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. Mary Beth Gallagher writes in MIT News that Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into “susceptible,” “exposed,” “infected,” and “recovered.” Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others. Raj Dandekar, a Ph.D. candidate studying civil and environmental engineering, and George Barbastathis, professor of mechanical engineering, enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.