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. 2025 Jun 13;83(1):152.
doi: 10.1186/s13690-025-01655-8.

COVID-19 RT-qPCR-based screening in Austrian schools and incidences in the general population: a Bayesian spatiotemporal analysis

Yessika Adelwin Natalia  1 , Sereina Annik Herzog  2 , Thomas Neyens  3   4 , Christoph Johann Zurl  5 , Volker Strenger  5 , Geert Molenberghs  3   4 , Christel Faes  3
Affiliations

COVID-19 RT-qPCR-based screening in Austrian schools and incidences in the general population: a Bayesian spatiotemporal analysis

Yessika Adelwin Natalia et al. Arch Public Health. .

Abstract

In 2021, the emergence of highly transmissible COVID-19 variants of concern increased susceptibility among younger populations. Despite this risk, face-to-face education remained essential for societal functioning and children's well-being, prompting the Austrian government to implement a nationwide screening program in educational institutions. This study explores the impact of this program on COVID-19 transmission by examining the relationship between incidence rates and factors such as age, vaccination coverage, and RT-qPCR positivity rates among school-aged children across Austrian districts, using a Bayesian spatiotemporal discrete model. Our findings highlight significant effects of vaccination and positivity rates on COVID-19 incidence, with variations in their influence across different age groups and locations. These results underscore the importance of monitoring these variables, particularly when active screening programs are in place.

Keywords: Austria; COVID-19; RT-qPCR; School; Screening; Spatiotemporal analysis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Written informed consent to participate in the school screening program was obtained from the participants (children and/or their guardians). The Federal Ministry of Education, Science, and Research collected anonymized data as part of the program. For this study, only aggregated, anonymized RT-qPCR data were utilized, ensuring that no individual-level information was accessible. Consequently, ethical approval was not required for the analysis of these pre-existing anonymized data. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Border of districts and federal states in Austria from September 13, 2021, to January 2, 2022
Fig. 2
Fig. 2
Posterior predictive check based on PIT histogram for the initial model among school children in Austria, from September 13, 2021, to January 2, 2022
Fig. 3
Fig. 3
Effect of age, vaccination coverage, and positivity rate on predicted COVID-19 incidence among school children in Austria, from September 13, 2021, to January 2, 2022. Green color indicates high vaccination coverage (≥50%) in the corresponding age group and yellow color indicates low vaccination coverage (<50%). The y-axis range varies across age groups for clarity of the visualization. PI = predicted interval
Fig. 4
Fig. 4
Posterior COVID-19 incidence rate (IR) per 1,000 children at the district level in Austria, from September 13, 2021, to January 2, 2022. The border of each federal state is indicated with black lines. The federal state of Vienna, excluded from the analysis, is marked with black fill
Fig. 5
Fig. 5
Exceedance probability at the district level in Austria, from September 13, 2021, to January 2, 2022. IR = incidence rate per 1,000 children. The border of each federal state is indicated with black lines. The federal state of Vienna, excluded from the analysis, is marked with black fill
Fig. 6
Fig. 6
Percentage of cold spots and hot spots in each federal state of Austria from September 13, 2021, to January 2, 2022. The holiday period in Burgenland, Upper Austria, Styria, and Vorarlberg is marked with red dashed lines. The holiday period in Carinthia, Lower Austria, Salzburg, and Tyrol is marked with black dashed lines
See this image and copyright information in PMC

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