Study may increase the effectieness of flu shots

New engineering research from the University of Pittsburgh has the potential to aid in the development of a more effective flu shot.

A study focusing on the composition and timing of shot design used optimization methods to examine whether or not the annual decision-making process regarding which strains of influenza should be included in the seasonal influenza vaccine could be improved, according to

"The flu's high rate of transmission requires frequent changes to the shot," Oleg Prokopyev, a coauthor of the study, said, reports. "Different strains can also cocirculate in one season, which gives us another challenge for figuring out the composition."

The complex decision is made every year by the U.S. Food and Drug Administration. The longer the FDA waits before making a decision, the more data is available to them as to what strains may be prevalent. As accuracy improves, however, the more likely it becomes that vaccine manufacturers will run out of time to make the proper amount needed.

The research, developed by faculty members of Pitt’s Swanson School of Engineering and the Department of Health Policy and Management, balances these two important elements of the selection decision and integrates the composition and timing of the shot design.

The newly developed decision-making model addresses how many strains to include in the combination vaccine, when the final decision needs to be made, how many times the FDA should meet to re-examine information concerning global flu data and potential developments in vaccine production methods.

"With this model, several policy questions can be addressed," Andrew Schaefer, one of the study’s co-authors, said, reports. "For example, incorporating more than three strains might increase the societal benefit substantially, particularly under more severe flu seasons.

"The strains in the flu shot are now chosen at least six months before the actual flu season. This leaves a lot of uncertainty because we're really not sure which strain will emerge. Our models provide insights into a better flu shot."