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Never before have our lives been so fraught with life-and-death questions. The COVID-19 pandemic has left leaders and听citizens听asking: Will lockdowns work? Can schools be opened? When can we visit our loved ones?听

"The pandemic timeline is going to be a long one," Bauch听says. "You still have the question of how we should deploy the vaccines to get back to normal as fast as possible. Models can be useful to figure out when conditions favour a change from one strategy to another."听

One strategy is to听first听vaccinate people aged 60 and older. The other strategies focus on ways to limit transmission by starting with people aged 20 and younger or by vaccinating everybody irrespective of their age. The fourth model initially targets听 the sector of the population responsible for the most contacts. "Many people's first instinct might be to say, 'Give it to the vulnerable groups first because they're the ones who need the protection,'" Bauch says. 鈥淏ut it may actually be possible to prevent more deaths through interrupting transmission, by prioritizing people who are more likely to spread COVID-19.听听听

鈥淭here's a trade-off between the strategies, and models are useful in situations like this where we want to understand when it makes more sense to vaccinate the elderly or the younger age groups that spread the virus," Bauch adds.听

The models can be populated with information from any province or country to decide on a vaccination strategy to prevent the most COVID-19 deaths in their population. It's a difficult question that jurisdictions around the world will likely need to tackle in 2021.听听

Math models that embrace human behaviour听

Bauch is no stranger to studying infectious disease. Over the years his research has developed models for measles, pertussis, chickenpox, rubella, pandemic influenza, smallpox, HIV, hepatitis A and human papillomavirus. But the rise of the novel听coronavirus presented the kind of unique challenges that Bauch's work is well-suited to investigating.听听

Bauch, who was involved with modelling the spread of COVID-19 in 2020, says his research isn't just about droplets and case numbers. His models include policy and the choices people make. "Human behaviour is hugely important,鈥澨齢e听says. 鈥淢any models assume that it's fixed but what we have seen in this pandemic is that human behaviour can change dramatically and we can't really understand the course of the pandemic without it."听听

For example,听Bauch鈥檚听research team used Google mobility data on how much time people spend at work or in retail and recreational destinations. This helped听create models of the effectiveness of stay-at-home messaging and lockdown orders, and how populations respond to outbreaks.听

Mathematicians recognized the risk of COVID-19 early听

The mathematical biologist recognized early on that COVID-19 had the potential to be the next global threat. In January 2020, he watched headlines about the spread of the disease in China and realized it was not a question of if but when it would cross borders. "It's a bit like trying to start a fire with flint and steel,鈥� Bauch says. 鈥淵ou know, you strike the rock, and at first, the sparks don't do anything, but eventually a spark lands听in听the right place and there's an outbreak. And that's the stage that we were at in January and February听(2020)听here in Ontario."听

Bauch says mathematical models are particularly helpful with unprecedented crises听such as听the COVID-19 pandemic: "When you're in an unprecedented situation like a pandemic, where we have no experience to build on, you have to use mathematical models to try to get your head around the possible scenarios that could play out and what it might look like," Bauch听explains. "Models are even more important for a pandemic than for existing infectious diseases because we had no history of COVID-19 in the population to learn from."听

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