Five years ago this month, one of the first U.S. outbreaks of the H1N1 virus swept through the Washington State University campus, striking some 2,000
A university math and biology professor has used a trove of data
gathered at the time to gain insight into how only a few infected people could
launch the virus's rapid spread across the university community.
2009 semester hadn't even started when the first cases came in to the
university's Health and Wellness Services clinic – 11 one day, and just two days
later, 47. Not two weeks later, doctors and nurses in the clinic saw 164 H1N1
patients, attending to a total of nearly 1,000 sick people, plus hundreds more
by phone. They ran out of Tamiflu, an antiviral medication.
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wasn't as intense as feared. People felt awful for three or four days and were
close to normal within a week. No one died.
WSU took on the national distinction of having one of the first and largest
H1N1 outbreaks at an American college. The epidemic also gave Elissa Schwartz,
an assistant professor of both math and biological sciences, an ideal
phenomenon for scientific study.
time, Schwartz was teaching students about the behaviour of epidemics in a
closed population. She had her students search the scientific literature
looking for studies that tracked actual epidemics in closed populations, which
have no movement in our out. They found very few.
Real live data
had a fairly closed population in Pullman, more specifically College Hill,
where many students live, often in shared housing. When they do leave the
house, they're on campus, in close proximity to more people. With the exception
of semester breaks and the occasional road trip, they rarely leave.
thought, 'Oh, if we can get data on this, then that will be real live data, not
simulated data, on the actual number of infections in this community,'"
Schwartz recently told Washington State Magazine. "And it turned
out that the Health and Wellness Services was tracking it, which was
analyze the numbers, Schwartz used a computer model called FluTE, which can
simulate the transmission of an influenza virus across a population and tease
out things like how many became infected, how many carriers first had it and
what strategies would make the biggest difference in containing its spread.
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is measured by the R0, or R naught, a term made somewhat popular in the movie
"Contagion". It's the average number of people infected by one person
in a fully susceptible population.
pegged the R naught for the Pullman outbreak at 2.2, meaning one infected
person ended up passing his or her infection on to roughly two others. That's
close to the rate attributed to the massive 1918 flu pandemic, which killed
more people than the bubonic plague.
Potentially exposed people
analysis also suggests the outbreak was started by as few as 20 people
initially infected by the virus. It's a remarkably low number of people given
the number of people who ultimately got sick.
given that it was spreading as fast as it was," Schwartz said, "and
people were living in close proximity as they were, which means the contact
rate is really high, then perhaps the number of carriers wasn't low."
Schwartz wondered what strategy might have worked best to contain the outbreak,
from vaccinations to isolation to quarantines, or all of the above. Sick people
were asked to isolate themselves from others, but that is difficult, Schwartz
said. A sick person can still share a bathroom with others.
Read: H1N1 discovery paves way for universal flu vaccine
quarantine would contain potentially exposed people, she said. But it would be
difficult to carry out because it's unclear how to define a sick person's "nearest neighbours" when many live in large shared houses such as fraternities,
sororities or dormitories.
analysis does show, though it may sound obvious, that vaccination would be the
best way to control these types of infections," said Schwartz. Her study
was published last year in the Journal of Biological Systems and she presented
her findings in July at the Society for Mathematical Biology Annual Meeting in
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