Popular People Help Experts Predict Flu Outbreak
Sept. 16, 2010— -- Popular people are the trendsetters of society, especially when it comes to the next flu outbreak, according to preliminary research from Harvard University.
During last fall's H1N1 pandemic, researchers tracked and compared the spread of the disease in a random sample of students and a group of more socially dominant students at the university.
While both groups ended up coming down with the flu in similar numbers, the more "popular" group of students got sick about two weeks ahead of their less socially-connected peers, making them a potentially useful gauge of when and to what extent a flu outbreak will occur in that population.
Because individuals who are more connected within their social network are more likely to come into contact with any given individual in that network, the logic goes that these social butterflies are also more likely to be the first to catch whatever communicable bug is going around.
"People who are very active socially and spend time with other people will have a higher risk of contagion. It's kind of intuitive," says Dr. Christopher Ohl, associate professor of Infectious Diseases, Wake Forest University Baptist Medical Center.
"Here we are focused on early detection. Think back to last fall and we had to make hard choices about who had to get vaccinated first. If you could have these "friend" sensors and know which part of the country were getting the flu earlier, it would give you more time to get vaccines there," says co-author James Fowler, a professor specializing in social networks at the University of California, San Diego.
While tracking high-risk individuals in a population in order to gauge the spread of the disease is not a new concept, public health experts say Fowler's approach is novel, and potentially useful.
"There is a tradition of defining sentinel populations for public health -- 'canaries in the coal mine' based on risk factors," says Stephen Eubank, professor of Virginia Bioinformatics Institute at Virginia Tech. This research applies this practice to theories about how to identify people who are socially central in a population, he says.
