These seem like interesting points, but overall I’m left thinking there is still a significant chance of setting off a long chain that wouldn’t have happened otherwise. (And even a lowish probability of a long chain means the bulk of the damages are on other people rather than your self.)
I think the argument applies to California too. Suppose that 20% have already been infected, and 0.5% are infected currently, and R = 1.
Then in 6 months, an extra 0.5%64 = 12% will have been infected, so 32% will have had it in total. That won’t be enough to create herd immunity & prevent a long chain.
An extra infection now would in expectation cause a chain of 641 = 24 infections, and if a vaccine then came and the disease were stamped out, then those 24 people wouldn’t have had the disease otherwise.
What seems to matter is that we’re in a “slow burn” scenario, where we’re a decently long way from ending it, but R ~ 1, but we’re not sure we’re going to reach herd immunity as the end game.
PS My figure for London was a rough ballpark from memory—your figures are better. (Though like I say I don’t think the argument is very sensitive to whether 10% or 30% have already had it.)
And even a lowish probability of a long chain means the bulk of the damages are on other people rather than your self
Sure, but how large? At an empirical IFR of 0.5%, and expected chain size of 5 (which I think is a bit of an overestimate for most of my friends in Berkeley), you get to 2% fatality rate in expectation (assuming personal risk negligible).
If you assume local IFRs of your child nodes are smaller than global IFR, you can easily cut this again by 2-5x.
This is all empirical questions, before double-counting concerns in moral aggregation.
These seem like interesting points, but overall I’m left thinking there is still a significant chance of setting off a long chain that wouldn’t have happened otherwise. (And even a lowish probability of a long chain means the bulk of the damages are on other people rather than your self.)
I think the argument applies to California too. Suppose that 20% have already been infected, and 0.5% are infected currently, and R = 1.
Then in 6 months, an extra 0.5%64 = 12% will have been infected, so 32% will have had it in total. That won’t be enough to create herd immunity & prevent a long chain.
An extra infection now would in expectation cause a chain of 641 = 24 infections, and if a vaccine then came and the disease were stamped out, then those 24 people wouldn’t have had the disease otherwise.
What seems to matter is that we’re in a “slow burn” scenario, where we’re a decently long way from ending it, but R ~ 1, but we’re not sure we’re going to reach herd immunity as the end game.
PS My figure for London was a rough ballpark from memory—your figures are better. (Though like I say I don’t think the argument is very sensitive to whether 10% or 30% have already had it.)
Sure, but how large? At an empirical IFR of 0.5%, and expected chain size of 5 (which I think is a bit of an overestimate for most of my friends in Berkeley), you get to 2% fatality rate in expectation (assuming personal risk negligible).
If you assume local IFRs of your child nodes are smaller than global IFR, you can easily cut this again by 2-5x.
This is all empirical questions, before double-counting concerns in moral aggregation.