rtnew=1.7 is an entirely different case. Suppressing it would require the sort of lockdown that would yield rt=0.6 for the old strain,
Is this valid reasoning? Intuitively I’d expect current measures to be more effective for a more infectious strain, so that it would require a lockdown that would yield something closer to 0.8 for the old strain.
I suspect that some measures will remain close to 100% effective, like not seeing family members and friends. Similarly some things physically can’t become more infective, like being infected by your partner.
In that case I would expect that what currently are very high risk interactions (e.g. partner) and very low risk interactions (e.g. masked, 6ft+ grocery shopping) will still be almost as risky for a more infectious strain.
However if the bulk of infections happen because of moderate risk interactions, then moderate risk interactions becoming high risk interactions would significantly increase overal cases.
I had a long discussion on this very topic, and wanted to share my thoughts somewhere. So why not here.
Disclaimer: I am not an expert on any of this.
The scaling assumption (if the new strain has an R of 1.7 when the old one has an R of 1, then we need countermeasures pulling the old one down to 0.6 to get the new one to 0.6 * 1.7 = 1) is almost certainly too pessimistic an estimate, but I have no clue by how much. A lot of high risk events (going to a concert, partying with 10+ people in a closed room for an entire night, having a multiple hour Christmas dinner with the entire family) will become less than linearly more risky. I interpreted the “70%” (after some initial confusion) to represent an increase in risk per event or unit time of exposure. But if you are sharing the same air with possibly contagious people for a long period of time your risk is all the way on the saturated end of the geometric distribution, and it simply can’t go above 100%. So high risk events will likely stay high risk events.
At the same time, I expect a lot of medium and low risk events to become almost proportionally more risky. This includes events like having one or two people over for dinner while keeping the room properly ventilated, going to supermarkets, going to the office and using public transport. Something that has been bugging me is that the increase in R-value has been deduced from the actual increased rate at which it spreads, so it is simply not possible that every activity has less than 70% (or whatever number you believe in) increased risk, since that is apparently the population average under the UK lockdown level 2 conditions. So some of this nonlinearity has already been factored in, making it very difficult to say what stronger lockdowns would mean.
In conclusion, I think it is possible that even if the new variant is 70% more transmissible that lockdown conditions that would have pushed the old strain down to 0.7 or only 0.8 might be sufficient to contain this new strain, and of course if the new strain is less transmissible than this we have even more leeway. At the same time I have absolutely no clue how to get a reliable estimate of the “old R needed”.
I don’t think it was that easy to get to the saturated end with the old strain. As I remember, the chance of catching COVID from a sick person in your household was only around 20-30%, and at superspreader events it was still just a small minority of total attendees that were infected.
I also thought this, but was told this was not the case (without sources though). If you are right then the scaling assumption is probably close to accurate. I tried briefly looking for more information on this but found it too complicated to judge (for example, papers summarizing contact tracing results in order to determine the relative importance of superspreader events are too complicated for me to undo their selection effects—in particular the ones I saw limited to confirmed cases, or sometimes even confirmed cases with known source).
EDIT: if I check microCOVID for example, they state that the chance of catching it during a 1 hour dinner with another person who has been confirmed to have COVID is probably between 0.2% and 20%, The relevant event risks for group spread (as opposed to personal risk evaluations) are conditional on at least one person present having COVID. So is this interval a small chance or a large chance? I wouldn’t be surprised if ~10% is significantly high that the linearity assumption becomes questionable, and a 1 hour dinner is far from the most risky event people are participating in.
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.
Is this valid reasoning? Intuitively I’d expect current measures to be more effective for a more infectious strain, so that it would require a lockdown that would yield something closer to 0.8 for the old strain.
I suspect that some measures will remain close to 100% effective, like not seeing family members and friends. Similarly some things physically can’t become more infective, like being infected by your partner.
In that case I would expect that what currently are very high risk interactions (e.g. partner) and very low risk interactions (e.g. masked, 6ft+ grocery shopping) will still be almost as risky for a more infectious strain.
However if the bulk of infections happen because of moderate risk interactions, then moderate risk interactions becoming high risk interactions would significantly increase overal cases.
I had a long discussion on this very topic, and wanted to share my thoughts somewhere. So why not here.
Disclaimer: I am not an expert on any of this.
The scaling assumption (if the new strain has an R of 1.7 when the old one has an R of 1, then we need countermeasures pulling the old one down to 0.6 to get the new one to 0.6 * 1.7 = 1) is almost certainly too pessimistic an estimate, but I have no clue by how much. A lot of high risk events (going to a concert, partying with 10+ people in a closed room for an entire night, having a multiple hour Christmas dinner with the entire family) will become less than linearly more risky. I interpreted the “70%” (after some initial confusion) to represent an increase in risk per event or unit time of exposure. But if you are sharing the same air with possibly contagious people for a long period of time your risk is all the way on the saturated end of the geometric distribution, and it simply can’t go above 100%. So high risk events will likely stay high risk events.
At the same time, I expect a lot of medium and low risk events to become almost proportionally more risky. This includes events like having one or two people over for dinner while keeping the room properly ventilated, going to supermarkets, going to the office and using public transport. Something that has been bugging me is that the increase in R-value has been deduced from the actual increased rate at which it spreads, so it is simply not possible that every activity has less than 70% (or whatever number you believe in) increased risk, since that is apparently the population average under the UK lockdown level 2 conditions. So some of this nonlinearity has already been factored in, making it very difficult to say what stronger lockdowns would mean.
In conclusion, I think it is possible that even if the new variant is 70% more transmissible that lockdown conditions that would have pushed the old strain down to 0.7 or only 0.8 might be sufficient to contain this new strain, and of course if the new strain is less transmissible than this we have even more leeway. At the same time I have absolutely no clue how to get a reliable estimate of the “old R needed”.
I don’t think it was that easy to get to the saturated end with the old strain. As I remember, the chance of catching COVID from a sick person in your household was only around 20-30%, and at superspreader events it was still just a small minority of total attendees that were infected.
I also thought this, but was told this was not the case (without sources though). If you are right then the scaling assumption is probably close to accurate. I tried briefly looking for more information on this but found it too complicated to judge (for example, papers summarizing contact tracing results in order to determine the relative importance of superspreader events are too complicated for me to undo their selection effects—in particular the ones I saw limited to confirmed cases, or sometimes even confirmed cases with known source).
EDIT: if I check microCOVID for example, they state that the chance of catching it during a 1 hour dinner with another person who has been confirmed to have COVID is probably between 0.2% and 20%, The relevant event risks for group spread (as opposed to personal risk evaluations) are conditional on at least one person present having COVID. So is this interval a small chance or a large chance? I wouldn’t be surprised if ~10% is significantly high that the linearity assumption becomes questionable, and a 1 hour dinner is far from the most risky event people are participating in.
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.