Salath and his team found that, at the end of the day, most people had experienced a fairly high number of person-to-person interactions, but they also found very little variation among individuals.
Strikingly, they did not find any individuals who had an extraordinarily high number of contacts when compared with the rest of the group. Such individuals--called super-spreaders--are known to be very important in the dynamics of disease spread.
"For example, in sexual-contact networks, one often finds a group of people with a much higher potential to contract and spread a virus such as HIV," Salath said.
"This potential is due to these individuals' extremely high number of interactions. But in our experiment, while there may have been kids with a few more interaction events, for the most part, everyone had about the same high level of interaction."
Salath explained that while schools may indeed be "hot beds" for colds and the flu, individual students do not seem to vary with regard to exposure risk due to their contact patterns.
Data from the motes also confirmed an important social-networking theory--that contact events are not random because many "closed triangles" exist within a community.
"If person A has contact with person B, and person B has contact with person C, chances are that persons A and C also have contact with each other," Salath said.
"Real data illustrating these triangles provide just one more piece of information to help us track how a disease actually spreads."
Salath also said that networking data such as his may help guide public-health initiatives such as vaccination strategies and prevention education.
|Contact: Cheryl Dybas|
National Science Foundation