How AI Is Tracking the Coronavirus Outbreak

With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.

Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.

A company called BlueDot used a similar approach-minus the social media sources-to spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.

The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization.

Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30.

Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves.

Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still aren’t entirely clear.

This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.

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