Multiplying all this together gets you to a 1 in 80 million chance of all this stuff happening under the null hypothesis, which is highly significant.
Not until you work out the chance of all this stuff happening under alternate hypotheses, and the prior probabilities of alternate hypotheses, and the prior probability of the null hypothesis. (I asked random.org for 10 random bytes and I got 02 c8 c2 30 60 b3 2e 93 a6 e9 . The chance of this happening under the null hypothesis is 1 in 1.2×10^24
Under the alternate hypothesis, location, warnings, timing and specific features are all much more likely. It’s probably something like 0.6^4 ~= 0.12
Priors for a lab leak vs a natural spillover are a bit harder, but we have examples of lab leak such as foot and mouth disease in the UK, other leaks of covid, etc. I think a reasonable prior for a lab leak is between 1% and 30% but priors are of course not something we can expect everyone to agree on.
You really think in 60% of cases where country A lifts a ban on funding gain of function research a pandemic starts in country B within 2 years? Same question for “warning published in Nature”.
It has to be conditional on a massive global pandemic starting in that country at all, to make a fair comparison with the 2⁄80 calculation under the null hypothesis.
But say we break it down into two parts. (1) probability that the GoF research does have the potential to cause a pandemic and (2) distribution in time of the pandemic after research starts.
If your theory is: there is a lab leak from WIV while working on defuse derived work then I’ll buy that you can assign a high probability to time & place … but your prior will be waaaaaay below the prior on “lab leak, nonspecific” (which is how I was originally reading your piece).
Under the null hypothesis we assign equal probability to each year between 1980 and 2060, and they add up to 1. So there is an assumption there that a pandemic will definitely occur starting in china.
We should make the same assumption under the alternate hypothesis. The only difference is under AH there’s a lab leak. So we just adjust the way the probability is allocated by year. It still has to add up to 100%.
So, maybe we’ll have a uniform background of 0.1% per year between 1980 and 2060, and then after the 2011 events where people started talking about GoF it increases a bit as GoF is at least possible, then it increases again in 2017 when GoF is funded and greenlit, and after that each year it decreases a little bit, think of it as a hazard rate, once it has happened once people will start being cautious again.
Another comment on timing updates: if you’re making a timing update for zoonosis vs DEFUSE, and you’re considering a long timing window w_z for zoonosis, then your prior for a DEFUSE leak needs to be adjusted for the short window w_d in which this work could conceivably cause a leak, so you end up with something like p(defuse_pandemic)/p(zoo_pandemic)= rr_d w_d/w_z, where rr_d is the riskiness of DEFUSE vs zoonosis per unit time. Then you make the “timing update” p(now |defuse_pandemic)/p(now |zoo_pandemic) = w_z/w_d and you’re just left with rr_d.
Not until you work out the chance of all this stuff happening under alternate hypotheses, and the prior probabilities of alternate hypotheses, and the prior probability of the null hypothesis.
(I asked random.org for 10 random bytes and I got 02 c8 c2 30 60 b3 2e 93 a6 e9 . The chance of this happening under the null hypothesis is 1 in 1.2×10^24
Under the alternate hypothesis, location, warnings, timing and specific features are all much more likely. It’s probably something like 0.6^4 ~= 0.12
Priors for a lab leak vs a natural spillover are a bit harder, but we have examples of lab leak such as foot and mouth disease in the UK, other leaks of covid, etc. I think a reasonable prior for a lab leak is between 1% and 30% but priors are of course not something we can expect everyone to agree on.
You really think in 60% of cases where country A lifts a ban on funding gain of function research a pandemic starts in country B within 2 years? Same question for “warning published in Nature”.
It has to be conditional on a massive global pandemic starting in that country at all, to make a fair comparison with the 2⁄80 calculation under the null hypothesis.
But say we break it down into two parts. (1) probability that the GoF research does have the potential to cause a pandemic and (2) distribution in time of the pandemic after research starts.
If your theory is: there is a lab leak from WIV while working on defuse derived work then I’ll buy that you can assign a high probability to time & place … but your prior will be waaaaaay below the prior on “lab leak, nonspecific” (which is how I was originally reading your piece).
But we are updating on the timing.
Under the null hypothesis we assign equal probability to each year between 1980 and 2060, and they add up to 1. So there is an assumption there that a pandemic will definitely occur starting in china.
We should make the same assumption under the alternate hypothesis. The only difference is under AH there’s a lab leak. So we just adjust the way the probability is allocated by year. It still has to add up to 100%.
So, maybe we’ll have a uniform background of 0.1% per year between 1980 and 2060, and then after the 2011 events where people started talking about GoF it increases a bit as GoF is at least possible, then it increases again in 2017 when GoF is funded and greenlit, and after that each year it decreases a little bit, think of it as a hazard rate, once it has happened once people will start being cautious again.
Another comment on timing updates: if you’re making a timing update for zoonosis vs DEFUSE, and you’re considering a long timing window w_z for zoonosis, then your prior for a DEFUSE leak needs to be adjusted for the short window w_d in which this work could conceivably cause a leak, so you end up with something like p(defuse_pandemic)/p(zoo_pandemic)= rr_d w_d/w_z, where rr_d is the riskiness of DEFUSE vs zoonosis per unit time. Then you make the “timing update” p(now |defuse_pandemic)/p(now |zoo_pandemic) = w_z/w_d and you’re just left with rr_d.
It’s not specifically DEFUSE, it’s DEFUSE and all possible related dangerous GoF work which became possible post 2017
Sorry, I edited (was hoping to get in before you read it)
It doesn’t specifically have to be DEFUSE, it just has to be some work which started after the following key events:
circa 2011: technology becomes available for dangerous GoF and people start discussing it
circa 2018: ban on GoF is lifted
e.g. this bet
https://longbets.org/9/
also contributes to the idea that the prior cannot be that small since Rees bet on it in 2017.