Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Dylan H. Morris, Katelyn M. Gostic, Simone Pompei, Trevor Bedford, Marta Łuksza, Richard A. Neher, Bryan T. Grenfell, Michael Lässig, John W. McCauley

Research output: Contribution to journalReview article

24 Scopus citations

Abstract

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Original languageEnglish (US)
Pages (from-to)102-118
Number of pages17
JournalTrends in Microbiology
Volume26
Issue number2
DOIs
StatePublished - Feb 2018

All Science Journal Classification (ASJC) codes

  • Microbiology
  • Microbiology (medical)
  • Infectious Diseases
  • Virology

Keywords

  • Influenza
  • Predictive evolution
  • predictive modeling
  • vaccine strain selection

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    Morris, D. H., Gostic, K. M., Pompei, S., Bedford, T., Łuksza, M., Neher, R. A., Grenfell, B. T., Lässig, M., & McCauley, J. W. (2018). Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends in Microbiology, 26(2), 102-118. https://doi.org/10.1016/j.tim.2017.09.004