  Current methods for the detection of contagious outbreaks give
contemporaneous information about the course of an epidemic at best.
Individuals at the center of a social network are likely to be infected sooner,
on average, than those at the periphery. However, mapping a whole network to
identify central individuals whom to monitor is typically very difficult. We
propose an alternative strategy that does not require ascertainment of global
network structure, namely, monitoring the friends of randomly selected
individuals. Such individuals are known to be more central. To evaluate whether
such a friend group could indeed provide early detection, we studied a flu
outbreak at Harvard College in late 2009. We followed 744 students divided
between a random group and a friend group. Based on clinical diagnoses, the
progression of the epidemic in the friend group occurred 14.7 days (95% C.I.
11.7-17.6) in advance of the randomly chosen group (i.e., the population as a
whole). The friend group also showed a significant lead time (p<0.05) on day 16
of the epidemic, a full 46 days before the peak in daily incidence in the
population as a whole. This sensor method could provide significant additional
time to react to epidemics in small or large populations under surveillance.
Moreover, the method could in principle be generalized to other biological,
psychological, informational, or behavioral contagions that spread in networks.
