Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna

  1. Nicolas Munsch
  2. Stefanie Gruarin
  3. Jama Nateqi
  4. Thomas Lutz
  5. Michael Binder
  6. Judith H. Aberle
  7. Alistair Martin
  8. Bernhard Knapp

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Abstract

Background

Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.

Methods

The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna’s official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.

Results

Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.

Conclusion

This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.

Article activity feed

  1. Version published to 10.1007/s00508-022-02028-9
  2. Version published to 10.1101/2021.02.24.21252426v4 on medRxiv
  3. Version published to 10.1101/2021.02.24.21252426v3 on medRxiv
  4. Version published to 10.1101/2021.02.24.21252426v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2021.02.24.21252426: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableUsers who did not provide a sex information were exlcuded from the symptom frequencies comparison between female and male.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were done in Python 3.8 using the libraries numpy (1.19.4), pandas (1.1.5), scikit-learn (0.24.0) and statsmodels (0.12.1).
    Python
    suggested: (IPython, RRID:SCR_001658)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, our study also has limitations: there is a selection bias for participants as old people are unlikely to use a chatbot (see Table 1 and ​[11]​) and there is an overrepresentation of female participants in this study (45.0% male and 55.0% female). Also, the dyspnea frequency and difference reported here might be due to a sample bias as (a) dyspnea is often a late symptom of an infection while chat-bot users might rather be at an earlier stage of an infection and (b) dyspnea can be a distressing symptom and affected individuals might rather call an emergency hotline instead of using a chatbot ​[12]​. Data Availability All relevant data is reported within the study. Funding This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 830017 and by the Austrian Research Promotion Agency under grant agreement No 880939 (supported by the Federal Ministries Republic of Austria for Digital and Economic Affairs and Climate Action, Environment, Energy, Mobility, Innovation and Technology). Declaration of interests NM, SG, AM, JN, TL and BK are employees of Symptoma GmbH. JN and TL hold shares of Symptoma. Ethical considerations This study was exempted from ethics review by the ethics commission of the city of Vienna (MA15-EK/21-037-VK_NZ). All individuals using the chatbot agreed that their data will be used in an anonymised way.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  6. Version published to 10.1101/2021.02.24.21252426v1 on medRxiv