Another Climate-COVID Computer Modelling Similarity

In this post, I wrote about parallels between climate and COVID alarm and related issues of computer modelling.  I realized I left out at least one parallel.

In the world of climate, computer model results are often used as the counterfactual case.  Let me give you an example.  The world has warmed over the last 100 years at the same time atmospheric CO2 concentration has increased.  Obviously, to truly judge the effect of CO2 on temperatures, we would like to know what the temperatures would have been over the last 100 years without rising CO2 concentrations.  But we don't have thermometers that read "with" and "without" CO2.

I remember I got caught up in this years ago when I published an analysis that showed that estimates of temperature sensitivity to CO2 concentrations used in projections going forward greatly over-predicted the amount of warming we have seen already.  In other words, there had not been enough warming historically to justify such high sensitivity numbers.  In response, I was told that alarmists considered the base case without CO2 increases to be a cooling world, because that is what some models showed.  Compared to this cooling counterfactual, they argued that the warming from CO2 historically had been much higher.

By the way, this argument always gets to be very circular.  When you really dig into the assumptions of the counter-factual models, they are based on assumptions that temperature sensitivity to CO2 is high.  Thus models predicated on high sensitivity are used to justify the assumption of high sensitivity.

I thought of all this today when I saw this post on COVID models and interventions from Kevin Drum.  I read Drum because, though I don't love his politics, he is more likely than most team-politics writers from either the Coke or Pepsi party to do a reasonable job of data analysis and interpretation.  But I have to fault him for this post, which I think is just terrible.  You can click through to see the chart but here is the text:

At the end of March, the highest estimate for [NY State] hospitalizations was 136,000+. Today the peak is estimated at about 30,000. That’s a difference of 5x. Did the modelers screw up?

Not really. Remember the Imperial College projections for the United States? They estimated about 2 million deaths if nothing was done; 1 million deaths if some countermeasures were taken; and 200,000 deaths if stringent countermeasures were taken. That’s a range of 10x. If you figure that we’ve taken fairly stringent countermeasures but not the maximum possible, then a reduction of 5x is about what you’d expect. Alternatively, if you ignore the Columbia University projection as an outlier, the IHME estimate has only gone down by about 2x. That’s what you’d expect if we took countermeasures that were just a little more stringent than their model assumed.

At the end of March it was still not clear how stringent and how effective the coronavirus countermeasures would be. In the event, it looks like they worked pretty well, cutting cases by at least 2x and possibly more. This is why the model estimates have gone down: because we followed expert advice and locked ourselves down. Just as we hoped.

Treating the early model estimates as if they are accurate representations of the "no intervention" counter-factual is just absurd.   It is particularly absurd in this case as he actually quotes a model -- the early Imperial College model -- that is demonstrably grossly flawed.  He is positing that we are in the Imperial College  middle intervention case, which estimated a million deaths in the US and is likely to be off by more than an order of magnitude.  Given this clear model/estimate miss, why in the world does he treat early Columbia and McKinsey models as accurate representations of the counter-factual?  Isn't it at least as likely that these models were just as flawed as the Imperial College models (and for many of the same reasons)?

The way he uses the IHME model results is also  flawed.  He acts like the reductions in the IHME estimates are due to countermeasures, but IHME has always assumed full counter-measures so it is impossible to use the numbers the way he wants to use them.