Scientists could one day predict flu seasons like they predict weather, allowing communities to stock up on vaccines and antiviral drugs, according to a new study published in the Proceedings of the National Academy of Sciences.
Using real-time data from the Centers for Disease Control, humidity forecasts, and historical flu season data, Columbia University researcher Jeffrey Shaman, who studies the environment's effects on infectious disease transmission for the university's Mailman School of Public Health, says his model can predict the height of flu season with reasonable accuracy up to seven weeks before it hits.
"It's a skillful forecast, meaning we can assign confidence to it—it's like if the weatherman says there's an 80 percent chance of rain, you'll plan differently than if there's a 20 percent chance of rain," Shaman says. "Just like the weather, the predictions that are sooner are usually much better."
Not every year has a distinct flu season—the winter flu season of 2011-2012 had relatively few cases spread over many months, while the winter of 2009-2010 had a large spike in flu cases during October. Shaman says his model will likely be able to predict these spikes better than it can predict moderate flu seasons.
"We might not be able to predict as well when the flu season really peters on," he says.
Shaman says he can see his prediction method being used by local health officials in order to warn people at high risk, such as senior citizens, of an impending spike in flu cases. The predictions would be similar to high pollen or air quality alerts that many weather stations already produce.
"Health officials will be able to stock up on vaccines, get the word out, and order antivirals," he says. "And if we know the circulating strain is particularly aggressive or has an extreme morbidity risk, it's something that could result in school and work closings."
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Jason Koebler is a science and technology reporter for U.S. News & World Report. You can follow him on Twitter or reach him at email@example.com.