Communications of the ACM
Computer Model Shows Best Ways to Slow the Spread of COVID-19
The model can show where vaccination levels need to be, or what levels of restrictions are necessary to keep a new COVID variant at bay.
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A computational model developed by researchers at Canada’s University of Waterloo is the first to simulate numerous variables impacting COVID-19 transmission, in an effort to slow the spread of variants.
The researchers incorporated raw data to forecast case numbers and hospitalizations, as well as factors like vaccination rates, mask use, lockdowns, and the number of breakthrough infections, into the model.
The model can simulate what would happen with a new variant and what measures would be needed to stop new, more contagious variants, such as optimal vaccination levels or certain restrictions.
Waterloo's Mehrshad Sadria said the model incorporates "vaccination and different vaccine types, delays in second and third doses, the impacts of restrictions, and even the competition among different variants of concern. We want policymakers and stakeholders to have the most pertinent information so they can make the best decisions."
From University of Waterloo News (Canada)
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