Curve flattening hard to predict right now, math modellers say
Lack of testing and virus’s incubation period contribute to uncertainty
When Caroline Colijn sees the daily numbers of new cases of COVID-19 in Canada, she looks for certain things.
As a disease modelling expert, she says the effects of the measures we’re taking today to limit the spread of the novel coronavirus may not appear for weeks.
But when they do, if effective, they should begin to appear as declines in the new number of cases relative to the days before and, ultimately, no new cases.
“As long as the testing remains consistent, even seeing lower rises or no rises or declines — all of those would be fine,” said Colijn, who is the Canada 150 research chair in mathematics for evolution, infection and public health at B.C.’s Simon Fraser University.
The decline in new cases is crucial to “flattening the curve,” once considered math jargon that has become part of everyday language as the public educates itself about the pandemic.
When health experts talk about the curve as it relates to COVID-19, they are referring to the number of active cases over time. In a pandemic, the goal is to ensure the peak of the curve falls below the health-care system’s capacity to deal with it.
But the challenge for both mathematicians and the federal government is a lack of data in some cases — and what Colijn called “noisy” data skewed by many factors in others — making it hard to predict the course of the disease in Canada.
On Monday, Health Minister Patty Hajdu said the federal government continues to ask the provinces to release more precise data, and has offered help to do so if their staff levels are stretched. Deputy Prime Minister Chrystia Freeland said Prime Minister Justin Trudeau was going to raise the issues during a call with the premiers Monday evening.
Ashleigh Tuite, associate professor of epidemiology at the University of Toronto, said graphs circulating that show the potential path of COVID-19 in Canada come with “huge amounts of uncertainty” for several reasons.
First, we’re still learning about disease transmission and behaviour. Second, confirmed cases reflect the recent past, not the present, because of the virus’s incubation period. And third, testing protocols have shifted and many people who have symptoms consistent with COVID-19 will never be tested, meaning modellers don’t have accurate numbers to work with.
“Unfortunately, the reality is that the data that we have to basically get a sense of what’s happening in terms of disease transmission is quite messy.”
“So those clean lines you see in the graphs are not really reflective of what we’re going to see in reality.”
In reality, we’re somewhere in the middle, where measures like social distancing and selfisolation will be key to flattening the curve, she said.