authenticated-orcid":true,"given":"Igor T.","family":"Peres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo S. L.","family":"Bastos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0262-5384","authenticated-orcid":true,"given":"Janaina F.","family":"Marchesi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilherme F. G.","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Gabriel M.","family":"Gelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernanda A.","family":"Bai\u00e3o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6235-8006","authenticated-orcid":true,"given":"Paula","family":"Ma\u00e7aira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvio","family":"Hamacher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4878-0256","authenticated-orcid":true,"given":"Fernando A.","family":"Bozza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"pone.0248920.ref001","unstructured":"Coronavirus Update (Live). [cited 7 Jul 2020]. Available: https:\/\/www.worldometers.info\/coronavirus\/"},{"key":"pone.0248920.ref002","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/1469-0691.12477","article-title":"Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience","volume":"20","author":"D Paolotti","year":"2014","journal-title":"Clinical Microbiology and Infection"},{"key":"pone.0248920.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6152041","article-title":"Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19","volume":"2020","author":"G Zhu","year":"2020","journal-title":"Discrete Dynamics in Nature and Society"},{"key":"pone.0248920.ref004","first-page":"1","article-title":"Wearable sensor data and self-reported symptoms for COVID-19 detection","author":"G Quer","year":"2020","journal-title":"Nature Medicine"},{"key":"pone.0248920.ref005","doi-asserted-by":"crossref","first-page":"e21364","DOI":"10.2196\/21364","article-title":"Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses","volume":"8","author":"A Mehl","year":"2020","journal-title":"JMIR mHealth and uHealth"},{"key":"pone.0248920.ref006","first-page":"1","article-title":"Real-time tracking of self-reported symptoms to predict potential COVID-19","author":"C Menni","year":"2020","journal-title":"Nature Medicine"},{"key":"pone.0248920.ref007","unstructured":"Dados do Bem. Available: https:\/\/dadosdobem.com.br\/"},{"key":"pone.0248920.ref008","doi-asserted-by":"crossref","first-page":"e1390","DOI":"10.1016\/S2214-109X(20)30387-9","article-title":"SARS-CoV-2 antibody prevalence in Brazil: results from two successive nationwide serological household surveys","volume":"8","author":"PC Hallal","year":"2020","journal-title":"The Lancet Global Health"},{"key":"pone.0248920.ref009","unstructured":"Acur\u00e1cia dos testes diagn\u00f3sticos registrados na ANVISA para a COVID-19. Available: https:\/\/portalarquivos2.saude.gov.br\/images\/pdf\/2020\/June\/02\/AcuraciaDiagnostico-COVID19-atualizacaoC.pdf"},{"key":"pone.0248920.ref010","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pone.0248920.ref011","doi-asserted-by":"crossref","first-page":"79","DOI":"10.32614\/RJ-2014-008","article-title":"ROSE: a Package for Binary Imbalanced Learning","volume":"6","author":"N Lunardon","year":"2014","journal-title":"The R Journal"},{"key":"pone.0248920.ref012","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10618-017-0517-y","article-title":"Imbalanced classification in sparse and large behaviour datasets","volume":"32","author":"J Vanhoeyveld","year":"2018","journal-title":"Data Min Knowl Disc"},{"key":"pone.0248920.ref013","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1055\/s-0039-1677692","article-title":"Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives","volume":"58","author":"S Rabhi","year":"2019","journal-title":"Methods Inf Med"},{"key":"pone.0248920.ref014","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"D Chicco","year":"2020","journal-title":"BMC Genomics"},{"key":"pone.0248920.ref015","doi-asserted-by":"crossref","unstructured":"Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery; 2015. pp. 1721\u20131730. doi: 10.1145\/2783258.2788613","DOI":"10.1145\/2783258.2788613"},{"key":"pone.0248920.ref016","article-title":"Analysis of COVID-19 under-reporting in Brazil","volume":"32","author":"MF do Prado","year":"2020","journal-title":"Revista Brasileira de Terapia Intensiva"},{"key":"pone.0248920.ref017","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1126\/science.abc0473","article-title":"Rapid implementation of mobile technology for real-time epidemiology of COVID-19","volume":"368","author":"DA Drew","year":"2020","journal-title":"Science"},{"key":"pone.0248920.ref018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-0300-0","article-title":"Estimating the efficacy of symptom-based screening for COVID-19","volume":"3","author":"A Callahan","year":"2020","journal-title":"npj Digital Medicine"},{"key":"pone.0248920.ref019","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1016\/S0140-6736(20)31095-3","article-title":"COVID-19 in Brazil: \"So what?\"","volume":"395","author":"T. Lancet","year":"2020","journal-title":"The Lancet"},{"key":"pone.0248920.ref020","article-title":"COVID-19 and the impact of social determinants of health","author":"EM Abrams","year":"2020","journal-title":"The Lancet Respiratory Medicine"},{"key":"pone.0248920.ref021","doi-asserted-by":"crossref","first-page":"e240","DOI":"10.1016\/S2468-2667(20)30085-2","article-title":"Why inequality could spread COVID-19","volume":"5","author":"F Ahmed","year":"2020","journal-title":"Lancet Public Health."},{"key":"pone.0248920.ref022","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.puhe.2021.01.005","article-title":"Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil","volume":"192","author":"IT Peres","year":"2021","journal-title":"Public Health"},{"key":"pone.0248920.ref023","article-title":"Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data","author":"OT Ranzani","year":"2021","journal-title":"The Lancet Respiratory Medicine"},{"key":"pone.0248920.ref024","article-title":"Symptoms and immunoglobulin development in hospital staff exposed to a SARS-CoV-2 outbreak","author":"S Brandstetter","journal-title":"Pediatric Allergy and Immunology"},{"key":"pone.0248920.ref025","article-title":"Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behavior, and Testing Identifies Contributors to Continued Disease Spread in the United States","author":"WE Allen","year":"2020","journal-title":"medRxiv"},{"key":"pone.0248920.ref026","article-title":"Clinical characteristics of SARS-CoV-2 patients: a French cross-sectional study in primary care","author":"P Sebo","year":"2020","journal-title":"Review"},{"key":"pone.0248920.ref027","first-page":"135","article-title":"Taste and smell as chemosensory dysfunctions in COVID-19 infection","volume":"33","author":"PC Passarelli","year":"2020","journal-title":"Am J Dent"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T15:56:30Z","timestamp":1616687790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0248920"}},"subtitle":[],"editor":[{"given":"Nicola","family":"Perra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":27,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,25]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0248920","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.09.01.20186049","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}