SciTech

Model predicts flu strains for vaccine production

Assistant professor Soo-Haeng Cho developed a model to improve the effectiveness of influenza vaccinations by predicting which virus strain will spread each year. Swine flu was the most prominent in 2009. (credit: Sibel Ergener/Art Staff) Assistant professor Soo-Haeng Cho developed a model to improve the effectiveness of influenza vaccinations by predicting which virus strain will spread each year. Swine flu was the most prominent in 2009. (credit: Sibel Ergener/Art Staff)

One of the most debated health issues of 2009 was the swine flu pandemic. At Carnegie Mellon especially, the disease infected student after student at an alarming rate. While the government has issued vaccines available for the general public, one Carnegie Mellon professor has developed a model that will make any future influenza vaccines much more effective and applicable to a multitude of diseases.

Soo-Haeng Cho is an assistant professor of operations management in the Tepper School of Business, teaching courses in not only operations management but also dynamic programming to Ph.D. students. His paper, “The Optimal Composition of Influenza Vaccines Subject to Random Production Yields,” was published in October 2009 and concerns providing a way to determine what virus strains would be most useful to be combated by a proposed vaccine. Although science is not part of Cho’s official role, he still has a remarkable grasp of how influenza viruses affect people.

Every year, various influenza viruses circulate with different levels of severity. In 2009, the most prominent of these virus strains happened to be the swine flu. “We cannot perfectly predict which virus strain will spread each year,” reported Cho from his office in Posner Hall. He first became interested in this research several years ago, when only two manufacturers provided flu vaccines. One of the manufacturers was forced to abandon production after their vaccines were revealed to be contaminated, leaving only 50 million vaccinations available to the general public — not nearly enough.

By the end of 2009, demand for the swine flu vaccine lessened as the active cases of flu had declined since the peak of H1N1 cases in the fall.
Therefore, the government decided to cut back its orders from vaccine manufacturers — a Yahoo news article reported that the U.S. would cut its vaccination order from Australia’s CSL Ltd. in half — as fear of the swine flu decreased. Each year, the Vaccine and Related Biologic Products Advisory Committee decides by February or March which specific virus strains to include in vaccines. Essentially, the committee has to choose one of three options: It can delay its decision in order to observe the viruses more thoroughly, it can retain the virus strain used in the previous year, or it can change to a new virus strain. “If you change to a new virus strain,” warns Cho, “production becomes more unpredictable.”

He points out the flaw in the argument as this: If the committee chooses to distribute earlier, the risk of protecting against a wrong strain rises. Of course, even with meticulous planning, there’s no telling if the vaccine will be useful again in the years to come, as the viruses are constantly mutating. The observation option involves monitoring virus activities around the world. This is precisely how Cho developed his mathematical model. It included several variables, including the “cross-efficacy” of the vaccine in question (how effective the vaccine will be when a vaccine strain differs from a circulating strain), the number of manufacturers, how many vaccines the manufacturers can produce in a certain amount of time, the amount of time before distribution, and the number of viruses estimated to be circulating.

Cho hopes that the model will be considered before the committee meets again for the yearly distribution plan. The ultimate goal of his work — to better match supply and demand through an “optimal dynamic policy” — may lead to a better use of limited resources, and, more importantly, limited time in the face of global health crises.