so, i'm not going to spend much time on this, it's the same propaganda we've seen elsewhere, but i just want to make some brief comments about the modelling, which is presented in a little more detail than elsewhere.

first, it should be pointed out that there's a large testing deficit in ontario, and any flattening effects seen on the curve are likely a relic of the fragmented data. if ontario was testing at the rates that quebec is, it would probably have actually found even more cases than quebec, and as ontario is such a large proportion of canada's population, testing lags in ontario will decisively effect the overall numbers. this is data that is known to be suspect, and canada's curve on that graph should be far more linear than is being presented.

second, i want to make a conceptual critique of the modelling in terms of how it understands the way the curves adjust to the different scenarios, or at least suggests that it does via it's use of pictorial aids. maybe i'm just correcting the props; i'd have to look deeper into it to know. the first option, where nothing is done at all, should indeed lead to a ramp up to a 70% infection rate fairly quickly, and then see a rapid drop in cases as the virus is unable to spread further in immune hosts - that's the benefit of herd immunity, and the graphic representation does capture that. however, the pictorial representations of the second and third options, where increasing amount of restrictions are applied, suggest the strict trail off achieved with herd immunity, while attempting to explicitly prevent it. again: i can't be sure what the formulas actually were. but, the pictorial representations of the graph should have had a plateau for the second graph, rather than a peak, and the third option should have had a long and sustained plateau, followed by an eventual trail-off, many months into the future. the reason these plateaus will kick in is that immunity will continue to be rare, and they will essentially stay in place for as long as it takes to actually get to 70%, meaning the number of actual cases is not lower, due to the onset of the sustained plateau.

see, and now we're getting some confusing messaging, then, because the graphical presentation of the data suggests that we can expect this to be over in the same time frame as though we'd done nothing, despite intentionally trying to slow down immunity, thereby stretching out the process.

....except that i actually don't think we're on the third curve. i think we're actually on the first one.

so, this is actually going to be a good signal to determine which model is more reflective of reality. if we see a sustained plateau in the number of deaths, we will know that it's because the effects are slowing the spread - and that it's going to take months to ease up. if we see a continual rise, a peak and sharp drop, we'll know that we had no effect on this thing at all, as it ripped through us at will, and burned itself out.

https://www.canada.ca/content/dam/phac-aspc/documents/services/diseases/2019-novel-coronavirus-infection/using-data-modelling-inform-eng.pdf