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  - 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  - 
TY  - JOUR
A1  - Marc, Zobel
A1  - Knapp, Bernhard
A1  - Nateqi, Jama
A1  - Martin, Alistair
T1  - Correlating global trends in COVID-19 cases with online symptom checker self-assessments
JF  - PLOS ONE
N2  - Background

Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user’s symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced.
Objectives

Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections.
Materials and methods

In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset’s spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries.
Results

We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries.
Conclusion

We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.
KW  - Online symptom checkers
KW  - coronavirus disease
Y1  - 
U6  - http://dx.doi.org/https://doi.org/10.1371/journal.pone.0281709
VL  - 18
IS  - 2
ER  -