The highly transmissible Omicron variant is forcing mathematicians to rework the models that have helped shape Canada’s understanding of COVID-19, as well as the country’s response to the pandemic.
Everything from who gets tested to who is most likely to get the virus has changed with the latest wave of the pandemic, and that poses distinct challenges for those modeling its impact, says Caroline Colijn, an associate professor of mathematics at the University Simon Fraser in Great Britain. Columbia.
In particular, Colijn said it will be difficult to understand the seriousness of the disease as it spreads among a largely vaccinated public.
“We are still adjusting to flying blind in terms of reported cases,” he said in an interview. “Hospitalizations are lagging and there’s not always good data on them, and (hospitalization numbers) won’t tell you as directly about infections as reported cases will.”
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Better hospitalization data could help, such as daily admission numbers for COVID-19 patients, as well as statistics on those who were hospitalized for other reasons but tested positive for COVID-19 while in care, but it’s complicated, he said.
For example, if hospitalizations are low, like in Newfoundland and Labrador, that kind of information could be a breach of privacy. “It’s a challenge,” Colijn said.
As the Omicron variant fueled weeks of unprecedented case counts across the country, provincial governments stopped testing for every possible COVID-19 case – the demand for testing and tracing was overwhelming and it was impossible to keep up. . Instead, provinces like British Columbia, Ontario, and Newfoundland and Labrador are now only testing cases among those who are at higher risk of infection and hospitalization, such as people in long-term care homes.
That means many cases will be missed, while daily case counts and test positivity rates — the percentage of tests that come back positive — don’t reflect what’s happening in the general population.
The definition of a positive case has really changed, says Jane Heffernan, an associate professor of mathematics at the University of York. “So the models have to change to accommodate that,” he said in an interview, adding, “In math, in order to measure something, you first have to define what you’re measuring.”
Model revisions are expected in any flu season, but there are many other complications with Omicron, Heffernan added, such as how it infects vaccinated and unvaccinated people, the latter with all possible dose combinations.
With the other variants, Heffernan said he could go back to his models and change some variables. Not so with Omicron.
“Since we’re trying to track mild, moderate and severe infections, we can’t just tweak one parameter because having two doses of vaccine versus one dose versus different ages and when different ages had their different rollouts and their boosters, all of that affects the structure of the model,” he said.
In short: “Undoubtedly, Omicron has made life very difficult for us.”
For Amy Hurford, a math professor at Memorial University of Newfoundland and Labrador, the hardest part of modeling Omicron has been its transmission speed.
Their doubling times — the time it takes for the number of infected people to double — are among “some of the fastest we’ve seen in the pandemic,” he said. The rapid spread has meant that governments needed answers about what was happening before scientists could see the variant unfold.
“We have been trying to answer questions with a lot of uncertainty,” he said.
Mathematicians have been much more involved with COVID-19 than with the previous SARS and H1N1 swine flu pandemics, Heffernan said.
“Before the pandemic really started in Canada, there were already models involved,” he said. “Some modellers were seconded by their provincial governments to work on it.”
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She, Colijn and Hurdford hope that the increased focus on math and how it can contribute to pandemic responses and even public health will help change the way people view the field and even encourage more people to enter it.
“I hope this has helped people see math as not just something abstract you learned in high school and then never looked at again, or even something you hated in school,” Colijn said. “(Mathematical modeling) is one of the only tools we have to think at the level of the entire population.”
This report from The Canadian Press was first published on January 13, 2022.
© 2022 The Canadian Press
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