In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 3 ( 2021-3-25), p. e0248920-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0248920
DOI:
10.1371/journal.pone.0248920.g001
DOI:
10.1371/journal.pone.0248920.g002
DOI:
10.1371/journal.pone.0248920.g003
DOI:
10.1371/journal.pone.0248920.g004
DOI:
10.1371/journal.pone.0248920.g005
DOI:
10.1371/journal.pone.0248920.t001
DOI:
10.1371/journal.pone.0248920.t002
DOI:
10.1371/journal.pone.0248920.s001
DOI:
10.1371/journal.pone.0248920.s002
DOI:
10.1371/journal.pone.0248920.s003
DOI:
10.1371/journal.pone.0248920.s004
DOI:
10.1371/journal.pone.0248920.s005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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