Xiangjun Du, etc.,al. Evolution-informed forecasting of seasonal influenza A (H3N2). Science Translational Medicine 25 Oct 2017
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.
See Also:
Latest articles in those days:
- Structures of H5N1 influenza polymerase with ANP32B reveal mechanisms of genome replication and host adaptation 22 hours ago
- Risk assessment of a highly pathogenic H5N1 influenza virus from mink 23 hours ago
- Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City 23 hours ago
- Sequence-based epitope mapping of high pathogenicity avian influenza H5 clade 2.3.4.4b in Latin America 2 days ago
- Guanylate-binding protein 1 inhibits inflammatory factors produced by H5N1 virus through Its GTPase activity 2 days ago
[Go Top] [Close Window]