MIT’s AI predicts catastrophe if social distancing restrictions relax too soon

Navin Bondade 0 Comments Artificial Intelligence, Data Science, Deep Learning, Machine Learning

COVID-19 is the major issue right now, there are more than 2.5 lakh cases of COVID-19 around the world. The vaccine for COVID-19 is still in the first phase & the only way to be safe from it is by following social distancing.

Many studies have shown that locking down the sensitive & hotspot areas of COVID-19 is helping many countries to contained this Chinese virus. One such study is from MIT, in a recently published paper, MIT describes a model that determine the efficacy of quarantine measures & better predict spread of the virus.

One interesting fact of this model is that unlike previous models it doesn’t rely on data from studies about previous outbreaks, like SARS or MERS. The model is trained to capture the number of infected individuals under quarantine using the SEIR model and measures the impact of quarantine & spread of the virus in area.

According to MIT researchers, they have trained this artificial intelligence-based model to extrapolate publicly-available data for insights into the disease’s spread, taking into account how different governments handled social distancing and quarantine orders as well as other standard epidemiology parameters.

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 USA, the effective reproduction number of COVID-19 remains greater, meaning the virus has continued to spread exponentially.

“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 engg.

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).

The MIT artificial intelligence-based model was trained on data collected from China’s Wuhan, Italy, South Korea, and the U.S. after the 500th case was recorded in each region (January 24, February 27, and February 22 for Wuhan, Italy, and South Korea, respectively) until April 1.

Armed with precise data from each of these countries, the team took the standard SEIR model & augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection.

After 500 iterations, it learned to predict patterns in the infection spread, drawing a correlation between quarantine measures and a reduction in the virus’s effective reproduction number.

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

“The neural network is learning what we are calling the ‘quarantine control strength function,’” explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections.

In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

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

The machine learning algorithm Dandekar and Barbastathis have developed predicted that the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely between April 15 and April 20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

“This is a crucial moment. If we relax quarantine measures, it could lead to disaster,” said Barbastathis. “If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic.” a crucial moment.

If we relax quarantine measures, it could lead to disaster,” says Barbastathis. As per Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly.

While the team didn’t study Singapore’s COVID-19 cases in their research, the second wave of infection this country is currently experiencing reflects their model’s finding of the correlation between quarantine measures and infection rate.

“If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic,” Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform COVID-19 quarantine strategies that can successfully slow the rate of infection.