The social dimensions of diseases and medical conditions continue to draw research attention, particularly for those interested in mapping and understanding fast-spreading illnesses. A recent study, undertaken by a sociologist and medical geneticist/political scientist, explores how the flu spreads:
The persons at the center of a social network are exposed to diseases earlier than those at the margins states the paradox. Again, your friends are probably more popular than you are, and this “friendship paradox” may help predict the spread of infectious disease. However, Christakis and Fowler found that analyzing a social network and monitoring the health of members is an optimal way to predict a wave of influenza, detailed information simply doesn’t exist for most social groups, and producing it is time-consuming and expensive…
[Sociologist Nicholas] Christakis states: We think this may have significant implications for public health. Public health officials often track epidemics by following random samples of people or monitoring people after they get sick. But that approach only provides a snapshot of what’s currently happening. By simply asking members of the random group to name friends, and then tracking and comparing both groups, we can predict epidemics before they strike the population at large. This would allow an earlier, more vigorous, and more effective response.
This sounds like it has more promise than recently proposed techniques like monitoring Google searches or Twitter feeds.
Additionally, more and more research suggests that monitoring and analyzing social networks is critical for understanding the complex world. Rather than simply examining individuals, we now have some tools to map and model more complex social relationships.