Will Twitter Predict a Flu Outbreak Before the CDC?
As we enter the final weeks of the year, looking back we can note that this year had various devastating health outbreaks around the world. Ebola in 2014 remains to be the largest outbreak in history and the first in West Africa, Madagascar is currently under surveillance due to an outbreak of the Black Death, and China has reported multiple cases of human infection with avian influenza.
As the colder weather settles in, there is something else that comes with the holiday cheer: the flu. It’s not possible to predict what this flu season will be like, but if it’s anything like last year – with more than 105 flu-related deaths in children alone being reported – it could be epidemic.
Which brings us to our question, can Twitter track the flu and predict outbreaks faster than the Centers for Disease Control and Prevention (CDC)?
A Difference in Tracking Methods
Traditional flu tracking performed by the CDC relies on outpatient reporting and virological test results supplied by laboratories nationwide that confirms an outbreak within two weeks after they begin. The CDC does not track all cases, however.
Instead of enduring this labor-intensive and time-consuming approach, researchers can capture comments from people with the flu who are sending out status messages, providing daily reports.
The social media site unveiled a new grant program earlier this year that will allow outside researchers to mine its stockpile of tweets, and Johns Hopkins is one example of an institute taking advantage of this form of flu tracking.
A team from Johns Hopkins and George Washington universities conducted a study to track flu-related tweets from New York City. The team concluded that Twitter data can accurately gauge the spread of flu at the local level, too.
Citing data from the 2012-2013 U.S. flu season, the research team reported on results they obtained by sifting through billions of tweets to identify flu infections and where flu patients were located.
“We found that we could do just as well in predicting flu trends in New York City as we did nationally,” said Mark Dredze, an assistant research professor of computer science at Johns Hopkins who supervised the research. “That’s critical because decisions about what to do during a flu epidemic are largely made at the local level.”
Success Found in Numbers
During last year’s severe flu season, the team members compared their national Twitter flu findings with data that the CDC collected from health care providers. They also compared their results with flu cases compiled by the New York City Department of Health and Mental Hygiene.
“Not only did our results track trends on the national level, but they also did so on the local level,” said David A. Broniatowski, lead author of the study. “It gives our system validity. It shows that we’re measuring what we say we’re measuring, that we’re tracking very useful information. And that localized data is valuable because the flu activity in, say, Boise, Idaho, may be quite different from the national flu trends.”
Twitter’s Data Grants program will give scholars access to its public and historical data for use in garnering helpful information on various topics. Techniques used to track flu trends via Twitter might also be applied to the study of subjects such as HIV-incidence and drug-related behaviors.
“The exciting results we’ve come up with so far bring up new questions that will require additional data that the Twitter grant program may enable us to work with,” said a graduate student on the team. “The more experiments we do with Twitter posts, the more proof I see that this is a great idea.”
C. Light aim to detect Alzheimer's with AI and eye movements
C. Light Technologies, a neurotechnology and AI company based in Boston, has received funding for a pilot study that will assess changes in eye motion during the earliest stage of Alzheimer's, known as mild cognitive impairment.
C. Light Technologies has partnered with the UCSF Memory and Aging Center for this research. As new therapeutics for Alzheimer’s are introduced to the clinic, this UCSF technology has the potential to provide clinicians a better method to measure disease progression, and ultimately therapeutic efficacy, using C. Light’s novel retinal motion technology.
Eye motion has been used for decades to triage brain health, which is why doctors asks you to “follow my finger” when they want to assess whether you have concussion. In more than 30 years of research, studies have revealed that Alzheimer’s disease patients' eye movements are affected by the disease, though to date, these eye movements have only been measured on a larger scale.
C. Light’s research takes the eye movement tests to a microscopic level for earlier assessments. Clinicians can study and measure eye motion on a scale as small as 1/100th the size of a human hair, which can help them monitor a patient’s disease and treat it more effectively.
The tests are also easy to administer. Patients put their chin in a chinrest and focus on a target for 10 seconds. The test does not require eye dilation, and patients are permitted to blink. A very low-level laser light is shown through the pupil and reflects off the patient’s retina, while a sensitive camera records the cellular-level motion in a high-resolution video. This eye motion is then fed into C. Light’s advanced analytical platform.
“C. Light is creating an entirely new data stream about the status of brain health via the eye,” explains Dr. Christy K. Sheehy, co-founder of C. Light. “Our growing databases and accompanying AI can change the way we monitor and treat neurological disease for future generations. Ultimately, we’re working to increase the longevity and quality of life for our loved ones."
At the moment developing therapeutic treatments for the central nervous system is difficult, with success rates of only 8% to go from conception to market. One reason for this is the lack of tools to measure the progression of diseases that impact the nervous system.
Additionally clinical trials can take a decade to come to fruition because the methods used to assess drug efficacy are inefficient. C. Light believe they can change this.
“Before this year, it had been almost 20 years since an Alzheimer’s drug was brought to market" explains Sheehy. "Part of the reason for this very slow progress is that drug developers haven’t had viable biomarkers that they can use to effectively stratify patients and track disease on a fine scale. The ADDF’s investment will allow us to do that."
C. Light has received the investment from the Alzheimer’s Drug Discovery Foundation (ADDF) through its Diagnostics Accelerator, a collaborative research initiative supported by Bill Gates, the Dolby family, and Jeff Bezos among other donors.
C. Light recently completed its second and final seed round raising $500,000, including the ADDF investment, which brings their total seed funding to more than $3 million. Second round seed funders included: ADDF, the Wisconsin River Business Angels, Abraham Investments, LLC and others.
The ADDF’s Diagnostics Accelerator has made previous investments in more than two dozen world-class research programmes to explore blood, ocular, and genetic biomarkers, as well as technology-based biomarkers to identify the early, subtle changes that happen in people with Alzheimer’s.