Epidemiological assessment of mutational capabilities in common seasonal viruses.

Abstract

Background: Influenza vaccine composition is reviewed prior to every flu season since influenza viruses frequently evolve through antigenic variations. Vaccine strains are selected in expectation of the upcoming influenza season to allow sufficient time for production. The aim of the present study is to assess the use of computational models for predicting the evolution of influenza based on the association of genetic mutations and antigenic traits of circulating viruses that may apprise vaccine strain assortment decisions. Methodology: This study also focuses on the correlation of viruses with spread rate using statistical methods. For this method, we have worked on four different viruses Influenza, Ebola, Measles and Dengue. The year-wise mutation rate was correlated with the epidemiological data to see the impact of mutations on the disease spread. Results: We highlight the efficiency of this approach by analyzing the mutation rate and correlating it with its spread rate to find out either mutation in viruses causes disease spread or not. Our study identified mutations in viruses get high before the outbreak of disease through which we can assess the upcoming outbreak. We can set a threshold value for nucleotide differences that can predict the next outbreak of viral disease. Conclusion: The concept of correlation between the genomic data and epidemic spread leads to the research analysis that mutations do not follow any pattern. Though most of the mutations are random. Our research concluded that some mutations may suppress the virus outbreak, and some mutate to become more resistant than the existing strain that causes an outbreak.

Keywords

• computational models
• genetic mutations
• vaccine strains
• viral outbreak