The inaccurate science of predicting the likely path of COVID-19 – Canada News

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Data experts warn Canadians who are already on the lookout for not taking Ontario’s dire predictions about COVID-19 deaths to the letter, even as the revelation of austere data coincided with more measures distancing and passionate calls from government and health officials to stay at home.

On Friday, the President and CEO of Public Health Ontario said staying at home could mean the difference between 6,000 deaths on April 30 and 1,600 deaths. The death toll could drop to 200 if further action is taken, said Dr. Peter Donnelly.

Officials also gave a glimpse of what could happen during the epidemic’s duration, which could range from 18 months to two years, but cautioned that these scenarios become less secure as they prepare.

If Ontario had not adopted various interventions, including school closings, up to 100,000 people would die from COVID-19, said Donnelly. But with various public health measures, deaths could be between 3,000 and 15,000, he said.

Pandemic experts say such projections are not really intended to predict the future, but rather to provide a general guide for policy makers and health systems struggling with a growing pandemic.

Ideally, the information should also assure ordinary people that their individual actions can make a difference, said Ashleigh Tuite, professor of epidemiology at the University of Toronto.

“These are very important comments to share not only in government, but with the general public, because everyone is investing so heavily in them,” said Tuite, who created his own projections for the spread of COVID-19.

“The answer may be that it will take longer than we thought. And while this is not the desired answer, it is a possible answer. Communicate that this is going to be really critical, especially if we are looking at longer time horizons. “

Prime Minister Justin Trudeau said on Saturday he saw a series of screenings.

“We know the situation is serious,” said Trudeau. “What is really going on depends on the choices we make every day. We can change the forecast. “

Provincial health officials have urged the public to “crack down” on isolation measures to break the chain of COVID-19 transmission, pointing to the best and worst-case scenarios that they believe are largely dependent on compliance .

The data was quickly followed by news of additional closings, clearly explaining why Ontario suddenly pushed the warning, says Tuite.

“We are in a situation where we need the support of everyone. And so I think that treating people like adults and having these conversations and explaining what we know and what we don’t know – and where we learn and where we potentially failed – I don’t think it’s a bad thing. As a society, we must have this dialogue. “

Various assumptions were used to build the Ontario model, and Donnelly warned that “modeling and projection is a very inexact science.”

“At the very beginning of an epidemic, it is about providing policymakers with an important early guide on what they should do. And that’s what happened in Ontario, “he said.

“Because as soon as the command table saw the figures that suggested there could be an overall mortality between 90 and 100,000, they moved very quickly to close the schools, which was the right thing to do. “

Modeling may be imperfect, but policy makers would essentially operate blind without them, says Dionne Aleman, professor of industrial engineering at the University of Toronto.

She notes that these educated guesses can help answer the big questions facing many hospitals: when will the wave of COVID-19 patients arrive? Do we have enough intensive care beds? How many patients will need ventilators? Are there enough nurses?

Yet a model will never be as good as the data on which it is based, and during a pandemic “it’s essentially impossible to get real data,” says Aleman, whose work has included building a model of simulate a hypothetical pandemic to explore how factors such as transmission rates affect demands for health care.

“The actual data was not really available for H1N1 just 10 years ago and it really isn’t at the moment,” said Aleman, noting many holes in the COVID-19 statistics available for a epidemiological study.

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