In fact, when there is an outbreak of disease, or of
But who are the people with most connections - the 'hubs' in any social network - that should be targeted with inoculating drugs or health education in order to quickly isolate a contagion?
Information about social networks in rural villages in the developing world is costly and time-consuming to collect, and usually unavailable. So current immunisation strategies target people with established community roles: healthcare workers, teachers, and local officials.
Now, Cambridge researchers have for the first time combined networking theories with 'real world' data collected from thousands of rural Ugandan households, and shown that a simple algorithm may be significantly more effective at finding the highly connected 'hubs' to target for halting disease spread.
The 'acquaintance algorithm' employed by researchers is remarkably simple: select village households at random and ask who in their network is most trusted for medical advice.
Researchers were surprised to find that the most influential people in social networks were very often not those with obvious positions in a community. As such, these valuable 'hubs' are invisible to drug administration programmes without the algorithmic approach.
"Everyone is a node in a social network. Most nodes have just a few connections. However, a small number of nodes have the majority of connections. These are the hubs we want to uncover and target in order to intentionally cause failure in social networks spreading pathogens or damaging behaviour," says lead researcher Dr Goylette Chami, from Cambridge's Department of Pathology.
"It was striking to find that important village positions may be best left untargeted for interventions seeking to stop the spread of pathogens through a rural social network," says Chami.
In the study, published today in the journal PNAS, the researchers write that this simple strategy could be particularly effective for isolating households that refuse to take medicine, so that they don't endanger the rest of a community with infection.
Read more at: https://phys.org/news/2017-07-neighbors-infection-countries.html#jCp
