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. 2022 Jun 11;22(1):1167.
doi: 10.1186/s12889-022-13555-5.

Respiratory viruses dynamics and interactions: ten years of surveillance in central Europe

Gibran Horemheb-Rubio #  1   2   3 , Ralf Eggeling #  4 , Norbert Schmeiβer  5 , Nico Pfeifer  4   6   7   8 , Thomas Lengauer  1   8 , Barbara C Gärtner  9 , Christiane Prifert  10 , Matthias Kochanek  11 , Christoph Scheid  11 , Ortwin Adams  12 , Rolf Kaiser #  13   14 ; Respiratory Virus Network
Collaborators, Affiliations

Respiratory viruses dynamics and interactions: ten years of surveillance in central Europe

Gibran Horemheb-Rubio et al. BMC Public Health. .

Abstract

Background: Lower respiratory tract infections are among the main causes of death. Although there are many respiratory viruses, diagnostic efforts are focused mainly on influenza. The Respiratory Viruses Network (RespVir) collects infection data, primarily from German university hospitals, for a high diversity of infections by respiratory pathogens. In this study, we computationally analysed a subset of the RespVir database, covering 217,150 samples tested for 17 different viral pathogens in the time span from 2010 to 2019.

Methods: We calculated the prevalence of 17 respiratory viruses, analysed their seasonality patterns using information-theoretic measures and agglomerative clustering, and analysed their propensity for dual infection using a new metric dubbed average coinfection exclusion score (ACES).

Results: After initial data pre-processing, we retained 206,814 samples, corresponding to 1,408,657 performed tests. We found that Influenza viruses were reported for almost the half of all infections and that they exhibited the highest degree of seasonality. Coinfections of viruses are frequent; the most prevalent coinfection was rhinovirus/bocavirus and most of the virus pairs had a positive ACES indicating a tendency to exclude each other regarding infection.

Conclusions: The analysis of respiratory viruses dynamics in monoinfection and coinfection contributes to the prevention, diagnostic, treatment, and development of new therapeutics. Data obtained from multiplex testing is fundamental for this analysis and should be prioritized over single pathogen testing.

Keywords: Coinfection; Respiratory viruses; Seasonality; Surveillance; Viral exclusion.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of RespVir Network. The figure shows a European map with the location of the 47 laboratory members of RespVir Network. These laboratories are located in the following countries: Germany, Austria, Switzerland, Netherlands, and Spain
Fig. 2
Fig. 2
Seasonality Profile of the Respiratory Viruses. The figure shows the seasonality profile of the 17 respiratory viruses studied. a) Degree of seasonality of each virus calculated by Kullback–Leibler divergence, where zero indicates no seasonality (see Methods, seasonality profile). b) Average linkage clustering of the 17 viruses according to their seasonality profile. c) The seasonal four groups according to the similarities of the 17 viruses, the figure shows the seasonal profile of one virus per group and the group name
Fig. 3
Fig. 3
Annual variation of seasonality. The figure shows the annual variation of seasonality and the biennial pattern discovered. a) Biennial pattern of HCoV-OC43 exhibiting high infection numbers at the end of even and beginning of odd years, but low infection numbers at the end of odd and beginning of even years. b) Hierarchical clustering with average linkage of the annual variation of seasonality of HCoV-OC43. c) Hierarchical clustering with average linkage of the annual variation of seasonality of FLUA(H3N2). d) Hierarchical clustering with average linkage of the annual variation of seasonality of FLUA(H1N1)
Fig. 4
Fig. 4
Interaction strength between the 17 virus pairs regarding coinfection. The figure shows the 17 studied viruses linked by lines. Orange lines indicate an exclusion interaction while green lines an affinity interaction. The thickness of the lines indicates the strength of the interaction
See this image and copyright information in PMC

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