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Machine-learning model shows quarantine impact on virus spread

Researchers at MIT deployed a machine learning algorithm to determine the impact of quarantine measures on the spread of COVID-19.

"Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology," Raj Dandekar, a PhD candidate studying civil and environmental engineering, told the MIT News.

Dandekar and George Barbastathis, an MIT professor of mechanical engineering,  focused their analysis on four regions struck by the pandemic: Wuhan, Italy, South Korea and the United States. Their goal was to capture the number of infected people under quarantine, and therefore no longer spreading the infection to others, and apply a well-known disease prediction model, the SEIR, which categorizes people into four groups: susceptible, exposed, infected and recovered.

With precise data from each country, the researchers augmented the standard SEIR model with a neural network that learned how infected individuals under quarantine impact the rate of infection. They put the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, they were able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

In places like South Korea, where strict quarantine measures were quickly implemented, the virus spread plateaued more quickly. In places slower to implement government interventions, like Italy and the United States, the “effective reproduction number” of COVID-19 remains greater than one, meaning the virus has continued to spread exponentially.

"Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one," Barbastathis said. "That corresponds to the point where we can flatten the curve and start seeing fewer infections."

The research is available in the paper: Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning.

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