Busy High School Model Shows How Diseases Spread

Students and staff in a high school had 762,868 encounters in a single day.

THE GIST

Scientists tracked the movements of everyone in a high school for a day.

The research helps explain how diseases like the flu can move through a population.

Using such models, scientist hope to better predict, and then limit, the spread of infectious diseases.

Last January several hundred bleary eyed students filed into an unnamed American high school to accept an unusual assignment: wear a matchbox-sized device around their neck for the day.

As the students passed each other in the halls, lined up for lunch, and listened to their teachers and administrators (who also sported the boxy jewelry), the devices recorded every encounter, or occasion when the devices came within 10 feet of each other. Ten feet is considered the maximum distance that spit, phlegm, or snot infected with influenza can travel.

By the end of the school day the devices had recorded an astonishing 762,868 encounters among the students, staff, teachers and administrators of the high school, far more than the scientists from Stanford University were expecting or had been reported before.

"It was clear that there were a lot of interactions going on," said Marcel Salathe, a co-author on a new paper that appears in the Proceedings of the National Academy of Sciences. "But the sheer numbers were amazing."

By tracking the comings and goings of an entire high school, the scientists from Stanford have collected the most detailed information to date of how diseases like influenza can spread between individuals. With this knowledge public health officials hope to improve their predictions about how infectious diseases spread through cities or countries and devise new ways to slow or stop the spread of infectious diseases.

The Stanford scientists studied a high school because of schools' key role in spreading infections.

"A school connects an entire community," said Salathe.

Once an infection gets into a high school, an environment packed with so many people in such a small space for such long periods of time day after day, a pathogen readily spreads to parents, siblings and then the parents' coworkers and other people.

But who exactly is doing the spreading? Or, in other words, which students or staff members are popular, both among other students and staff and among pathogens? If Salathe and his colleagues could identify those individuals with the most encounters, then they might consider selectively vaccinating those people first to slow the spread of a disease, said Salathe.

"If you ask people who are the most popular students, and those are the people that we identify as the ones with the most interactions, then finding the people with the most interactions is relatively easy; you just ask who is popular," said Salathe.

Salathe can't say whether the boys and girls who are popular among their peers are a modern-day version of "Typoid Mary" just yet though. (Typhoid Mary was a cook in the mid-1800's who was a healthy carrier of typhoid fever and who unwittingly infected dozens of people.) The high school data was collected completely anonymously, so the scientists have no way to connect a particular device to a particular individual. But it is one question they hope to answer in the future.

And the research will have to be repeated at other schools, said Stephen Eubank, a professor of at the Virginia Bioinformatics Institute, which is part of Virginia Tech.

If the Stanford scientists can identify that a particular cafeteria worker, popular student, or well-connected administrator has the most contacts, then, in the event of a pandemic with a limited amount of vaccine, those people could be vaccinated, have antivirals administered, or told to stay home during an outbreak to limit its spread, said Eubank.

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

"You can actually create a network" with this new research, said Marc Lipsitch, a professor of epidemiology at Harvard University. "You get another level of resolution that you can add to those larger models" and improve their predictions.

Ira Longini, a professor of biostatistics at the University of Washington School of Public Health, agreed that the research will help improve the computer models scientists use to predict the spread of diseases.

"I think that for something like the flu, if we had this kind of information on households, schools, day care centers, and workplaces, then we could string it together in our models, and that would be useful," in determining whether or when schools should be closed or who should be vaccinated or treated with antivirals so they don't get infected in the first place, said Longini.

Gathering that kind of data will be difficult though, said Salathe. It was hard enough getting an entire high school to wear the devices for a single day. Getting an entire company or office building to wear the device will be even harder. And using cell phone records to track a population isn't really an option either; cell phone signals just aren't precise enough for the needs of the scientists.

People are making the best recommendations with the best available research, but until we have more studies like this we won't really know what the best interventions are, said Eubank.

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