Hit or Miss: Flu Vaccines
Abstract
Influenza affects millions and causes more than 30,000 deaths in the US every year. Vaccination has to be done annually and yet effectiveness of the vaccine is variable due to multiple strains of the Influenza virus and the difficulty in predicting the strains for vaccine production. The question I researched in this experiment is whether or not there is a significant correlation between the degree of match between the strains of Influenza and the vaccine (measured as the percentage of matching amino acids of the flu virus and its vaccine), and the overall effectiveness of the vaccine. I used the U.S. Center for Disease Control website to find the most common flu strains sub typed during each season between 2001 and 2013, and the vaccines that were used in those seasons. Next, using the BLAST algorithm from the National Center for Biotechnology Information website, I determined the percentage of the amino acids that matched between the flu viruses and the vaccines. This represents the degree of the match. Then, I collected surveillance data for the corresponding years from the CDC website to determine the severity of the flu. The five criteria used to determine the severity were Mortality, Hospitalizations, Pediatric Mortality, Percentage of Outpatient Visits Related to Flu, and Percentage of Lab Tests that are Positive. Using statistical analysis, I determined the correlation between the degree of the matching amino acids of the vaccine and virus, and the severity of the flu epidemic. From this, I determined that there is a correlation between the degree of matching amino acids of the vaccine and virus, and the severity of the flu. Four out of the five criteria correlated to the degree of matching amino acids of the vaccine and the virus. I found a trend that as the degree of matching amino acids increased, the severity of the flu decreased. Furthermore, I noticed that a one hundred percent match of amino acids was not required to have a successful
Influenza affects millions and causes more than 30,000 deaths in the US every year. Vaccination has to be done annually and yet effectiveness of the vaccine is variable due to multiple strains of the Influenza virus and the difficulty in predicting the strains for vaccine production. The question I researched in this experiment is whether or not there is a significant correlation between the degree of match between the strains of Influenza and the vaccine (measured as the percentage of matching amino acids of the flu virus and its vaccine), and the overall effectiveness of the vaccine. I used the U.S. Center for Disease Control website to find the most common flu strains sub typed during each season between 2001 and 2013, and the vaccines that were used in those seasons. Next, using the BLAST algorithm from the National Center for Biotechnology Information website, I determined the percentage of the amino acids that matched between the flu viruses and the vaccines. This represents the degree of the match. Then, I collected surveillance data for the corresponding years from the CDC website to determine the severity of the flu. The five criteria used to determine the severity were Mortality, Hospitalizations, Pediatric Mortality, Percentage of Outpatient Visits Related to Flu, and Percentage of Lab Tests that are Positive. Using statistical analysis, I determined the correlation between the degree of the matching amino acids of the vaccine and virus, and the severity of the flu epidemic. From this, I determined that there is a correlation between the degree of matching amino acids of the vaccine and virus, and the severity of the flu. Four out of the five criteria correlated to the degree of matching amino acids of the vaccine and the virus. I found a trend that as the degree of matching amino acids increased, the severity of the flu decreased. Furthermore, I noticed that a one hundred percent match of amino acids was not required to have a successful