Mathematics can improve the flu shot, experts say

Mathematics can improve the flu shot, experts say

Credit: University

Mathematical modeling can improve the effectiveness of the flu vaccine, according to Rice University experts, where such a model has existed for more than 15 years and the Baker Institute for Public Policy.

Michael Deem, John W. Cox, professor of biochemical and genetic engineering at Rice; Melia Bonomo, a PhD candidate in physics and astronomy at the university; and Kirstin Matthews, member of the scientific and technological policy of the Baker Institute's Center for Health and Biological Sciences, illustrated their insights in a new policy document, "Improving the effectiveness of the annual flu vaccine".

Seasonal influenza (influenza) causes up to 49 million diseases and 79,000 deaths in the United States each year since 2010. To combat its impact, the Center for Disease Control and Prevention (CDC) recommends to all healthy children and adults to get a flu shot every year. In 2017-18, 58% of healthy children (6 months to 17 years) and only 37% of adults got the vaccine. About 80% of pediatric flu deaths during that season were unvaccinated children.

"To develop a vaccine in time for the start of the flu season in the fall, scientists must start in early January," the authors wrote. "The current method used by the CDC involves scientists vaccinating ferrets with several vaccinated candidates, then extracting antibodies from ferrets to estimate which vaccine was the most effective against the dominant viruses of the previous influenza season. This method was used to almost 50 years However, it has been shown to be inconsistent in predicting how well the vaccines would behave in humans, especially with the recent A (H3N2) viruses that mutate rapidly. In addition, experiments with ferrets are long and expensive. "

In contrast, mathematical models, including a model developed in Rice more than 15 years ago, allow scientists to calculate how well the flu vaccine matches the infectious viruses. The Rice model, called pEpitope, estimates the efficacy of the vaccine and has proven to work well for influenza vaccines A (H3N2), A (H1N1) and B. For flu season 2018-19, rice scientists predict that the vaccine will be between 20 and 40% effective against most A (H3N2) viruses.

"Public health researchers are often slow to change," the authors wrote. "Although Rice's pEpitope model has been around for more than 15 years, it is unclear why the CDC should still take advantage of it to develop its seasonal influenza vaccine by adding such a model to existing ferret experiments. decision-making process.

"This mathematical modeling technique can quickly restrict the viruses that would be good candidates for the vaccine during a particular flu season," they continued. "It can serve as a check to ensure that the vaccine virus does not mutate during the production process. The pEpitope model is also low cost, as it requires no specialized equipment. Finally, it is extremely fast, taking only a couple of seconds to analyze the potential effectiveness of a vaccine against thousands of infectious viruses in a particular geographic region. "

The authors stated that the CDC should strengthen its current protocols for the selection of vaccine candidates using all available forecasting models. "This will improve the overall efficacy rates of the flu vaccine and potentially even coverage rates," they wrote. "Scientists hope that with greater effectiveness, they will also be able to improve vaccine coverage rates, which continue to follow the 70% goal of the CDC's Healthy People 2020 given the difficulty of producing effective vaccines and the general climate of public distrust of immunization, this work has the potential to improve the selection and education of the vaccine strain by providing an instrument that is accessible both to researchers and to citizens' scientists ".

Explore further:
The study predicts that the flu vaccine of 2018 will have a 20% effectiveness

More information:
Improving flu shots: … flu shots /

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Rice University


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