Researchers develop computer model to show ways to curb Covid-19 spread

Technology has proved to be a major source of hope in the global fight against Covid-19. In wake of this, researchers at the University of Waterloo created the first computational model that simulated many variables affecting the transmission of Covid-19 to slow the spread of variants.

Their study was published in ‘Scientific Reports’.

The model took raw data already in use to forecast case numbers and hospitalizations, and then added other factors, such as vaccination rates, the use of masks and lockdowns, and the number of breakthrough infections.

The researchers based their computation model on Ontario’s recent experience with Covid-19 and data from the Ontario Covid-19 Science Advisory Table.

“We were actually building the model when the Delta variant was still the dominant one in Ontario,” said Anita Layton, professor of applied mathematics at Waterloo and Canada 150 Research Chair in mathematical biology and medicine.

“We simulated a variant that was similar to Omicron, and the model is helpful for understanding whatever variants will come next,” she said.

The research team could change the parameters of the computational model to see what would happen with a new variant. It could also show what it would take to stop variants that are more contagious than others are. As a result, the model could show where vaccination levels needed to be or what levels of restrictions were necessary to keep a new variant at bay.

“It includes vaccination and different vaccine types, delays in second and third doses, the impacts of restrictions and even the competition among different variants of concern,” said Mehrshad Sadria, a PhD student in applied mathematics at Waterloo who also worked on the new model. “We want policymakers and stakeholders to have the most pertinent information so they can make the best decisions.”

The researchers planned to develop the model to include even more factors that influence the spread of Covid-19 in specific communities.

“We’d like to investigate how people of different ages are impacted and compare different levels of vaccination between and within age groups,” Layton said.

“We’re also looking to make it more refined so we can focus on specific regions of Ontario, which can then be helpful for looking at resource distribution,” Layton concluded.