Tuesday, August 12, 2014

How A Computer Algorithm Predicted Ebola Outbreak Before WHO's Announcement.

When the Ebola outbreak first took hold in West Africa, eyewitness reports, social media, and other informal accounts from hospitals or local health workers were among the only sources of information available. Later reports indicate that the first case likely emerged in Guinea on February 9, though it was not known to be Ebola at the time.

The early days of the epidemic were marked by confusion and fear among the public; just as taken aback were the under-prepared health care workers, who had never seen Ebola in this part of Africa before.


Perhaps in countries like Uganda, South Sudan, and the Democratic Republic of the Congo, where Ebola has struck numerous times over the past few decades, symptoms of the virus would have raised alarm bells right away.
But when the first people started getting sick with symptoms like diarrhea, vomiting, and muscle pain, health care workers did not immediately suspect xEbola. The first round of testing ruled out common illnesses, like Malaria. Then came testing for rarer diseases — in this case, they strongly suspected the cause of the symptoms was Lassa Fever, a viral disease endemic in West Africa.
By the time doctors realized that they might be working with Ebola, countless patients had already been seen in hospitals and clinics with no special protections to avoid spreading the virus to others.

And even after it became apparent that Ebola was a possibility, in many cases health care workers had neither the training nor the equipment to avoid infecting themselves or other patients.
Because of the initial confusion, official reports of the first patients were slow to come. The first mentions of the Ebola outbreak were confined to conversations between health care workers and families.
Nevertheless, on March 14, HealthMap picked up on reports about an outbreak of a ‘mystery hemorrhagic fever’ that had already killed 8 in Guinea. Then, on March 19, the algorithm detected what may well be the first local news report of a possible Ebola outbreak, triggering HealthMap to issue an alert.
This map shows the first alert for an Ebola-like illness, issued on March 19.

Nine days later, on March 23, the World
Health Organization (WHO) issued its first public statement on the outbreak.
So how did a computer algorithm pick up on the start of the outbreak before the WHO?


As it turns out, some of the first health care workers to see Ebola in Guinea regularly blog about their work. As they began to write about treating patients with Ebola-like symptoms, a few people on social media mentioned the blog posts.

Beyond the unique concept, two features make the tool particularly innovative. First, HealthMapgoes well beyond the standard mashup and is more like a small-scale implementation of the long-awaited semantic web. The site, which the scientists previously describedin the journalPLoS Medicine, creates machine-readable public health information from text indexed by Google News, the World Health Organization updates, reports from the Centers for Disease Control and other public health agencies, online listserv discussions, Twitter, and a number of other sources.

HealthMap uses these sources to generate information that includes locations of specific outbreaks and tracks new cases and deaths. In a truly ingenious concept, the system is also capable of logging public sentiment.
Although HealthMap doesn’t have an extremely high level of precision — outbreaks can’t be tracked down to the neighborhood level, for example — it’s far from a traditional Google Maps mashup. The back end of the system does far more than marry data points to locations. According to technical advisers who worked on the project, a host of complex algorithms underpin the simple interface that the site’s users see.

The second really critical feature is that the site is free for users. By doing it all with publicly available news sources and low operating costs, the service itself remains free. After a small-scale launch in 2006, the site’s model and potential attracted a $450,000 grant last year from Google.org’s Predict and Prevent Initiative, which is focused on emerging infectious diseases.

HealthMap’s free accessibility is particularly important for tracking diseases in the developing world, where poor public health infrastructure and lack of money has handicapped epidemiological efforts.

And it didn’t take long for HealthMap to detect these mentions. While social media isn’t necessarily the most reliable source of news, HealthMap’s algorithm is specifically designed to sort out the “noise” from the “signal” as it digs through the data, making the results far more useful.

No comments:

Post a Comment