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The range of scenarios seems dizzying, the predictions seriously extreme. Already, critics are insisting the models bias high, leading many to wonder how much we should trust the models upon which so many life-upending decisions are being made.

It’s not possible to be exact about where we will end up … it’s difficult to know exactly where you stand… projections and modelling for a brand new viral disease are very inexact… this is not an exact science.

Donnelly used the word exact, or variations of it, six times throughout the briefing. Yet the imperfect science is informing Ontario’s strategy. And it underlines how assumptions used in modelling the pandemic may rest on “very flimsy foundations,” as Robert Dingwall, a professor of sociology at Britain’s Nottingham Trent University said this week in response to a study questioning the benefits of school closures in terms of scientific evidence.

The Ontario models made projections in terms of mortality based on the global experience thus far of COVID-19, as well as data gathered from Canada. The tables released Friday pegged the case fatality ratio (the percentage of confirmed infections that end in death) at 2.1 per cent overall for Ontario, from 0 per cent for people aged less than 40, to a truly scary 15.9 per cent for people 80 years and over. However, the case fatality ratio is based on known infections, and biases in both directions can inflate or underestimate it — notably, not counting mild cases can produce a falsely high one.