Improving the prediction of influenza vaccine effectiveness by refined genetic distance measure

Our previous research demonstrated that genetic distance (GD) on effective mutation (EM) sites can be used to evaluate vaccine effectiveness (VE) in silico in real time. This study further investigates the relationship between VE and GD on antigenic sites (AS) and identifies key amino acid sites related to vaccine protection against influenza A/H1N1pdm09 and A/H3N2 between 2009 and 2019 flu seasons. We found that not any AS on hemagglutinin (HA) and neuraminidase (NA) may cause a decrease in VE, rather, GD on the intersection set of EM and AS is highly predictive of influenza VE. The integrated GD of HA and NA can explain up to 87.8% of VE variations for H3N2. Significant improvement is also found for VE prediction for pH1N1. Accurate prediction of influenza VE before vaccine deployment may facilitate reverse vaccinology to optimize vaccine antigen design and facilitate government preparedness of epidemics. ### Competing Interest Statement M.H.W and B.C.Y.Z are shareholders of Beth Bioinformatics Co., Ltd. B.C.Y.Z is a shareholder of Health View Bioanalytics Ltd. S.Y.C and J.L. are employees of Beth Bioinformatics Co., Ltd. All other authors declare no competing interests. ### Funding Statement This work was partially supported by the Health and Medical Research Fund, the Food and Health Bureau, the Government of the Hong Kong Special Administrative Region \[COVID190103\] (MHW) and \[INF-CUHK-1\] (EKY), the National Natural Science Foundation of China \[31871340\] (MHW), and the Chinese University of Hong Kong Grant \[PIEF/Ph2/COVID/06, 4054600\] (MHW). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.