@article{MunschGruarinNateqietal.,
  author    = {Munsch, Nicolas and Gruarin, Stefanie and Nateqi, Jama and Lutz, Thomas and Binder, Michael and Aberle, Judith H. and Martin, Alistair and Knapp, Bernhard},
  title     = {Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna},
  series = {Wiener Klinische Wochenschrift / The Central European Journal of Medicine},
  volume    = {2022},
  journal   = {Wiener Klinische Wochenschrift / The Central European Journal of Medicine},
  number    = {134 (9-10)},
  publisher = {Springer},
  pages     = {344 -- 350},
  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.},
  subject      = {COVID-19},
  language  = {en}
}