The King Laboratory
of Theoretical Ecology & Evolution
at the University of Michigan

Evolution-informed forecasting of seasonal influenza A (H3N2)

X. Du, A. A. King, R. J. Woods, and M. Pascual
Science Translational Medicine 9(413): eaan5325, 2017.
Forecasting influenza using information about the virus’ evolution....

Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, 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 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 for more than 10 years, we demonstrate the feasibility of skillful prediction for total cases ahead of season, with a tendency to underpredict monthly peak epidemic size, and an accurate real-time forecast for the 2016/2017 influenza season.

The official version of the paper is here.   Please contact Prof. King if you'd like a reprint.

© 2020 Aaron A. King
3068 Biological Sciences Building
1105 North University Avenue
Ann Arbor MI 48109-1085 USA