Quotation Hirk, Rainer, Kastner, Gregor, Vana, Laura. 2020. Investigating the Dark Figure of COVID-19 Cases in Austria: Borrowing From the Decode Genetics Study in Iceland. Austrian Journal of Statistics. 49 (5), 1-17.




The number of undetected cases of SARS-CoV-2 infections is expected to be a multiple of the reported figures mainly due to the assumed high proportion of asymptomatic infections and to limited availability of trustworthy testing resources. Relying on the deCODE genetics study in Iceland, which offers large scale testing among the general population, we investigate the magnitude and uncertainty of the number of undetected cases COVID-19 cases in Austria. We formulate several scenarios relying on data on the number of COVID-19 cases which have been hospitalized, in intensive care, as well as on the number of deaths and positive tests in Iceland and Austria. We employ frequentist and Bayesian methods for estimating the dark figure in Austria based on the hypothesized scenarios and for accounting for the uncertainty surrounding this figure. Using data available on April 01, 2020, our study contains two main findings: First, we find the estimated number of infections to be on average around 8.35 times higher than the recorded number of infections. Second, the width of the uncertainty bounds associated with this figure depends highly on the statistical method employed. At a 95% level, lower bounds range from 3.96 to 6.83 and upper bounds range from 9.82 to 12.61. Overall, our findings confirm the need for systematic tests in the general population of Austria.


Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Austrian Journal of Statistics
Language English
Title Investigating the Dark Figure of COVID-19 Cases in Austria: Borrowing From the Decode Genetics Study in Iceland
Volume 49
Number 5
Year 2020
Page from 1
Page to 17
Reviewed? Y
URL https://doi.org/10.17713/ajs.v49i4.1142
DOI n.a.
Open Access Y
Open Access Link https://doi.org/10.17713/ajs.v49i4.1142


High-dimensional statistical learning: New methods to advance economic and sustainability policies
Hirk, Rainer (Details)
Kastner, Gregor (Details)
Vana, Laura (Details)
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
Google Scholar: Search