PHILADELPHIA, PA — Classroom rosters combined with human-networking theory may give a clearer picture of just how infectious diseases such as influenza can spread through a closed group of people, and even through populations at large, according to a press release.
Using high school schedule data for a community of students, teachers and staff, Penn State University's Marcel Salathé, an assistant professor of biology, and Timo Smieszek, a post-doctoral researcher, have developed a low-cost but effective method to determine how to focus disease-control strategies based on which individuals are most likely to spread the infection, the release stated.
According to the release, the team's new findings build on earlier research in Salathé's lab addressing the challenge of counting the number of disease-spreading, face-to-face interactions within a closed group of people.
"Theoretically, we know that people come into contact with many other people, that interactions vary in length, and that each contact is an opportunity for a disease to spread via small droplets that spread from the nose or mouth of one individual to another," said Salathé.
"But, it's very tedious and unreliable to ask people, 'How many different people have you been in contact with today, and for how long?' We knew we had to figure out the number of person-to-person contacts systematically," Salathé added.
Now, in their new study, Salathé and Smieszek have studied and recorded possible disease-spreading interactions with a different system: The team members gathered data from classroom rosters and formulated a "collocation rank" — the cumulative time each individual is potentially exposed to other individuals — for all students, teachers, and staff members at a high school, the release noted.
The team members then compared their new classroom-schedule data to a computer model they had developed from the earlier motes study, the release added.
Click here to read the release in its entirety.