Dr. Daniel Gillis
By Otaiba Ahsan
From physical distancing to phased reopening of businesses, figuring out which mitigation strategies work best for fighting the COVID-19 pandemic is the goal of a novel simulation tool developed by University of Guelph researchers.
The team is now testing the tool, designed to help policy makers gauge which public health measures will control the spread of the disease most effectively.
“We hope to provide public health opportunities at the local and provincial levels to put into action the best mitigation strategies available, regardless of what the future holds for this pandemic,” says Dr. Daniel Gillis, a professor in the School of Computer Science who is working on the project with several students.
The team developed a spatial environmental agent-based model (eABM). These models simulate individual behaviours and characteristics of a population and are excellent resources for scenario analyses like public health outcomes, says Gillis.
The team used data for Guelph and Ontario populations.
Before creating scenarios, they identified and pooled existing information about COVID-19, including risk factors, co-morbidities and risks associated with different sub-populations. All information retrieved for the study was stored in an open source database.
After consulting existing research, they developed at least three plausible scenarios that might impact Public Health’s ability to respond to the pandemic. The researchers then identified mitigation strategies locally and provincially and estimated how these might help reduce risk severity.
The model accounts for employment, gender, socio-economic status and other factors that may affect COVID-19 transmission and severity.
Gillis says the model will help public health officials and policy makers to compare local and provincial strategies to reduce the health burden in future infection waves. This open source, easy-to-use tool can be readily updated and modified to evaluate local or province-wide mitigation strategies.
Environmental agent-based models have been used to understand the impact of human activities on wild animal populations. The U of G researchers adapted the model to consider the public health impacts of the novel coronavirus.
They began simulations this past fall and are now testing the eABM for accuracy. Gillis says he expects the study results will be published and presented at conferences. Following ongoing discussions, he plans to make the tool available to the pandemic response team of the Public Health Agency of Canada.
“At the end of the day, we want to do our part to help slow down the spread of the virus and help save as many lives as possible, particularly the vulnerable,” he says.
This research is funded by the University of Guelph COVID-19 Research Development and Catalyst Fund.