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 Medjournal_title
Science translational medicineauthors
Du X,King AA,Woods RJ,Pascual Mdoi
10.1126/scitranslmed.aan5325subject
Has Abstractpub_date
2017-10-25 00:00:00issue
413eissn
1946-6234issn
1946-6242pii
9/413/eaan5325journal_volume
9pub_type
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