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Model could help determine quarantine measures needed to reduce Covid-19’s spread

Some of the research described in this article has been published on a preprint server but has not yet been peer-reviewed by experts in the field.

As Covid-19 infections soar across the U.S., some states are tightening restrictions and reinstituting quarantine measures to slow the virus’ spread. A model developed by MIT researchers shows a direct link between the number of people who become infected and how effectively a state maintains its quarantine measures.

The researchers described their model in a paper published in Cell Patterns in November, showing that the system could recapitulate the effects that quarantine measures had on viral spread in countries around the world. In their next study, recently posted to the preprint server medRxiv, they drilled into data from the United States last spring and summer. That earlier surge in infections, they found, was strongly related to a drop in “quarantine strength” — a measure the team defines as the ability to keep infected individuals from infecting others.

The latest study focuses on last spring and early summer, when the southern and west-central United States saw a precipitous rise in infections as states in those regions reopened and relaxed quarantine measures. The researchers used their model to calculate the quarantine strength in these states, many of which were early to reopen following initial lockdowns in the spring.

If these states had not reopened so early, or had reopened but strictly enforced measures such as mask-wearing and social distancing, the model calculates that more than 40 percent of infections could have been avoided in all states that the researchers considered. In particular, the study estimates, if Texas and Florida had maintained stricter quarantine measures, more than 100,000 infections could have been avoided in each of those states.

“If you look at these numbers, simple actions on an individual level can lead to huge reductions in the number of infections and can massively influence the global statistics of this pandemic,” says lead author Raj Dandekar, a graduate student in MIT’s Department of Civil and Environmental Engineering. 

As the country battles a winter wave of new infections, and states are once again tightening restrictions, the team hopes the model can help policymakers determine the level of quarantine measures to put in place.

“What I think we have learned quantitatively is, jumping around from hyper-quarantine to no quarantine and back to hyper-quarantine definitely doesn’t work,” says co-author Christopher Rackauckas, an applied mathematics instructor at MIT. “Instead, good consistent application of policy would have been a much more effective tool.”

The new paper’s MIT co-authors also include undergraduate Emma Wang and professor of mech


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