App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.
PLoS One
; 16(3): e0248920, 2021.
Article
in English
| MEDLINE | ID: covidwho-1150550
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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 ANDMETHODS:
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Machine Learning
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pone.0248920
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