Rhinovirus prevalence as indicator for efficacy of measures against SARS-CoV-2

GND
1229951075
ORCID
0000-0003-2909-5376
LSF
59762
Zugehörige Organisation
Bioinformatics and Computational Biophysics, Faculty of Biology and Centre for Medical Biotechnology (ZMB), University of Duisburg-Essen, Essen, Germany
Kitanovski, Simo;
Zugehörige Organisation
Department of Infectious Diseases, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
Horemheb-Rubio, Gibran;
GND
113836651X
Zugehörige Organisation
Institute of Virology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
Adams, Ortwin;
GND
1219090867
ORCID
0000-0002-5234-7634
Zugehörige Organisation
Institute of Medical Microbiology and Hygiene, Saarland University Medical Center, Homburg, Germany
Gärtner, Barbara;
GND
1021379298
ORCID
0000-0003-3801-2640
Zugehörige Organisation
Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
Lengauer, Thomas;
GND
1021044180
ORCID
0000-0003-2973-7869
LSF
16263
Zugehörige Organisation
Bioinformatics and Computational Biophysics, Faculty of Biology and Centre for Medical Biotechnology (ZMB), University of Duisburg-Essen, Essen, Germany
Hoffmann, Daniel;
Zugehörige Organisation
Institute of Virology, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
Kaiser, Rolf

Background: Non-pharmaceutical measures to control the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) should be carefully tuned as they can impose a heavy social and economic burden. To quantify and possibly tune the efficacy of these anti-SARS-CoV-2 measures, we have devised indicators based on the abundant historic and current prevalence data from other respiratory viruses.

Methods: We obtained incidence data of 17 respiratory viruses from hospitalized patients and outpatients collected by 37 clinics and laboratories between 2010-2020 in Germany. With a probabilistic model for Bayes inference we quantified prevalence changes of the different viruses between months in the pre-pandemic period 2010-2019 and the corresponding months in 2020, the year of the pandemic with noninvasive measures of various degrees of stringency.

Results: We discovered remarkable reductions δ in rhinovirus (RV) prevalence by about 25% (95% highest density interval (HDI) [−0.35,−0.15]) in the months after the measures against SARS-CoV-2 were introduced in Germany. In the months after the measures began to ease, RV prevalence increased to low pre-pandemic levels, e.g. in August 2020 δ =−0.14 (95% HDI [−0.28,0.12]).

Conclusions: RV prevalence is negatively correlated with the stringency of anti-SARS-CoV-2 measures with only a short time delay. This result suggests that RV prevalence could possibly be an indicator for the efficiency for these measures. As RV is ubiquitous at higher prevalence than SARS-CoV-2 or other emerging respiratory viruses, it could reflect the efficacy of noninvasive measures better than such emerging viruses themselves with their unevenly spreading clusters.

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© The Author(s) 2021

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