The colored points show historical data on R vs. the 6-period average, with color indicating the date.
Thanks for actually plotting historical Rt vs infection rates!
Whereas, it seems more natural to take (3) as evidence that (1) was wrong.
In my own comment, I also identified the control system model of any kind of proportionality of Rt to infections as a problem. Based on my own observations of behaviour and government response, the MNM hypothesis seems more likely (governments hitting the panic button as imminent death approaches, i.e. hospitals begin to be overwhelmed) than a response that ramps up proportionate to recent infections. I think that explains the tight oscillations.
I’d say the dominant contributor to control systems is something like a step function at a particular level near where hospitals are overwhelmed, and individual responses proportionate to exact levels of infection are a lesser part of it.
You could maybe operationalize this by looking at past hospitalization rates, fitting a logistic curve to them at the ‘overwhelmed’ threshold and seeing if that predicts Rt. I think it would do pretty well.
This tight control was a surprise and is hard to reproduce in a model, but if our model doesn’t reproduce it, we will go on being surprised by the same thing that surprised us before.
My own predictions are essentially based on continuing to expect the ‘tight control’ to continue somehow, i.e. flattening out cases or declining a bit at a very high level after a large swing upwards.
It looks like (subsequent couple of days data seem to confirm this), Rt is currently just below 1 in London—which would outright falsify any model that claims Rt never goes below 1 for any amount of infection with the new variant, given our control system response, which according to your graph, the infections exponential model does predict.
If you ran this model on the past, what would it predict? Based on what you’ve said, Rt never goes below one, so there would be a huge first wave with a rapid rise up to partial herd immunity over weeks, based on your diagram. That’s the exact same predictive error that was made last year.
I note—outside view—that this is very similar to the predictive mistake made last Febuary/March with old Covid-19 - many around here were practically certain we were bound for an immediate (in a month or two) enormous herd immunity overshoot.
Based on what you’ve said, Rt never goes below one
You’re saying nostalgebraist says Rt never goes below 1?
I interpreted “R is always ~1 with noise/oscillations” to mean that it could go below 1 temporarily. And that seems consistent with the current London data. No?
I meant, ‘based on what you’ve said about Zvi’s model’ I.e. Nostalgebraist says zvi says Rt never goes below 1 - if you look at the plot he produced Rt is always above 1 given Zvi’s assumptions, which the London data falsified.
Thanks for actually plotting historical Rt vs infection rates!
In my own comment, I also identified the control system model of any kind of proportionality of Rt to infections as a problem. Based on my own observations of behaviour and government response, the MNM hypothesis seems more likely (governments hitting the panic button as imminent death approaches, i.e. hospitals begin to be overwhelmed) than a response that ramps up proportionate to recent infections. I think that explains the tight oscillations.
You could maybe operationalize this by looking at past hospitalization rates, fitting a logistic curve to them at the ‘overwhelmed’ threshold and seeing if that predicts Rt. I think it would do pretty well.
My own predictions are essentially based on continuing to expect the ‘tight control’ to continue somehow, i.e. flattening out cases or declining a bit at a very high level after a large swing upwards.
It looks like (subsequent couple of days data seem to confirm this), Rt is currently just below 1 in London—which would outright falsify any model that claims Rt never goes below 1 for any amount of infection with the new variant, given our control system response, which according to your graph, the infections exponential model does predict.
If you ran this model on the past, what would it predict? Based on what you’ve said, Rt never goes below one, so there would be a huge first wave with a rapid rise up to partial herd immunity over weeks, based on your diagram. That’s the exact same predictive error that was made last year.
You’re saying nostalgebraist says Rt never goes below 1?
I interpreted “R is always ~1 with noise/oscillations” to mean that it could go below 1 temporarily. And that seems consistent with the current London data. No?
I meant, ‘based on what you’ve said about Zvi’s model’ I.e. Nostalgebraist says zvi says Rt never goes below 1 - if you look at the plot he produced Rt is always above 1 given Zvi’s assumptions, which the London data falsified.
Rt can go below one in Zvi’s model. It just takes an even higher rate of new infections.
Here’s the same picture, with the horizontal axis extended so this is visible: https://64.media.tumblr.com/008005269202c21313ef5d5db6a8a4c6/83a097f275903c4c-81/s2048x3072/7b2e6e27f1fb7ad57ac0dcc6bd61fce77a18a2c1.png
Of course, in the real world, Rt dips below one all the time, as you can see in the colored points.
As a dramatic example, Zvi’s model is predicting the future forward from 12/23/20. But a mere week before that date, Rt was below one!