Kentucky State University

Influenza vaccine mismatch

Presenter Information

Jamal HayesFollow

Grade Level at Time of Presentation

Senior

Major

Biology

Institution 24-25

Kentucky State University

KY House District #

57

KY Senate District #

20

Department

Biology

Abstract

Influenza vaccine mismatch: Disease burden, key mutations, and emergence of vaccine escape variants

*Hayes, J., Gray, A., Garrett, K., and Lai, A., Biology/STEM, Kentucky State University, Frankfort, Kentucky

Abstract

Annual reformulation and vaccination against influenza is needed to counteract “antigenic drift” due to the rapid evolution of the virus. Vaccine mismatch results in significant increase in disease burden. We evaluated the impact of specific mutations at the antigenic sites and vaccine mismatch and to correlate that disease burden as an index for viral virulence. The goal was to unmask the influence vaccine effectiveness to dissect the basis of viral evolution. We collected and analyzed epidemiological data (morbidity and mortality, plus hospitalization data) from past influenza seasons. We established a database of viral genomes from past vaccine strains and analyzed the mutations at the antigenic sites and compare to temporal circulating viral strains. We discovered that the alignment of antigenic sites for A/H3N2, there was a bias of mutations at the antigenic site B. Whether this biased mutation pattern is correlated to vaccine effectiveness and disease burden remains to be determined. A mathematical model is being developed. This model will be first validated by existing data, followed by “training” by using existing data, and to be tested as a “predictive model”.

This document is currently not available here.

Share

COinS
 

Influenza vaccine mismatch

Influenza vaccine mismatch: Disease burden, key mutations, and emergence of vaccine escape variants

*Hayes, J., Gray, A., Garrett, K., and Lai, A., Biology/STEM, Kentucky State University, Frankfort, Kentucky

Abstract

Annual reformulation and vaccination against influenza is needed to counteract “antigenic drift” due to the rapid evolution of the virus. Vaccine mismatch results in significant increase in disease burden. We evaluated the impact of specific mutations at the antigenic sites and vaccine mismatch and to correlate that disease burden as an index for viral virulence. The goal was to unmask the influence vaccine effectiveness to dissect the basis of viral evolution. We collected and analyzed epidemiological data (morbidity and mortality, plus hospitalization data) from past influenza seasons. We established a database of viral genomes from past vaccine strains and analyzed the mutations at the antigenic sites and compare to temporal circulating viral strains. We discovered that the alignment of antigenic sites for A/H3N2, there was a bias of mutations at the antigenic site B. Whether this biased mutation pattern is correlated to vaccine effectiveness and disease burden remains to be determined. A mathematical model is being developed. This model will be first validated by existing data, followed by “training” by using existing data, and to be tested as a “predictive model”.