Hashtag health: Using social media to track flu outbreaks

Monitoring tweets can help experts
identify flu patterns, researcher says.

A social media–monitoring program 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.

The Centers 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 U.S. cities and monitored tweets originating from within a 17-mile radius of those cities. Whenever people tweeted the keywords "flu" or "influenza," the program 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 containing "influenza."

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.

Method picked up on outbreaks earlier

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 detecting daily."

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."

Source: San Diego State University

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Facility management: Humidity reduces airborne flu

Better facility management can reduce
sickness and absenteeism rates.

A recent study carried out by the US National Institute for Occupational Safety & Health (NIOSH) has shown that the airborne transmission of the influenza virus is significantly reduced by maintaining an atmosphere of 40% relative humidity (RH) and above.

To test the effects of humidity on airborne influenza, aerosols of flu virus were “coughed” into a room’s atmosphere by a mechanical manikin at humidity ranging from 7-73%RH, while the air intake from a breathing manikin in the room was monitored.

The air inhaled by the breathing manikin showed that at ≤23%RH the airborne flu virus retained 71-77% infectivity, while at ≥43%RH infectivity dropped to just 15-22%. The study showed that inactivation of the virus at the higher humidity occurred rapidly after coughing with most of the decline occurring in the first 15 minutes.

The study concluded that maintaining relative indoor humidity at 40%RH and above will significantly reduce the infectivity of an aerosolized influenza virus.

It is estimated that over 7.6million working days are lost in the UK each year as a result of flu-related sickness, costing the UK economy over £1.35 billion per annum. Most experts think that flu is spread by tiny droplets created when people with flu cough or sneeze, which can remain in the air for hours.

“This study shows how important it is to maintain an optimum humidity in the workplace to reduce absenteeism and especially in areas of high risk to airborne viruses, such as hospitals and doctors’ surgeries,” said Tim Scott, a humidification specialist.

“Although many professional bodies, including BSRIA, CIBSE and HSE, all recommend maintaining indoor humidity at above 40%RH it is not uncommon to see humidification systems being turned off to reduce operating costs. A low humidity is not as noticeable by employees as a low temperature, so it can go unnoticed. However, the true cost of not maintaining indoor humidity can be poor staff health and an increase in absenteeism, which can far outweigh the cost of operating the building’s humidification system.” Scott said.

Source: Mousetrap Media Ltd

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Paid sick leave reduces spread of flu

photo: freedigitalphotos.net

Allowing all employees access to paid sick days would reduce influenza infections in the workplace, according to a first-of-its-kind analysis by University of Pittsburgh Graduate School of Public Health modeling experts.

The researchers simulated an influenza epidemic in Pittsburgh and surrounding Allegheny County and found that universal access to paid sick days would reduce flu cases in the workplace by nearly 6 percent and estimated it to be more effective for small, compared to large, workplaces. The results are reported in the online version of the American Journal of Public Health and will be in the August print issue.

"The Centers for Disease Control and Prevention recommends that people with flu stay home for 24 hours after their fever breaks," said lead author Supriya Kumar, Ph.D., M.P.H., a post-doctoral associate in Pitt Public Health's Department of Epidemiology. "However, not everyone is able to follow these guidelines. Many more workers in small workplaces than in large ones lack access to paid sick days and hence find it difficult to stay home when ill. Our simulations show that allowing all workers access to paid sick days would reduce illness because fewer workers get the flu over the course of the season if employees are able to stay home and keep the virus from being transmitted to their co-workers."

In addition to investigating the impact of universal access to paid sick days, Dr. Kumar and her colleagues looked at an alternative intervention they termed "flu days," in which all employees had access to one or two paid days to stay home from work and recover from the flu. The idea behind flu days is that they encourage employees to stay home longer than they currently do, thus reducing the potential for them to transmit illness to colleagues at work.

Giving employees one flu day resulted in more than a 25 percent decrease in influenza infections due to workplace transmission. A two flu-day policy resulted in a nearly 40 percent decrease. The researchers found that flu days were more effective for larger workplaces, defined as having 500 or more employees.

"These findings make a strong case for paid sick days," said Dr. Kumar. "Future research should examine the economic impacts of paid sick-day policies."
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