App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
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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.
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SciScore for 10.1101/2020.09.01.20186049: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study presents some limitations. First, the symptoms are self-reported. Hence, the participant may report apparent manifestations of the disease, which may not be precise as physiological evaluation by a physician. Second, we were unable to know when a symptom appeared to indicate the stage of the disease at the testing moment. …
SciScore for 10.1101/2020.09.01.20186049: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study presents some limitations. First, the symptoms are self-reported. Hence, the participant may report apparent manifestations of the disease, which may not be precise as physiological evaluation by a physician. Second, we were unable to know when a symptom appeared to indicate the stage of the disease at the testing moment. Third, a non-negligible number of false negatives may be present, considering the sensitivity of the serological test. However, identification of potential clusters and optimization of testing resources using a combination of self-reported symptoms is a viable strategy for many countries. A similar combination of symptoms can explain the SARS-CoV-2 infections in developed countries, such as the United Kingdom and the United States, as well as in LMIC such as Brazil.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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