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Respiratory viruses dynamics and interactions: ten years of surveillance in central Europe.
Horemheb-Rubio, Gibran; Eggeling, Ralf; Schmeißer, Norbert; Pfeifer, Nico; Lengauer, Thomas; Gärtner, Barbara C; Prifert, Christiane; Kochanek, Matthias; Scheid, Christoph; Adams, Ortwin; Kaiser, Rolf.
  • Horemheb-Rubio G; Institute of Virology, Faculty of Medicine and University Hospital Cologne, Köln, Germany.
  • Eggeling R; Department of Infectious Diseases, Instituto Nacional de Ciencias Médicas Y Nutrición Salvador Zubirán, Mexico City, Mexico.
  • Schmeißer N; DZIF, Center for Infection Research, partner site Cologne Bonn, Cologne, Germany.
  • Pfeifer N; Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.
  • Lengauer T; Medeora GmbH, Cologne, Germany.
  • Gärtner BC; Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.
  • Prifert C; Faculty of Medicine, University of Tübingen, Tübingen, Germany.
  • Kochanek M; German Center for Infection Research, Partner Site Tübingen, Tübingen, Germany.
  • Scheid C; Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
  • Adams O; Institute of Virology, Faculty of Medicine and University Hospital Cologne, Köln, Germany.
  • Kaiser R; Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
BMC Public Health ; 22(1): 1167, 2022 06 11.
Artículo en Inglés | MEDLINE | ID: covidwho-1885294
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.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / Virus / Virosis / Coinfección Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico Límite: Humanos / Lactante Idioma: Inglés Revista: BMC Public Health Asunto de la revista: Salud Pública Año: 2022 Tipo del documento: Artículo País de afiliación: S12889-022-13555-5

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / Virus / Virosis / Coinfección Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico Límite: Humanos / Lactante Idioma: Inglés Revista: BMC Public Health Asunto de la revista: Salud Pública Año: 2022 Tipo del documento: Artículo País de afiliación: S12889-022-13555-5