Social networking theory paints a picture of how diseases spread

Remember real life social networks? Scientists have an initialism for that. It’s CPI – close proximity interaction – and on any given day, high school students, teachers and staff engage in thousands of CPIs that can pass around infectious diseases.

Most of these – like the common cold, SARS, and influenza – are spread when droplets from an infected person find their way to someone else.

“Do you know how many contacts you have with infectious people on a daily basis? Do you know how many contacts you have with anybody on a daily basis?” asks James Holland Jones, a professor of anthropology. “Very often, those are the things we know the least about because they're the hardest to measure.”

So Jones, Marcel Salathé and other colleagues at Stanford University found a way to do it systematically, using wireless sensors to track people at your typical American high school for one January day.

“From a pathogen's point of view,” Salathé says, “each interaction is another chance to jump from person to person.”

The 788 volunteers wore matchbox-sized gadgets – called motes – on lanyards around their necks that transmitted and received radio signals every 20 seconds during the day to record the presence of other motes.

The motes ended up recording 762,868 incidents when two people intermingled within 10 feet of each other – the maximum distance that a disease can be transmitted through cough or sneeze droplets. “The enormous amount of interactions that occur in a single day is mind-blowing,” Salathé says.

Also surprisingly, they didn’t find any individuals who had an extraordinarily high number of contacts. “That flies in the face of what most people might think – that the super-popular kids with more connections than everyone else are more likely to spread more of the virus,” Salathé says. “But it doesn't matter if you're a teacher or a student or a staff member, or whether you're popular or not. Everyone's pretty much the same when it comes to transmission of the flu.”

Then they compiled the tracking data into a high-resolution social contact network and used a computer model to simulate the spread of a flu-like disease throughout the school. The results of the simulated outbreak corresponded well with the absences during fall 2009’s H1N1 spread.

By understanding how pathogens spread between people, scientists can also extrapolate how it will spread among far larger populations using sophisticated computer models.

Social contact networks, the study suggests, can be used to devise immunization strategies that are more targeted than random vaccination campaigns. They found it hardly matters whom you inoculate, unless you are certain of how people are interacting with others. “Almost nothing was better than the random strategy unless you measure who interacts with who and for how long in a typical day,” Salathé says.

The study was published in Proceedings of the National Academy of Sciences earlier this week.

This post was originally published on Smartplanet.com