With the flu, every year is a rapid arms race. Countless numbers of viruses are pushed to mutate and spread over and over again, transforming until they can finally evade the human antibodies that have built up to fight off their predecessors. This shifting landscape poses a particular problem for the people in charge of reformulating the annual flu vaccine. How do you prepare for the onslaught of a virus that doesn't exist yet?

Two computational biologists have just unveiled the first computer model that forecasts the yearly changes in the worldwide populations of flu viruses. As they report today in Nature, their model will have immediate impact in the development of flu vaccines—and proves that in some cases, projecting evolutionary change may not be beyond our reach.

"We don't actually predict new mutations in the flu virus," says Marta Luksza, one of the scientists at Columbia University, "Our model only considers the rise and fall of families of closely related viruses." Still, the computer model has proven that it can with 93 percent accuracy predict which families will harbor the most widespread viruses in the upcoming year. That's enough information to build a better vaccine, as vaccines need to inoculate only against a closely related virus to be effective.

Although the model attacks a chaotic and complex system, its framework is deceptively simple. At its core, the model is built around two concepts: First, that viruses spread and die out at fairly repetitive rates, and second, that you can categorize certain classes of mutations to determine how infectious and battle-worthy a virus will be.

The researchers gather and analyze the genetic data on the vast number of seasonal flu viruses mucking about and making people miserable. Every virus is separated into its family. "The number of these families varies from season to season, it's usually somewhere around six to ten," Luksza says.

The researchers then calculate how fearsome each virus family will be by comparing its genetic drift from the previous year. For simplicity's sake, the model incorporates new mutations only to the outer layer of proteins on the virus's shell. This is because these proteins—which determine how the flu hides from antibodies and latches onto helpless cells—are the virus's cloak and dagger.

The model predicts a flu family will be tougher if it has new mutations to the part of these shell proteins that antibodies observe because these mutations will make the virus more unrecognizable. Meanwhile, the model docks points for mutations to the part of the shell proteins that help the virus latch onto and infect a foreign cell, because it's likely that any dagger mutations will botch or disrupt the already finely-honed weapon. (The flu is already great at infecting our cells; it doesn't need a change here.)

The way in which the models weighs these attributes and translates them in the likelihood a flu family will rise or fall is based on decades of data that Luksza and her colleague number-crunched into the model. "It sounds simple, but the behind-the-scenes effort in order to have make work is absolutely substantial," says Katia Koelle, an infectious disease biologist at Duke University, who was not involved with the research.

The brand-new model has its limitations—it correctly predicts the seasonal decline of a flu family only about three out of four times, for example. And there's plenty of room for improvement. But Luksza and Koelle both agree that it's a proof of principle and that the underlying framework is solid. Given just how chaotic the real world of mutating and spreading viruses actually is, the model's forecasting ability is nothing short of impressive, Koelle says.

Luksza is already investigating if this modeling approach can be applied to other viruses, such as swine flu, and reports that the preliminary results look promising. But for now as, as vaccine researchers already prepare for this autumn's new flu season, the model already offers up a new way to peer forward into the unforeseeable.

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