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Evolution-informed forecasting of seasonal influenza A (H3N2)

View ORCID ProfileXiangjun Du, View ORCID ProfileAaron A. King, View ORCID ProfileRobert J. Woods, View ORCID ProfileMercedes Pascual
doi: https://doi.org/10.1101/198168
Xiangjun Du
1Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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Aaron A. King
2Departments of Ecology & Evolutionary Biology and Mathematics, University of Michigan, Ann Arbor, MI, USA
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Robert J. Woods
3University of Michigan Health System, University of Michigan, Ann Arbor, MI, USA
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Mercedes Pascual
1Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
4The Santa Fe Institute, Santa Fe, NM, USA
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  • For correspondence: pascualmm@uchicago.edu
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ABSTRACT

Inter-pandemic or seasonal influenza exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus’ antigenic evolution. We propose here a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino-acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States over 10 years, we demonstrate the feasibility of prediction ahead of season and an accurate real-time forecast for the 2016/2017 influenza season.

SUMMARY Skillful forecasting of seasonal (H3N2) influenza incidence ahead of the season is shown to be possible by means of a transmission model that explicitly tracks evolutionary change in the virus, integrating information from both epidemiological surveillance and readily available genetic sequences.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 04, 2017.
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Evolution-informed forecasting of seasonal influenza A (H3N2)
Xiangjun Du, Aaron A. King, Robert J. Woods, Mercedes Pascual
bioRxiv 198168; doi: https://doi.org/10.1101/198168
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Evolution-informed forecasting of seasonal influenza A (H3N2)
Xiangjun Du, Aaron A. King, Robert J. Woods, Mercedes Pascual
bioRxiv 198168; doi: https://doi.org/10.1101/198168

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