• FluTrackers.com Inc. does not provide medical advice. Information on this web site is collected from various internet resources, and the FluTrackers board of directors makes no warranty to the safety, efficacy, correctness or completeness of the information posted on this site by any author or poster. The information collated here is for instructional and/or discussion purposes only and is NOT intended to diagnose or treat any disease, illness, or other medical condition. Every individual reader or poster should seek advice from their personal physician/healthcare practitioner before considering or using any interventions that are discussed on this website. By continuing to access this website you agree to consult your personal physican before using any interventions posted on this website, and you agree to hold harmless FluTrackers.com Inc., the board of directors, the members, and all authors and posters for any effects from use of any medication, supplement, vitamin or other substance, device, intervention, etc. mentioned in posts on this website, or other internet venues referenced in posts on this website.
  • We are not asking for any donations. Do not donate to any entity who says they are raising funds for us.

BMC Public Health . Rhinovirus prevalence as indicator for efficacy of measures against SARS-CoV-2

tetano

Editor, Senior Moderator
BMC Public Health


. 2021 Jun 21;21(1):1178.
doi: 10.1186/s12889-021-11178-w.
Rhinovirus prevalence as indicator for efficacy of measures against SARS-CoV-2


Simo Kitanovski[SUP] #[/SUP][SUP] 1 [/SUP], Gibran Horemheb-Rubio[SUP] #[/SUP][SUP] 2 3 [/SUP], Ortwin Adams[SUP] 4 [/SUP], Barbara Gärtner[SUP] 5 [/SUP], Thomas Lengauer[SUP] 6 [/SUP], Daniel Hoffmann[SUP] 7 [/SUP], Rolf Kaiser[SUP] 2 [/SUP], Respiratory Virus Network



Affiliations

Abstract

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.

Keywords: Bayesian; COVID-19; Germany; Modeling; Rhinovirus; SARS-CoV-2.
 
Back
Top Bottom