Source: unite.ai
An AI-driven health monitoring and disease detection platform was able to catch the signs of the Wuhan viral outbreak approximately a week before government agencies warned the public, providing a look at how AI can be used to catch disease outbreaks in a timely fashion.
While the official World Health Organization notification of the Wuhan virus went out on January ninth and the US Center for Disease Control and Prevention (CDC) received word of the outbreak on January sixth, the first warning signs of the outbreak were picked up by a Canadian health monitoring system almost a week prior. As Wired reported, the AI-driven health system BlueDot warned its clients about the possible outbreak on December 31st. Bluedot uses AI algorithms to monitor different global news sources and detect patterns in health reports. It also takes into account information on plant and animal disease networks. Using the information it collects, BlueDot epidemiologists then delivers warnings and predictions about possible health risks and outbreaks to its subscribers.
When dealing with an outbreak of disease, early detection is always better. The earlier the detection, the more time health officials have to respond. In the case of the Wuhan virus and other disease outbreaks in China, the Chinese government has often been slow in sharing information with global public health officials. This possesses a problem as the CDC and WHO rely on communications from other government agencies to plan their own responses. However, if an AI system like BlueDot can make accurate predictions based on the information that leaks through across many individual news reports, blogs, and forums, this could potentially enable health organizations to act quicker in response to outbreaks.
According to Kamran Khan, the found of BlueDot, the company doesn’t use social media data when predicting the spread of diseases because the data is too variable and messy to be of use. Instead, news reports, data on known animal disease networks, and airline ticketing data is combined to create a model that predicts where infections begin and where infected people may travel next. BlueDot was correctly able to predict that the Wuhan virus would spread to Taipei, Tokyo, Seoul, and Bangkok within a few days of its manifestation.
BlueDot was launched by Khan in 2014, and the company currently has 40 employees, including data scientists, physicians, and programmers who work together to create the disease surveillance and prediction models. Machine learning algorithms and natural language processing techniques are used to mine data from news reports spanning the globe and covering 65 different languages. Khan said to Wired:
“What we have done is use natural language processing and machine learning to train this engine to recognize whether this is an outbreak of anthrax in Mongolia versus a reunion of the heavy metal band Anthrax.”
After the automated data collection and initial analysis are complete, human analysts double-check the data and ensure that the model’s conclusions seem sound. Finally, a report is generated and sent out to the clients of the application.
BlueDot’s system is far from the first attempt by the AI field to predict the spread of diseases. Data scientists have been using big data and machine learning models to track the spread of various diseases like for some time now, with some attempts being more successful than others. Google tried its own hand at tracking the spread of disease with Google Flu Trends, but its attempts to predict the severity of the 2013 flu seasons were reportedly off by about 140%. Only time will tell if BlueDot can consistently predict the spread of diseases, but if it can it could pave the way for faster, more accurate estimates of disease outbreaks.
