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April 17, 2020 08:46 pm

MIT's AI Suggests That Social Distancing Works

In a preprint academic paper published in early April, MIT researchers describe a model that quantifies the impact of quarantine measures on the spread of COVID-19, the novel coronavirus. From a report: Unlike most of the models that have so far been proposed, this one doesn't rely on data from studies about previous outbreaks, like SARS or MERS. Instead, it taps an AI algorithm trained to capture the number of infected individuals under quarantine using the SEIR model, which groups people into classes like "susceptible," "exposed," "infected," and "recovered." This approach potentially achieves accuracy higher than or comparable to previous work, which could help to better inform governments, health systems, and nonprofits as they make treatment and policy decisions about social distancing. For instance, the model found that in places like South Korea, where there was immediate government intervention, the virus spread plateaued more quickly. "Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one. The [model] is learning what we are calling the 'quarantine control strength function,'" said George Barbastathis, MIT professor of mechanical engineering, who developed the model over the course of several weeks with civil and environmental engineering Ph.D. candidate Raj Dandekar as a part of a final class project. "That corresponds to the point where we can flatten the curve and start seeing fewer infections."

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Original Link: http://rss.slashdot.org/~r/Slashdot/slashdot/~3/ijuM-R0LNFo/mits-ai-suggests-that-social-distancing-works

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