Of the 18 million undergraduates in the United States, more than one in five are likely to get the flu this year.
Scientists say a new technique that uses mobile devices to capture lifestyle data can help identify the students most at risk.
Unlike most infection models, which focus on population-level changes in the proportion of people likely to get sick, this approach gives a personalized daily forecast for each patient, says Katherine Heller, a statistician at Duke University who helped develop the model.
In theory, doctors could use such data to identify and alert at-risk students before they get sick or start to feel symptoms, or to encourage them to stay at home to avoid infecting other students.
Heller and colleagues presented their findings this month at the 21st International Conference on Knowledge Discovery and Data Mining in Sydney, Australia.
100 students with Androids
To test the model, the researchers applied it to a study of roughly 100 students at the University of Michigan.
For 10 weeks during the 2013 flu season, the students carried Google Android smartphones with built-in software, iEpi, that used Wi-Fi, Bluetooth, and GPS technology to monitor where they went and who they came in contact with from moment to moment.
The students also recorded their symptoms every week online. Students who reported coughing and fever, chills, or aches provided throat swabs to determine whether they had a cold or the flu.
The model then returned the odds that each student would spread or contract the flu on a given day, and identified the personal health habits—such as hand-washing or getting a flu shot—that might help them beat the odds or hasten their recovery.
Not surprisingly, when a student got sick, his or her friends were more likely to get sick too.
The researchers also found that students who smoked or drank took longer to recover.
“We didn’t have this kind of personalized health data until a few years ago,” Heller says. “But now, smartphones and wearable health and fitness devices allow us to collect information like a person’s heart rate, blood pressure, social interactions, and activity levels with much more regularity and more accurately than was possible before. You can keep a continuous logbook.”
“We want to leverage that data to predict what people’s individual risk factors are, and give them advice to help them reduce their chances of getting sick,” Heller adds.
Kai Fan of Duke, Marisa Eisenberg and Alison Walsh of the University of Michigan, and Allison Aiello of the University of North Carolina-Chapel Hill are authors of the study, which was supported by the National Science Foundation and the US Centers for Disease Control and Prevention.
Source: Duke University