I really appreciate your attempt to summarize this literature. But it seems you still believe that the Oxford paper provides evidence in favor of very low IFR, when in fact others are claiming that this is merely an assumption of their model, and that this assumption was made not because the authors believe it is plausible but simply for exploratory purposes. If this is correct (I haven’t myself read the paper, so I can only defer to others), then the reputation or expertise of the authors is evidentially irrelevant, and shouldn’t cause you to update in the direction of the very low IFR. (Of course, there may be independent reasons for such an update.)
Thanks Pablo for your comment and helping to clarify this point. I’m sorry if I was being unclear.
I understand what you’re saying. However:
I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this (“Irresponsible”—see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.
I really appreciate your attempt to summarize this literature. But it seems you still believe that the Oxford paper provides evidence in favor of very low IFR, when in fact others are claiming that this is merely an assumption of their model, and that this assumption was made not because the authors believe it is plausible but simply for exploratory purposes. If this is correct (I haven’t myself read the paper, so I can only defer to others), then the reputation or expertise of the authors is evidentially irrelevant, and shouldn’t cause you to update in the direction of the very low IFR. (Of course, there may be independent reasons for such an update.)
Thanks Pablo for your comment and helping to clarify this point. I’m sorry if I was being unclear.
I understand what you’re saying. However:
I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this (“Irresponsible”—see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.