Relaxing quarantine rules too soon will be catastrophic for the US and see explosion in cases, shows MIT model
Relaxing or reversing quarantine or stay-at-home measures in the US could be "catastrophic" and lead to an "exponential explosion" in COVID-19 infections, according to MIT researchers. The analysis is based on a new MIT model created by a team of engineers that quantifies the impact of quarantine measures on COVID-19's spread.
"In the case of the US, our model captures the current infected curve growth and predicts a halting of infection spread by April 20," say MIT researchers Raj Dandekar and George Barbastathis.
"We further demonstrate that relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid-March 2020," they added.
The findings are critical and come at a time when President Donald Trump has released guidelines to reopen America in a phased manner.
"Our mixed model analysis for the US, employing quarantine rates learned from the models of Wuhan, Italy and South Korea in the US model starting from April 1, 2020, reveals that stronger quarantine policies might lead to an accelerated plateauing in the infected case count, and subsequently smaller infected case count," say the team in their findings, a pre-print version of which has been published.
"On the other hand, we forecast that relaxing or abandoning the quarantine policies gradually over the period of the next 17 days may well lead to approximately 1 million infections without any stagnation in the infected case count by mid-April 2020," they add.
The MIT model
According to experts, existing models analyzing the role of travel restrictions in the spread of COVID-19 either used parameters based on prior knowledge of SARS/MERS coronavirus epidemiology and not derived independently from the COVID-19 data or were not implemented on a global scale.
The MIT model, on the other hand, 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.
Most models used to predict the spread of disease follow what is known as the SEIR model, which groups people into "Susceptible, Exposed, Infected and Recovered." categories.
The MIT researchers 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.
According to researchers, the model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.
"Our proposed model is globally applicable and interpretable with parameters learned from the current COVID-19 data, and does not rely upon data from previous epidemics like SARS/MERS," says the team.
The results
Leveraging their neural network augmented model, the team focused their analysis on four locales: Wuhan, Italy, South Korea, and the US. They "compared the role played by the quarantine and isolation measures in each of these countries in controlling the effective reproduction number of the virus."
All four regions that researchers applied their model to had developed infected and exposed populations that are sufficiently large to train models. 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 results show a generally strong correlation between the strengthening of quarantine controls, that is, increasing quarantine rates as learned by the neural network model — actions taken by the regions' respective governments, and decrease of the effective reproduction number," says the study.
The analysis shows that quarantine restrictions are successful in getting the effective reproduction number from "larger than one to smaller than one." That corresponds to the point where countries can flatten the curve and start seeing fewer infections, according to experts.
The model, for example, 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 the US and Italy, the "effective reproduction number" of COVID-19 remains greater than one, meaning the virus has continued to spread exponentially.
"Our results unequivocally indicate that the countries, in which rapid government interventions and strict public health measures for quarantine and isolation were implemented, were successful in halting the spread of infection and prevent it from exploding exponentially," states the analysis.
In the case of Wuhan especially, where the available data were earliest available, experts were able to test the predicting ability of their model by training it from data in the January 24 till March 3 window and then matching the predictions up to April 1.
Even for Italy and South Korea, the team says they had a buffer window of one week (March 25-April 1) to validate the predictions of the model.
For the US, quarantine rates show a stagnation till March 20, after which it shows a sharp increase accompanied with a decrease in reproduction number, which is in alignment with the ramping up of government policies and quarantine interventions post-mid-March in the worst affected states like New York, New Jersey, California, and Michigan, says the team.
The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the US will arrive somewhere between April 15-20.
"The machine learning algorithm predicted that the US 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 US before the rate of infection starts to stagnate," the findings state.
Over 671,420 cases have been reported from across the US as of April 17, and over 33,280 have died in the COVID-19 pandemic, shows the Johns Hopkins tracker.
The researchers point towards Singapore, though the country was not part of the study. They say one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly.
The second wave of infection Singapore is currently experiencing reflects the model's finding of the correlation between quarantine measures and infection rate, say experts.
"If the US were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic," warns the team.