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

被引:5
|
作者
Munsch, Nicolas [1 ]
Gruarin, Stefanie [2 ]
Nateqi, Jama [2 ,3 ]
Lutz, Thomas [2 ]
Binder, Michael [4 ]
Aberle, Judith H. [5 ]
Martin, Alistair [1 ]
Knapp, Bernhard [1 ,6 ]
机构
[1] Symptoma GmbH, Sci Dept, Vienna, Austria
[2] Symptoma GmbH, Sci Dept, Salzburg, Austria
[3] Paracelsus Med Univ, Dept Internal Med, Salzburg, Austria
[4] Vienna Hlth Care Co, Vienna, Austria
[5] Med Univ Vienna, Ctr Virol, Vienna, Austria
[6] Univ Appl Sci Technikum, Fac Comp Sci, Vienna, Austria
关键词
Symptom assessment; Self-reported; Symptom checker; Chatbot; Machine learning;
D O I
10.1007/s00508-022-02028-9
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页码:344 / 350
页数:7
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