New methods for analyzing personal health and lifestyle data captured through wearable devices or smartphone applications can help identify college students at risk of catching the flu, say researchers at the University of North Carolina-Chapel Hill and Duke University.
[Photo: Dr. Allison Aiello]
Dr. Allison Aiello, professor of epidemiology at UNC’s Gillings School of Global Public Health, collaborated with Dr. Katherine Heller, assistant professor of statistical science at Duke, to develop a model that allows researchers to predict the spread of influenza from one person to the next over time. The model is aided by a mobile app that monitors with whom and when students interact.
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, Dr. Heller said.
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.
The researchers presented their findings August 12 at the twenty-first International Conference on Knowledge Discovery and Data Mining in Sydney, Australia.
Although this year’s flu season will not peak before winter, it begins as early as October, and college campuses nationwide are getting ready. Close living quarters, low flu vaccination rates and busy social calendars make college students particularly prone to catching the virus.
Of the nation’s 18 million undergraduates, more than one in five are likely to be infected with the flu this year. That could mean up to two weeks of fever, chills, muscle aches, scratchy throat, runny or stuffy nose, congestion and sneezing, not to mention missed classes and extracurriculars.
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, called iEpi, that used Wi-Fi, Bluetooth and GPS technology to monitor where they went and with whom they came in contact 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 provided the odds that each student would spread or contract the flu on any given day and identified personal health habits — such as hand washing or obtaining 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 also were more likely to get sick.
The researchers also found that students who smoked or drank took longer to recover.
“This study showed that it is possible to harness the power of collecting real-time data with smart phone apps to measure interactions and behaviors more accurately,” Dr. Aiello said. “We believe this serves as a window into the future of epidemiological data collection. This technology has allowed us to better assess the context in which social and behavior practices influence health.”
Dr. Aiello said that the major challenge in the study was the immense amount of data collection and cleaning.