Publications by authors named "K Bodie Weedop"
Article Synopsis
- The study introduces NIAViD, an unsupervised machine learning tool that effectively detects antigenic changes in H3N2 influenza A viruses, which is crucial for improving vaccine design.
- NIAViD achieved a sensitivity of 88.9% in training and 72.7% in validation, significantly outperforming a standard model, while eliminating the need for costly laboratory assays.
- This tool enhances influenza surveillance by identifying new antigenic clusters and pinpointing critical sites for antigenic changes, ultimately helping in the development of more effective vaccines.
The 2019/2020 influenza season in the United States began earlier than any season since the 2009 H1N1 pandemic, with an increase in influenza-like illnesses observed as early as August. Also noteworthy was the numerical domination of influenza B cases early in this influenza season, in contrast to their typically later peak in the past. Here, we dissect the 2019/2020 influenza season not only with regard to its unusually early activity, but also with regard to the relative dynamics of type A and type B cases.
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