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Some very simple models of covid case trajectories in Toronto based on decomposition of old variants and novel variants (b117 right now).
The dashed lines are forward projections. Solid lines are best guesstimates of fraction old (ov) and new (nv) variants based on lab data.
R for b117 seems around 40% higher than old variants, and that ratio was stable through January. So you can run this model forward assuming that ratio remains constant, and assigning an R to old variants (with nv 40% higher).
Our estimate for R right now in Ontario is 0.8. Similar inside and outside Toronto. You can see (top left) that we’d have to be a bit better than this (ov R 0.7) for novel variants not to take off.
At our current level of control (stay at home order, closed schools) the novel variants do increase, but the take off is slow. If old variant R increases to 0.9, that corresponds to a new variant R of 1.26, and we should see a fairly brisk spring wave.
Bottom right is the original logistic curve based on decomposition of some lab data (blue), as well as curves from these simple models. Under any of these scenarios we do see novel variants predominating over the next 4-6 weeks (as they have elsewhere; we are not different).
Tldr here:
1. Novel variants aren’t the apocalypse, but they give us less margin for error, especially as ICUs are already pretty full.
2. Public reporting of NV and OV fractions (even just % SGTF over time) would be really helpful. We can decompose these epidemic curves
1. Novel variants aren’t the apocalypse, but they give us less margin for error, especially as ICUs are already pretty full.
2. Public reporting of NV and OV fractions (even just % SGTF over time) would be really helpful. We can decompose these epidemic curves
I think that will be helpful to decision makers, as these really are distinct processes with different trajectories.
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Coi statement: I didn’t watch the Super Bowl, but if you’re talking greatest athlete of all time and not mentioning Serena williams, you need to broaden your interests.
