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Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
- 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.
Author: | Nicolas Munsch, Stefanie Gruarin, Jama Nateqi, Thomas Lutz, Michael Binder, Judith H. Aberle, Alistair Martin, Bernhard Knapp |
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Parent Title (English): | Wiener Klinische Wochenschrift / The Central European Journal of Medicine |
Publisher: | Springer |
Document Type: | Article |
Language: | English |
Completed Date: | 2022/05/01 |
Responsibility for metadata: | Fachhochschule Technikum Wien |
Release Date: | 2023/01/10 |
GND Keyword: | COVID-19; Chatbot; Machine learning; Self-reported; Symptom assessment |
Volume: | 2022 |
Issue: | 134 (9-10) |
Pagenumber: | 7 |
First Page: | 344 |
Last Page: | 350 |
Publish on Website: | 1 |
Open Access: | 1 |
Reviewed: | 0 |
Link to Publication: | https://pubmed.ncbi.nlm.nih.gov/35416543/ |
Department: | Department Computer Science |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit |
0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik | |
Research Focus: | Data-Driven, Smart & Secure Systems |
Sonstiges | |
Projects: | Import |
Studienjahr: | 2021/2022 |