# An econometrician on the SEIRD epidemiological model for Covid-19

There is a new paper by Ivan Korolev:

This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I demonstrate using the data from Iceland that auxiliary information from random tests can be used to calibrate the initial parameters of the model and reduce the range of possible forecasts about the future number of deaths. Finally, I show that the basic reproduction number R0 can be identified from the data, conditional on the clinical parameters. I then estimate it for the US and several other countries, allowing for possible underreporting of the number of cases. The resulting estimates of R0 are heterogeneous across countries: they are 2-3 times higher for Western countries than for Asian countries. I demonstrate that if one fails to take underreporting into account and estimates R0 from the cases data, the resulting estimate of R0 will be biased downward and the model will fail to fit the observed data.

Here is the full paper. And here is Ivan’s brief supplemental note on CFR. (By the way, here is a new and related Anthony Atkeson paper on estimating the fatality rate.)

And here is a further paper on the IMHE model, by statisticians from CTDS, Northwestern University and the University of Texas, excerpt from the opener:

- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)

Again, I am very happy to present counter evidence to these arguments. I readily admit this is outside my area of expertise, but I have read through the paper and it is not much more than a few pages of recording numbers and comparing them to the actual outcomes (you will note the model predicts New York fairly well, and thus the predictions are of a “train wreck” nature).

Let me just repeat the two central findings again:

- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)

So now really is the time to be asking tough questions about epidemiology, and yes, epidemiologists. I would very gladly publish and “signal boost” the best positive response possible.

And just to be clear (again), I fully support current lockdown efforts (best choice until we have more data and also a better theory), I don’t want Fauci to be fired, and I don’t think economists are necessarily better forecasters. I do feel I am not getting straight answers.