TY  - JOUR
A1  - Munsch, Nicolas
A1  - Gruarin, Stefanie
A1  - Nateqi, Jama
A1  - Lutz, Thomas
A1  - Binder, Michael
A1  - Aberle, Judith H.
A1  - Martin, Alistair
A1  - Knapp, Bernhard
T1  - Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
JF  - Wiener Klinische Wochenschrift / The Central European Journal of Medicine
N2  - 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.
KW  - COVID-19
KW  - Chatbot
KW  - Machine learning
KW  - Self-reported
KW  - Symptom assessment
Y1  - 
VL  - 2022
IS  - 134 (9-10)
SP  - 344
EP  - 350
PB  - Springer
ER  - 
TY  - JOUR
A1  - Simböck, Elisabeth
A1  - Marksteiner, Jessica
A1  - Machacek, Thomas
A1  - Wiessner, Katharina
A1  - Gepp, Barbara
A1  - Jesenberger, Veronika
A1  - Weihs, Anna
A1  - Leitner, Rita
T1  - The Power of Problem Based Learning beyond its Didactic Attributes
JF  - Journal of Problem Based Learning in Higher Education (JPBLHE)
N2  - Hybrid courses with a focus on practice-orientated education and self-guided learning phases are on the rise on the higher education sector. Disciplines in Life Sciences implicate a high degree of practical laboratory expertise. The University of Applied Sciences (UAS) in Vienna, Austria, has thus been endeavoured offering students a high qualitative education integrating hybrid courses based on PBL principles, which consist of on-site (including the transmission of necessary background and practical laboratory training) and off-site (including self-study phases) sessions. As practical laboratory units are central in those courses, the restrictive measures, including the transition to a complete online teaching format due to the first Covid-19-pandemic lock-down, had severe effects on the implementation and the quality of the curriculum. According to surveys made specifically to address this problematic situation, it can be concluded that on-site practical units are fundamental for certain disciplines such as Life Sciences.
KW  - Problem-based Learning
KW  - Life Science didactics
KW  - Hybrid PBL-methods
KW  - COVID-19
KW  - Life Science Education
Y1  - 
VL  - 9
IS  - 1
SP  - 109
EP  - 130
ER  - 
TY  - JOUR
A1  - Munsch, Nicolas
A1  - Martin, Alistair
A1  - Gruarin, Stefanie
A1  - Nateqi, Jama
A1  - Abdarahmane, Isselmou
A1  - Weingartner-Ortner, Rafael
A1  - Knapp, Bernhard
T1  - Authors’ Reply to: Screening Tools: Their Intended Audiences and Purposes. Comment on “Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study”
JF  - Journal of Medical Internet Research
KW  - COVID-19
KW  - Symptom Checkers
KW  - Digital Health
Y1  - 
IS  - Vol 23, No 5 (2021)
ER  -