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Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control

Author

Listed:
  • Sarah C. Kramer

    (Campus Charité Mitte)

  • Sarah Pirikahu

    (Campus Charité Mitte)

  • Jean-Sébastien Casalegno

    (Laboratoire de Virologie
    Hôpital de la Croix-Rousse
    École Normale Supérieure de Lyon)

  • Matthieu Domenech de Cellès

    (Campus Charité Mitte)

Abstract

Pathogen-pathogen interactions represent a critical but little-understood feature of infectious disease dynamics. In particular, experimental evidence suggests that influenza virus and respiratory syncytial virus (RSV) compete with each other, such that infection with one confers temporary protection against the other. However, such interactions are challenging to study using common epidemiologic methods. Here, we use a mathematical modeling approach, in conjunction with detailed surveillance data from Hong Kong and Canada, to infer the strength and duration of the interaction between influenza and RSV. Based on our estimates, we further utilize our model to evaluate the potential conflicting effects of live attenuated influenza vaccines (LAIV) on RSV burden. We find evidence of a moderate to strong, negative, bidirectional interaction, such that infection with either virus yields 40-100% protection against infection with the other for one to five months. Assuming that LAIV reduces RSV susceptibility in a similar manner, we predict that the impact of such a vaccine at the population level would likely depend greatly on underlying viral circulation patterns. More broadly, we highlight the utility of mathematical models as a tool to characterize pathogen-pathogen interactions.

Suggested Citation

  • Sarah C. Kramer & Sarah Pirikahu & Jean-Sébastien Casalegno & Matthieu Domenech de Cellès, 2024. "Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53872-4
    DOI: 10.1038/s41467-024-53872-4
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    References listed on IDEAS

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    1. Rachel E. Baker & Ayesha S. Mahmud & Caroline E. Wagner & Wenchang Yang & Virginia E. Pitzer & Cecile Viboud & Gabriel A. Vecchi & C. Jessica E. Metcalf & Bryan T. Grenfell, 2019. "Epidemic dynamics of respiratory syncytial virus in current and future climates," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. King, Aaron A. & Nguyen, Dao & Ionides, Edward L., 2016. "Statistical Inference for Partially Observed Markov Processes via the R Package pomp," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i12).
    3. Sarah C Kramer & Sen Pei & Jeffrey Shaman, 2020. "Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-21, October.
    4. Sourya Shrestha & Aaron A King & Pejman Rohani, 2011. "Statistical Inference for Multi-Pathogen Systems," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-14, August.
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