Photo by: Pixelkult via Pixabay
The rains are here again in many parts of the world, and ‘tis the season for coughing, sneezing, fever, sore throats, body aches, and what have you.
Most likely, you will be posting on Facebook, Twitter, and Instagram that you are not feeling well, tagging your sympathetic friends and relatives, and earning their likes, sad emoticons, and get-well wishes.
Most likely, too, you will be Googling facts about your ailment to know how you’ll get better fast, with or without a check-up with the doctor.
For all you know, while you’re at it, pharmaceutical companies and healthcare businesses are not the only ones interested in looking at your posts to scoop opportunities for selling their wares or services.
Public and private groups in the health sector may also be already tabulating data you post on your social media platforms as well as your Google search, and analyzing them, to know if there could be potential disease outbreak, or at least a trend, in your area.
Last July 25, New Zealand Health Minister Jonathan Coleman disclosed a new project
that looks at whether social media can really help see flu epidemics and outbreaks of other infectious diseases coming.
“We’re in the midst of the cold and flu season, so trying to predict outbreaks of infectious bugs is top of mind,” Coleman said.
“The Ministry of Health is trialling an innovative approach aimed at improving our response to epidemics by predicting outbreaks earlier.”
The Massey University of Environmental Science Limited is helping out on the project, which gets funding from Vote Health, a government funding source, worth US$888 million.
“Social media is often the first place people talk about feeling ill, so trends can be visible on platforms like Twitter and Facebook before doctors are aware of them,” the Health Ministry said in a statement.
Part of the work is also to run a simple online survey to see how New Zealanders would feel about sharing when they feel ill and how they currently use social media to let people know they’re sick, the Health Ministry said.
The Health Ministry’s website provides a link to the survey.
“The earlier health services can see an outbreak coming, the better prepared they can be to cope,” the Health Ministry said.
“This can help with concentrating resources in affected parts of the country and ensuring sufficient stocks of drugs are available.”
Data culled from these new sources will be added to those gathered from existing sources like reports from doctors on how many patients have visited them for a certain illness.
“The information is anonymised; it’s about numbers of cases not the individuals,” the Health Ministry pointed out.
“By adding earlier tracking through social media, it should be possible to build a more comprehensive picture.
The more people that get involved the better prepared New Zealand can be.”
Detecting outbreak by tracking people’s use of social media and web searches is not exactly a new thing.
The Centers for Disease Control (CDC) of the United States took advantage of social media posts made by users about possible symptoms and claims of possible outbreak of the H1N1 virus during the H1N1 pandemic in 2009 and during the influenza epidemic in 2012-2013.
Elaine Nsoesie, a Research Fellow at HealthMap and Harvard Medical School, explained that in such studies, “researchers usually develop a process for collecting tweets containing flu-related terms (such as flu, influenza, and “Tamiflu”), the time at which each tweet is published, and the geographical location from which the tweet is sent.
Researchers then estimate daily and/or weekly flu-like illness from the data. These data sometimes correlate with flu-like illness data collected by CDC.”
In November 2013, the CDC launched a contest named “Predict the Influenza Season Challenge” to encourage researchers to use social media data like on Facebook, Twitter, web search, and web surveys in forecasting flu trend or outbreak.
Winners in the contest got US$75,000 reward from the CDC.
After this, a group of researchers, Nsoesie said, even presented an approach for combining multiple data sources, including Twitter, Google Flu Trends, and online news reports to predict flu activity.
The study was titled “Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions.
So, next time you post that “unwell” message or picture on your social media accounts, it can help health authorities tell if an outbreak is looming.