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.
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 PhD 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).
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.
Read more at Massachusetts Institute of Technology
Image: This figure shows the model prediction of the infected case count for the United States following its current model with quarantine control and the exponential explosion in the infected case count if the quarantine measures were relaxed. On the other hand, switching to stronger quarantine measures as implemented in Wuhan, Italy, and South Korea might lead to a plateau in the infected case count sooner.
Image courtesy of the researchers.
