* Google data, computer model combine for possible flu prediction
* First rudimentary forecasts might be available next flu season
* Potential boon for public health agencies
By Deborah Zabarenko
WASHINGTON, Nov 27 (Reuters) - New research suggests it may be possible to forecast flu outbreaks in much the same way meteorologists predict weather, a potential boon for public health officials and consumers, one of the study's authors said on Tuesday.
Using real-time U.S. data gathered by Google Inc, along with a computer model showing how flu spreads, the researchers offered a system that could generate local forecasts of the severity and length of a particular flu outbreak.
This kind of forecasting could improve preparation and management of annual flu outbreaks in the United States, said Irene Eckstrand of the National Institutes of Health.
Influenza kills 250,000 to 500,000 people each year around the globe; the U.S. annual flu death toll is 35,000.
If the forecasts are reasonably accurate, they could help public health officials target vaccines and anti-viral drugs to areas of greatest need, said study co-author Jeffrey Shaman of Columbia University's Mailman School of Public Health.
"If you have a six-week forecast with good confidence that you're going to have an outbreak in New York City and nothing's going on in L.A., you'd send the vaccines there (to New York) because there's enough time to distribute them ... before there's an actual outbreak," Shaman said.
He suggested that flu forecasts might be distributed through TV weather programming. Individuals then could decide whether to get the flu vaccine, keep their distance from people who sneeze or cough and closely monitor symptoms.
This pilot study, published on Monday in the journal Proceedings of the National Academy of Sciences, looked only at the New York City area, using data from 2003 through 2008.
Even so, if all goes well, the system could offer rudimentary forecasts as soon as next year's flu season, Shaman said. It might be possible to issue a few flu forecasts this season, though those would be in "test-case form," he said.
"We have to try it for other regions, other cities," said Shaman. "We have to look and see how it worked during the pandemic years ... we have to see the differences in performance depending on the aggressiveness of the strain of flu."
The computer program the scientists used is a standard epidemiological model showing how influenza moves through a population, from those who are susceptible to flu, to those who have it, to those who have recovered, said study co-author Alicia Karspeck of the National Center for Atmospheric Research in Boulder, Colorado.
The problem with this model is that it's nearly impossible to pinpoint who is susceptible and difficult to track recoveries, though it is possible to figure out the trajectory of an outbreak, Karspeck said.
To conduct their research, the authors said, they needed real-time data, and they found it in an online tool called Google Flu Trends, which uses search terms people put into the Web-based search engine to figure out where influenza is occurring. The tool, launched in 2008, then notifies the U.S. Centers for Disease Control and Prevention in real time.
In a process known as retrospective forecasting, the scientists tested their findings against what happened in the New York area from 2003 through 2008. Because they knew what had happened in these years, they could check their work.
Using the computer program and the flu trends data, they generated retrospective weekly flu forecasts, which predicted the peak of the outbreak more than seven weeks before it occurred.