蓝莓视频

Researchers at the University of 蓝莓视频 created the first computational model that simulates many variables affecting the transmission of COVID-19 to slow the spread of variants.

The model takes raw data already in use to forecast case numbers and hospitalizations, and then adds 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鈥檚 recent experience with COVID-19 and data from the Ontario COVID-19 Science Advisory Table.

鈥淲e were actually building the model when the Delta variant was still the dominant one in Ontario,鈥� said Anita Layton, professor of applied mathematics at 蓝莓视频 and Canada 150 Research Chair in mathematical biology and medicine. 鈥淲e simulated a variant that was similar to Omicron, and the model is helpful for understanding whatever variants will come next.鈥�

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

鈥淚t 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,鈥� said Mehrshad Sadria, a PhD student in applied mathematics at 蓝莓视频 who also worked on the new model. 鈥淲e want policymakers and stakeholders to have the most pertinent information so they can make the best decisions.鈥�

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

鈥淲e鈥檇 like to investigate how people of different ages are impacted and compare different levels of vaccination between and within age groups,鈥� Layton said. We鈥檙e 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.鈥�

The research team鈥檚 paper on the computation model appears in . The project grew from a research collaboration in the University Health Network and expanded with rapid research funding from the Canadian Institutes of Health Research.