Evolution-informed forecasting of seasonal influenza A (H3N2).

Abstract:

: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.

journal_name

Sci Transl Med

authors

Du X,King AA,Woods RJ,Pascual M

doi

10.1126/scitranslmed.aan5325

subject

Has Abstract

pub_date

2017-10-25 00:00:00

issue

413

eissn

1946-6234

issn

1946-6242

pii

9/413/eaan5325

journal_volume

9

pub_type

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