New model explains ups and downs of flu epidemics

Notoriously wily, the influenza virus keeps public health experts guessing about when new strains will emerge and whether existing vaccines will be effective against them.

In an attempt to better understand the dynamics of influenza outbreaks, a team of theoretical ecologists at U-M has developed a new model that incorporates information on how the virus evolves with knowledge of the epidemiology of the disease.

While the model underscores the complexities that make outwitting the ever-changing virus so challenging, it also lays the groundwork for eventually teasing out patterns that may aid in predicting and controlling influenza outbreaks. The new model was presented in the Dec. 22 issue of the journal Science.

The first step in developing the model was to consider how the flu virus evolves, says first author Katia Koelle, who received her doctoral degree under the direction of U-M theoretical ecologist Mercedes Pascual and now is a postdoctoral fellow at The Pennsylvania State University.

"We know that the influenza virus evolves very rapidly—that's why people can get reinfected with the virus within 10 years of a previous infection," she says. But there are two distinct patterns of evolution to consider: the underlying genetic mutations that lead to the development of different strains, and the physical changes in the virus that determine how our immune systems respond to the various strains.

The immune response is triggered by proteins called antigens on the surface of the virus. When a person is infected with the virus, the body produces antibodies that recognize the antigens.

Strains of virus with similar antigens all elicit the same response, but viruses with significantly different antigens can evade the immune system and cause illness. Especially large flu outbreaks occur when new viral strains, with new antigens, appear.

"Our model does away with the idea that greater genetic differences imply greater antigenic differences," Koelle says. In their model, viral strains can be grouped into "antigenic clusters"—groups of strains with similarly shaped antigens and similar immunological properties.

The model helps explain the ups and downs of influenza outbreaks, Koelle says, predicting a boom-and-bust pattern in the genetic diversity of circulating strains that explains some of the variation in year-to-year outbreak sizes.

The model's results have implications for vaccination strategies, Koelle says. "In years when there's no cluster transition, using a vaccine strain that's related to a strain in the current antigenic cluster should provide a lot of cross-immunity—the vaccine should work relatively well."

However, the model also suggests that predicting when new clusters will arise and what their antigens will look like—information that could help public health experts stay one step ahead of the virus—is extremely difficult.

"But it's not impossible," says Pascual, an associate professor of ecology and evolutionary biology. "The next step will be working from our model with additional data to look for regular patterns that may help us cut through the complexity."

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