Machine learning algorithm quantifies the impact of quarantine measures on COVID-19's spread

Every day for the past few weeks, charts and graphs plotting the projected apex of COVID-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the COVID-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus. "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 Ph.D. candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).