Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic

  1. Leander Melms
  2. Evelyn Falk
  3. Bernhard Schieffer
  4. Andreas Jerrentrup
  5. Uwe Wagner
  6. Sami Matrood
  7. Jürgen R. Schaefer
  8. Tobias Müller
  9. Martin Hirsch

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Abstract

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

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  1. Version published to 10.1371/journal.pone.0258649
  2. ScreenIT

    SciScore for 10.1101/2021.02.01.21250537: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: Informed consent for scientific evaluation was provided by accepting the privacy policy (explicitly asked before submitting the questionnaire, see point 3.1 in the privacy policy).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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:
    Limitations of the study: Our work has numerous limitations. One major limitation of our study is that we do not have data on confirmatory laboratory test results of the study participants. Because of this, we cannot extend and/or adopt the proposed model by Drew et al. by means of, for instance machine learning. Notably though, the same symptom combination was the most discriminative between the groups with and without confirmed contact, which suggests that the proposed combination might indeed achieve the highest specifity. In addition, we recognize that the data collected originates from a modern web application. Although a balanced gender and age distribution was achieved in the study population, it can be assumed that less technology-affine individuals are underrepresented in the study collective, which means that a contributor bias cannot be excluded. Thus, it cannot be ruled out that certain symptom constellations are more likely to be significant than others, which may be more common in underrepresented user groups. Furthermore, the nature of the collected data is self-reported. Data validity is therefore not checked and may contain false statements. Another limitation is that since users cannot create an account for reasons of data security and privacy, only snapshots of symptoms can be collected and cannot be tracked over time (i.e. longitudinal data). Also, our data and calculations are based on submissions of the questionnaire and do not strictly correlate with th...

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2021.02.01.21250537v1 on medRxiv