A social media–monitoring programme led by
San Diego State University geography professor Ming-Hsiang Tsou could help
physicians and health officials learn when and where severe outbreaks are
occurring in real time. In results published last month in the Journal of
Medical Internet Research, Tsou demonstrated that his technique might allow
officials to more quickly and efficiently direct resources to outbreak zones
and better contain the spread of the disease.
"There is the potential to use social
media to really improve the way we monitor the flu and other public health
concerns, "Tsou said.
Unpredictability of outbreaks
The Centres for Disease Control and
Prevention (CDC) defines flu season as the period from October through May,
usually peaking around February. But the unpredictability in exactly when and
where outbreaks occur makes it difficult for hospitals and regional health
agencies to prepare for where and when to deploy physicians and nurses armed
with vaccines and medicines.
There's about a two-week lag in the time
between hospitals first noticing an uptick in flu patients and the CDC issuing
a regional warning. Tsou and his colleagues, funded by a $1.3 million grant
from the National Science Foundation, wanted to find a quicker, more efficient
way to identify these patterns.
They selected 11 US cities and monitored
tweets originating from within a 17-mile radius of those cities. Whenever
people tweeted the keywords "flu" or "influenza", the
programme would record characteristics about those tweets, including username,
location, whether they were original tweets or retweets, and whether they
linked to a Web site.
From June 2012 to the beginning of December,
the algorithm recorded 161 821 tweets containing the word "flu", 6 174
Tsou compared his team's findings to
regional data based on the CDC's definition of influenza-like illnesses (ILI).
Nine of the 11 cities showed a statistically significant correlation between an
increase in the number of tweets mentioning those keywords and regionally
reported outbreaks. In five of those cities, Tsou's algorithm picked up on the
outbreaks earlier than the regional reports. The cities with the strongest
correlations were San Diego, Denver, Jacksonville, Seattle and Fort Worth.
"Traditional procedures take at least
two weeks to detect an outbreak," Tsou said. "With our method, we're
Original tweets and tweets without Web site
links also proved more predictive than retweets or those that did include
links, possibly because original and non-linking tweets are more likely to
reflect individuals posting about their own symptoms, Tsou said.
The next step in Tsou's ongoing research
will be hunting for even finer-grained correlations between ILI data and
specific symptomatic keywords like "cough", "sneeze",
"congestion", and "sore throat".
Tsou envisions this kind of
"infoveillance" applying to a range of public health, such as
monitoring regional incidences of heart attack or diabetes. The project is
connected to a larger SDSU initiative, Human Dynamics in the Mobile Age, one of
the university's four recently selected Areas of Excellence. Tsou is a core
faculty member for the initiative.
"In social media, there's a lot of
noise in the data," Tsou said. "But if we can filter that noise out
and focus on what's relevant, we can find all kinds of useful connections
between real life and cyberspace."