Background
The current COVID-19 pandemic prominently demonstrates the serious threat posed by respiratory infections, not only for the health of individuals, but also for the stability of modern society, in general. While SARS-CoV-2 infections are currently extensively recorded and analysed, future studies must encompass the full breadth of respiratory viruses as has been done in the past. Even before the pandemic, lower track respiratory infections were among the main causes of death in children and adults [
1,
2]. Influenza infection killed between 250,000 and 500,000 people annually, 152,000 deaths were reported in Europe in the 2017–2018 season [
3]. In Germany, during the 2018–2019 season, 182,000 influenza-positive tests were confirmed, including 40,000 from inpatients [
4].
In 2009 the Respiratory Viruses Network (RespVir
www.clinical-virology.net) was founded as an initiative of a Clinical Virology group within the German Virology Society (GfV). The purpose of RespVir is to record respiratory infections in an online database [
5], providing clinicians with up-to-date information about circulating pathogens. The RespVir database contains mainly registries from inpatients data reported by 47 laboratories from university hospitals and a few private. These institutions are located primarily in Germany, Austria, and Switzerland, collecting data from central Europe. Over 12 years, RespVir has analysed more than 280,000 samples with respect to 25 respiratory pathogens (17 viruses and 8 bacteria). Among these years RespVir had obtained data on causal agents of respiratory infections.
RespVir includes records of samples from all patients with respiratory symptoms, sent in by clinicians requesting a diagnosis. Independent of the diagnostic hypothesis of the clinician, each sample was tested in a multiplex manner covering a maximum of 17 respiratory viruses, depending on test availability of each laboratory.
In this study, our aims were (i) to describe the prevalence and seasonal variation (seasonality) for each pathogen, (ii) to assess the prevalence of coinfections and (iii) to determine the rate of exclusion or affinity for pairwise coinfections.
After filtering registries with incomplete data and a post-hoc data quality control. To accomplish our objectives, we performed the analysis in a subset of the RespVir database including 17 different viral pathogens covering the time span from 2010 to 2019.
We observed that 48.64% of all reported respiratory infections are caused by influenza virus. We found four general seasonality patterns. Each of the 17 viruses belongs to one of these patterns. Stratification across years shows biennial seasonality patterns for some viruses, indicating infection peaks every other season. We further observed that coinfections do not occur statistically independently, but that for most virus pairs coinfection is far less frequent than expected by chance.
Discussion
In the present study, we present data from the respiratory pathogens network that has been in place since 2009, based on multicenter, wide-spectrum collection rather than collection of data based on narrowly defined selection criteria. In principle it could be taken as a limitation due to the different clinical criteria by applied by physicians as they request specific diagnostic test. The multiplex test approach in our analysis reduces a possible bias because the samples are tested not only for a single suspected pathogen, but for the 17 respiratory viruses of the multiplex panel.
One disadvantage of this strategy is a lack of clinical historal data. For example, we cannot determine the influence of vaccination rate in our cohort. Nevertheless, the broad coverage (nationwide) of our data allows to assume that the vaccination rate is representative in our cohort.
Filtering and post-hoc curation was required. This was partly necessary due to non-curated data entering the database. To overcome this disadvantage, it is important to use quality control mechanisms during data collection in the future to reject the collection of false data mainly regarding coinfections.
We analysed the frequency of 17 respiratory pathogens with respect to monoinfections and coinfections, their seasonal variation, and the affinity to coinfect with other viruses, spanning 10 years (2010–2019). To our knowledge, this study reports on the largest volume of data of its kind [
4,
8‐
15]. Nevertheless, another limitation in terms of global health is that our samples come mainly from Germany, Austria and Switzerland, these samples constitute a good basis to analyze the Central Europe (continental) region and therefor the the results about saisonality should not be extrapolated to other hemisphere regions, nor even to north Europe, Spain or United kingdom.
Our results confirm previous reports that influenza A viruses, HRSV, and RV were detected most frequently in our cohort [
4,
13,
14,
16,
17]. Nevertheless, almost one quarter (24.65%) of the infections are caused by other respiratory viruses. Although influenza tests are the most frequently performed assays, 51.36% of all positive tests derived from other respiratory viruses. This supports the importance of testing for multiple pathogens for diagnostic purposes [
18‐
20]. A disadvantage of our approach is that relies on routine diagnostic test, therefore influenza typing is restricted to Influenza A (H1N1 and H3N2) and no further data on the subtypes of influenza B are given, Consequently, no detailed specific description of the viruses' behaviour or more specific coinfection relations can be evaluated.
To detect patterns of seasonal variation worldwide, corresponding worldwide and long-term studies are needed [
18,
19]. Our study is robust and covers a long-term period for the central European area. This study allows us to confirm previously reported seasonal patterns [
18‐
22], but also to propose a new seasonal classification of the studied viruses into four groups. Furthermore, we found a typical seasonal pattern repeated every other years for HPIV-1, HPIV-3, HMPV, HRSV, and HCoV-OC43; also, for FLUA(H3N2) except for the years 2010 and 2018 as well as for FLUA(H1N1) except for years 2010 and 2012, respectively. This confirms the constancy of the biennial patterns except for years near to a pandemic event due to new viruses appearance.
The SARS-CoV-2 outbreak has raised questions regarding the seasonal pattern of this virus. Studies on seasonal patterns of endogenous viruses could help to solve these questions. We found a slight difference in the seasonal profile within the coronaviruses (Figure S1). SARS-CoV-2 belongs to beta-coronaviruses which season usually starts in November–December and has its peak in December-January. Thus, a similar seasonality could be expected for SARS-CoV-2 in the future.
Coinfection modifies the natural history of diseases caused by single infections. Thus, deeper understanding of coinfections, especially the exclusion mechanisms could help the development of antivirals [
23]. Only a few large-scale data analyses on virus-virus interactions exist, in contrast to numerous studies on bacterial coinfections and virus-bacteria studies [
24‐
28]. We characterized the coinfection prevalence and the interactions between 17 different viruses and analysed 7,790,879 tests combinations, within the ten year observation period. To our knowledge, our study provides the analysis of virus-virus interaction with the largest diversity of respiratory viruses, the longest surveillance period, and the largest number of tests performed.
As expected, the most prevalent coinfection virus pairs and the highest positivity percentage (Table S1) had also a high monoinfection prevalence and a seasonal overlap. To compare the propensity of a virus pair to coinfect, we introduced a coinfection exclusion score (CES). To exclude bias due to seasonal effects, we calculated an average coinfection exclusion score (ACES).
One of the most relevant studies of virus-virus interaction has been performed by Nickbakhsh et. al. [
29], who analysed 44,230 respiratory illness cases tested for 11 viruses over nine years and classified the viral pairs interactions. Our data confirm a strong exclusion of any of the influenza A strains (H1N1 or H3N2) to coinfect with rhinovirus. This exclusion has been confirmed also in an animal model [
30]. Our data also confirm an exclusion between FLUB and HAdV. In contrary to Nickbakhsh et al. [
29], our data suggest strong exclusion for HRSV and HMPV coinfection and no significant interaction between HPIV-2 with HPIV-3. Nickbakhsh et al. [
29] did not report any other interaction, while our data shows the strongest exclusion for FLUA(H1N1) and FLUB as well as for FLUA(H3N2) and FLUB. For the other virus pairs the numbers are too small to test for significance. So, further studies are needed to get more insight into the frequency ant role of virus co-infections.
Acknowledgements
The RespVir-Network is supported by the Gesellschaft für Virology, Deutsche Vereinigung zur Bekämpfung der Viruskrankheiten e.V., and Paul Ehrlich Gesellschaft für Chemotherapie e.V. Respiratory Virus Network
Rolf Kaiser, PhD1, Barbara C. Gärtner, MD8,
Benedikt Weissbrich, PhD9, Ortwin Adams, MD11, Annemarie Berger, Dr. med. vet.12, Katrin Palupsky13, Daniela Huzly, MD14, Carsten
Tiemann, PhD15, Wegene Borena, PhD16, Andreas Lindauer,
PhD17, Uwe Gerd Liebert, PhD18, Hans-Joachim Siemens, MD19,
Jörg Hofmann20, Anna-Maria Eis-Hübinger, PhD21, Hajo
Grundmann, MD22, Astrid Kehlen, PhD23, Albrecht Oehme, MD23,
Paul Schnitzler, PhD24, Joachim Kühn, MD25, Albert Heim,
PhD26, Andreas Sauerbrei, MD27, Barbara Schmidt, MD28,
Robert Beck, MD29, Dieter Hoffmann, MD30, Detlef Michel,
PhD31, Hans Nitschko, PhD32, Christian Aepinus,PhD33,
Jens Dreier,PhD34, Elisabeth Puchhammer-Stoeckl, PhD35,
Theresa Popow Kaup, PhD35,
Monika Redlberger,PhD35, Harald Kessler,MD36,
Martin Obermeier, MD37, Kerstin Weise,PhD38, Patricia
Bartsch39, Annette Devide, MD40, Bert Niesters, PhD41, Michael Kleines, PhD42, Andi Krumbholz, MD43,
Thomas Meyer,, PhD44, Peter Gohl,, PhD45, Christian G.
Schüttler, MD46, Meri Gorgievski, MD47, Andres Anton
Pagarolas, PhD48, Wolfgang Gulich49, Thomas Ziegler,PhD50,
Babett Wintsche,PhD51, Marcena Griego52, Walter Bossart,
PhD53. 1Institute of Virology, Faculty of Medicine and University
Hospital Cologne, Germany
8Institute of Medicine Microbiology and Hygiene,
University of the Saarland Kirrberger Homburg/Saar, Germany
9Faculty of Medicine, Institute for Virology and
Immunobiology, Würzburg University, Würzburg, Germany,
11University of Düsseldorf, Medical Faculty, Institute for
Virology, Düsseldorf, Germany,
12 Institut für Medizinische Virologie -
Universitätsklinikum Frankfurt, Frankfurt, Germany,
13Institut für Virologie – Universitätsklinikum
Essen, Essen, Germany,
14Institut für Virologie - Universitätsklinikum
Freiburg, Freiburg, Germany,
15Labor Krone GbR - Medizinal-Untersuchungsstelle im
Regierungsbezirk Detmold, Bad Salzuflen, Germany,
16 Institut für Virologie - Medizinische Universität
Innsbruck, Innsbruck, Austria,
17SYNLAB Medizinisches Versorgungszentrum Weiden
GmbH, Weiden in der Oberpfalz, Germany,
18 Institut für Virologie - Universität Leipzig,
Leipzig, Germany,
19MVZ Institut für Medizinische Mikrobiologie, Infektiologie, Hygiene und Tropenmedizin
GmbH, Germany,
20Institut für Virologie, Charité -
Universitätsmedizin Berlin, Berlin Germany,
21Institut für Virologie - Universitätsklinikum Bonn,
Bonn Germany,
22Institut für Infektionsprävention und
Krankenhaushygiene - Universitätsklinikum Freiburg, Freiburg Germany,
23Institut für Medizinische Mikrobiologie -
Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany,
24Department für Infektiologie, Virologie -
Universitätsklinikum Heidelberg, Heidelberg, Germany,
25Institut für Medizinische Mikrobiologie, Klinische
Virologie - Universitätsklinikum Münster, Münster, Germany,
26Institut für Virologie und Seuchenhygiene -
Medizinische Hochschule Hannover, Hannover, Germany,
27Institut für Virologie und Antivirale Therapie -
Universitätsklinikum Jena, Jena, Germany,
28Institut für Mikrobiologie und Hygiene -
Universitätsklinikum Regensburg, Regensburg, Germany,
29Institut für Medizinische Virologie und
Epidemiologie der Viruskrankheiten - Uniklinikum Tübingen, Tübingen, Germany,
30Institut für Virologie - Technische Universität
München, München, Germany,
31Institut für Virologie - Universitätsklinikum Ulm,
32Max-von-Pettenkofer-Institut für Hygiene und
Medizinische Mikrobiologie - Universität München, München, Germany,
33Institut für Virologie - Universität Marburg,
Marburg, Germany,
34Institut für Laboratoriums- und Transfusionsmedizin
- Universitätsklinik Bochum, Bochum, Germany,
35Department für Virologie - Medizinische Universität
Wien, Vienna, Austria,
36Institut für Hygiene, Mikrobiologie und
Umweltmedizin - Medizinische Universität Graz, Graz, Austria,
37MIB, Medizinisches Infektiologiezentrum Berlin,
Berlin, Germany,
38Institut für Virologie - Universitätsmedizin Mainz,
Mainz, Germany,
39Medizinisches Versorgungszentrum Dr. Eberhard &
Partner Dortmund, Dortmund, Germany,
40Institut für Medizinische Mikrobiologie, Virologie
und Hygiene - Universitätsmedizin Rostock, Rostock, Germany,
41Laboratory of Clinical Virology - University of
Gröningen, Gröningen, Germany,
42Labordiagnostisches Zentrum - Uniklinik RWTH
Aaachen, Aachen, Germany,
43Labor Dr. Krause und Kollegen MVZ GmbH, Kiel,
Germany,
44Medizinische Mikrobiologie, Virologie und Hygiene -
Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany,
45Bioscientia Institut für Medizinische Diagnostik
GmbH - Labor Ingelheim,, Ingelheim, Germany,
46Medizinische Virologie - Universitätsklinikum
Giessen und Marburg GmbH, Marburg, Germany,
47Institut für Infektionskrankheiten - Universität
Bern, Bern, Switzerland,
48Microbiology Department - Hospital Universitari
Vall d‘Hebron, Barcelona, Spain,
49Medizinisches Labor Ostsachsen - Labor Görlitz,
Görlitz, Germany,
50Institut für Medizinische Diagnostik Berlin-Potsdam
MVZ GbR, Berlin, Germany,
51 Gemeinschaftslabor Cottbus
MVZ GbR, Cottbus, Germany,
52Praxis für Labormedizin und Mikrobiologie, Bochum,
Germany,
53Institut für Medizinische Virologie - Universität
Zürich, Zürich, Switzerland.