“Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology,” explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were 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.The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. One of those students was Dandekar. “The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem,” he says.What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

Read more at: http://news.mit.edu/2020/new-model-quantifies-impact-quarantine-measures-covid-19-spread-0416

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