Suppose, as you say, some of this nonlinearity is already factored into the 70% estimate, that would imply that the ‘real’ number is even higher. For some interaction, like having a face to face conversation without any protection, the probability of an infection may have increased by 100% or even more.
I’m also not an expert. Intuitively this seems like a big step with just a handful of mutations.
I agree that this means particular interactions would have a larger risk increase than the 70% cited (again, or whatever average you believe in).
In the 24-minute video in Zvi’s weekly summary Vincent Racaniello makes the same point (along with many other good points), with the important additional fact that he is an expert (as far as I can tell?). The problem is that this leaves us in the market for an alternative explanation of the UK data, both their absolute increase in cases as well as the relative growth of this particular variant as a fraction of all sequenced COVID samples. There are multiple possible but unlikely explanations, such as superspreaders, ‘mild’ superspreaders along with a ‘mild’ increase in infectiousness, or even downright inflated numbers due to mistakes or political motives. To me all of these sound implausible, but if the biological prior on a mutation causing such extreme differences is sufficiently low they might still be likely a postiori explanations.
I commented something similar on Zvi’s summary, but I don’t know how to link to comments on posts. It has a few more links motivating the above.
Suppose, as you say, some of this nonlinearity is already factored into the 70% estimate, that would imply that the ‘real’ number is even higher. For some interaction, like having a face to face conversation without any protection, the probability of an infection may have increased by 100% or even more.
I’m also not an expert. Intuitively this seems like a big step with just a handful of mutations.
I agree that this means particular interactions would have a larger risk increase than the 70% cited (again, or whatever average you believe in).
In the 24-minute video in Zvi’s weekly summary Vincent Racaniello makes the same point (along with many other good points), with the important additional fact that he is an expert (as far as I can tell?). The problem is that this leaves us in the market for an alternative explanation of the UK data, both their absolute increase in cases as well as the relative growth of this particular variant as a fraction of all sequenced COVID samples. There are multiple possible but unlikely explanations, such as superspreaders, ‘mild’ superspreaders along with a ‘mild’ increase in infectiousness, or even downright inflated numbers due to mistakes or political motives. To me all of these sound implausible, but if the biological prior on a mutation causing such extreme differences is sufficiently low they might still be likely a postiori explanations.
I commented something similar on Zvi’s summary, but I don’t know how to link to comments on posts. It has a few more links motivating the above.