[BostonCard]

Anyway, none of that matters because the covid-19 models here have zero predictive value even if the model calculations were all done correctly.

I disagree here.  All models have associated uncertainties.  I think it is a fair critique that too much emphasis is placed on the prediction, and not enough on the uncertainty associated with it.  However, to say that because the prediction has uncertainty that it has zero predictive value is bad.  Like, I can tell you with 99+% confidence that the cumulative case count tomorrow will be higher than it is today, and that is a predictive statement based on an ultra-crude model.  I can also say with very high confidence that the number of cases in the US tomorrow will be higher than today.  The longer the time horizon, of course, the less precise the estimate is.  But there is a brobdingnagian chasm between "won't give us exact numbers" and "zero predictive value".

I also disagree with the implication that because the models aren't perfectly predictive they are useless.  You could look at the data right now and say, listen, the US has only 3,500 deaths due to coronavirus compared to 10,000 deaths a year due to influenza.  Therefore why are we freaking out about coronavirus?  The answer doesn't lie in the current death count, but in the potential future one, which is model-derived.  Apparently, what got the President to abandon the mid-April target of lifting the social distancing restrictions was being shown models that millions of Americans could die if we didn't social distance.  That's important, even if the model winds up being off by even an order of magnitude.

To say, we "simply don't know" so why bother is defeatist.

Play around with the epidemic calculator: http://gabgoh.github.io/COVID/index.html, which is based on an SEIR model. You will see how sensitive the predictions are to the parameters used (particularly Rt), which is itself very useful.  Because whether the model predicts 200,000 deaths and reality produces 180,000 is less important than the fact that even a small change in Rt can produce a brobdingnagian difference in the number of deaths.

BC

[BostonCard]

Anyway, none of that matters because the covid-19 models here have zero predictive value even if the model calculations were all done correctly.

I disagree here.  All models have associated uncertainties.  I think it is a fair critique that too much emphasis is placed on the prediction, and not enough on the uncertainty associated with it.  However, to say that because the prediction has uncertainty that it has zero predictive value is bad.  Like, I can tell you with 99+% confidence that the cumulative case count tomorrow will be higher than it is today, and that is a predictive statement based on an ultra-crude model.  I can also say with very high confidence that the number of cases in the US tomorrow will be higher than today.  The longer the time horizon, of course, the less precise the estimate is.  But there is a brobdingnagian chasm between "won't give us exact numbers" and "zero predictive value".

I also disagree with the implication that because the models aren't perfectly predictive they are useless.  You could look at the data right now and say, listen, the US has only 3,500 deaths due to coronavirus compared to 10,000 deaths a year due to influenza.  Therefore why are we freaking out about coronavirus?  The answer doesn't lie in the current death count, but in the potential future one, which is model-derived.  Apparently, what got the President to abandon the mid-April target of lifting the social distancing restrictions was being shown models that millions of Americans could die if we didn't social distance.  That's important, even if the model winds up being off by even an order of magnitude.

To say, we "simply don't know" so why bother is defeatist.

Play around with the epidemic calculator: http://gabgoh.github.io/COVID/index.html, which is based on an SEIR model. You will see how sensitive the predictions are to the parameters used (particularly Rt), which is itself very useful.  Because whether the model predicts 200,000 deaths and reality produces 180,000 is less important than the fact that even a small change in Rt can produce a brobdingnagian difference in the number of deaths.

BC