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Using his expertise in mathematics and statistics, Professor Jianhong Wu is working to model the future impacts of COVID-19 and its variants.

By Elaine Smith

Throughout the COVID-19 pandemic, Faculty of Science mathematics and statistics Professor Jianhong Wu has been working non-stop with both federal and provincial agencies and a National Modelling Task Force to project the spread of the disease and its variants throughout the country – a testament to both his expertise and 91��ɫ’s leadership in mathematical modelling.

Wu, a Distinguished Research Professor at 91��ɫ, is one of Canada’s most prolific researchers for publications in COVID-19 and mathematical modelling and joins several other 91��ɫ faculty members who helped lead the way with research in the area (SciVal, 2021).

It’s nothing new to Wu, who is . He was tasked in 2003 with leading a national network of mathematicians and researchers to model the path of SARS-1. In 2020, the Fields Institute asked him to organize a national modelling Task Force for COVID-19, and he responded to the call by simply reactivating and expanding the network.

“Over the last two decades, my research time has been spent on establishing and leading national teams from one pandemic to another,” says Wu, whose work focused on big data and neural networks prior to the SARS outbreak.

Sitting on multiple provincial, national and international panels, Wu is also a member of the Ontario Modelling Consensus Table that builds consensus – using research results of multiple modelling teams from across Ontario universities – about the projected COVID-19 cases and the disease burden on the health system under a range of intervention scenarios. This consensus has been providing critical data to inform the government’s policy about closures and re-openings: how to do so and how quickly to do so. By integrating mathematical modelling and stochastic optimization, Wu’s group suggested optimal pathways and likely scenarios for escalating or de-escalating social distancing, and estimated the costs and benefits of each – factoring in economics and mental health.

“From SARS-1 onward, we’ve been working with a variety of stakeholders on collecting data of population contacts, drug resistance, vaccine efficacies, waning and vaccination priorities, and health-care system,” Wu says. “The data quality and accessibility has been much improved in Ontario this time, as well as the co-ordination of efforts from different research groups. Each of these modelling teams has a different collection of expertise and that helps cross-validation, which is important when the disease moves so fast and our knowledge about the disease advances fast.”

Wu’s group has also incorporated artificial intelligence into its work, facilitating the real-time processing of “the huge amount of data to identify vulnerable populations and hot spots."

“AI and Mathematics don’t have emotion, and they allow us to think several steps ahead,” says Wu, “but with the disease moving so fast, it has been a challenge to convince the decision makers to take a proactive approach rather than being reactive, and, unfortunately, sometimes with a delay.”

Being at the forefront of pandemic modelling isn’t something Wu anticipated when he arrived at 91��ɫ. Born in China, he came to Canada to pursue post-doctoral research in Alberta with an international expert in mathematical biology. He joined 91��ɫ’s Department of Mathematics and Statistics in 1990 and became the nation’s youngest Senior Canada Research Chair in 2001. Some of his ongoing research concerns the impact of Lyme disease. He is working to predict the tick-borne infection risk worldwide and looking at how its trajectory is being affected by global warming. In 2017, he was awarded the , and has been leading a large 91��ɫ-Sanofi collaboration to evaluate the cost and benefits of various immunization programs.

His research interest includes big data and informational analytics, and he is the funding co-chair for a major global conference on this topic.

“During peaceful times, it’s my hobby,” he says.

However, these aren’t peaceful times and COVID-19 is currently a priority. Wu’s stellar work serves to demonstrate 91��ɫ’s impact on COVID modelling, which will continue to support efforts for future outbreaks and pandemics, COVID-19 related or otherwise.

“During a pandemic like this, our theories confront reality,” Wu says – and big data analytics is a part of his tool kit.

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