tetano
Editor, Senior Moderator
Wien Klin Wochenschr
. 2022 Apr 13.
doi: 10.1007/s00508-022-02028-9. Online ahead of print.
Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
Nicolas Munsch[SUP] 1 [/SUP], Stefanie Gruarin[SUP] 2 [/SUP], Jama Nateqi[SUP] 2 3 [/SUP], Thomas Lutz[SUP] 2 [/SUP], Michael Binder[SUP] 4 [/SUP], Judith H Aberle[SUP] 5 [/SUP], Alistair Martin[SUP] 1 [/SUP], Bernhard Knapp[SUP] 6 7 [/SUP]
Affiliations
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.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.
. 2022 Apr 13.
doi: 10.1007/s00508-022-02028-9. Online ahead of print.
Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
Nicolas Munsch[SUP] 1 [/SUP], Stefanie Gruarin[SUP] 2 [/SUP], Jama Nateqi[SUP] 2 3 [/SUP], Thomas Lutz[SUP] 2 [/SUP], Michael Binder[SUP] 4 [/SUP], Judith H Aberle[SUP] 5 [/SUP], Alistair Martin[SUP] 1 [/SUP], Bernhard Knapp[SUP] 6 7 [/SUP]
Affiliations
- PMID: 35416543
- DOI: 10.1007/s00508-022-02028-9
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.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.