Relevant Metaculus question: Will the US hold mass-turnout elections for President on schedule in 2020?

Thoughts re: my question and the responses:

• The leverage argument for working on COVID-19 rather than existential risk (x-risk) seems weak by itself. My guess is that working on COVID-19 has about 7 orders of magnitude more tractability than x-risk (2 for knowing which risk you're working on, 2 for understanding it, and 3 for less prior effort), but that the scale of x-risk is more than 10 orders of magnitude higher than the direct disease burden of COVID-19.
• I'm unsure if world-ending pandemics look like this one, but it's a good point.
• The civilisational threat point seems pretty legitimate, but I'm unsure how to weigh it.
• The harms done by factory farming of non-human animals seem comparable to the direct disease burden of COVID-19 to me.
• OpenPhil may be funding some forecasting work with the Good Judgement Project, which is what I was referring to, but as far as I'm concerned, Metaculus (which I'm involved in) is doing better forecasting (and might also be funded by OpenPhil?). See this dashboard of predictions.
• This situation seems like it reveals a lot of information about governance, but if all one wants to do is learn from the situation, it seems better to document it a bit now and wait later. However, if one wants to contribute to a probable effort to improve governance of pandemics at the tail end of this outbreak, that would require careful analysis and action now.
• If I mostly wanted to raise the status of the effective altruism movement, I wouldn't push a policy proposal as unpopular as variolation - but it might become more popular as people become better at marketing it?

Overall, I now think that it's worth the full time of a small contingent of effective altruists to focus on policy responses to COVID-19 as well as broadly understanding it, and that we are probably assigning too little focus to this (although within the portfolio of attention, I wish more were directed towards neglected high-leverage interventions). Most of the arguments presented I find convincing, except for the one that I said was convincing during the call.

If they were perfectly calibrated on this one-off prediction, about 14 should've had the actual outcome fall in their 80% confidence interval.

Nope. Suppose I roll a 100-sided die, and all LessWrongers write down their centred 80% credible interval for where the answer should fall. If the LWers are rational and calibrated, that interval should be [10,90]. So the actual outcome will fall in everybody's credible interval or nobody's. The relevant averaging should happen across questions, not across predictors.

Out of 645 tests done in Colorado on first responders and their families, there were zero positive results.

Note: half of carriers don't show symptoms at the time they tested positive, could well be that they show symptoms later.

Yes, [comparing the evidence against the theory to the evidence for it is] what I'm trying to do here.

It looks more like you listed all the evidence you could find for the theory and didn't do anything else.

Although you can have problems of self-selection and bias, when you’ve got big data like this you tend to trust it more.

I don't think this is actually how selection effects work.

You'd expect to see people to many severe cases amongst people who travelled for business a lot in January and February.

Those people are less famous so you wouldn't necessarily hear about them.

I don't quite understand what you're saying here.

That the asymptomatic rate isn't all that high, and in at least one population where everybody could get a test, you don't see a big fraction of the population testing positive.

I'm not just cherry picking the tail-end of a normal distribution of IFRs etc. The Gupta study in particular and some of the other studies suggest a fundamentally different theory of the pandemic.

The point remains: given that some people have such a different theory, it's unclear how many supporting pieces of evidence your should expect to see, and it's important to compare the evidence against the theory to the evidence for it.

The King's Professor seems to find this number convincing.

With all due respect it's not that hard to get data that you yourself find convincing, even if you're a professor.

Tom Hanks, Prince Charles and Boris Johnson don't talk meet more people everyday then your typical Uber driver cashier etc.

They do meet more different populations of people though. So if a small number of cities have relatively widespread infection, people who visit many cities are unusually likely to get infected.

Crucially depends on the asymptomatic rate, which might very well be very high.

Not likely. About 1% of Icelanders without symptoms test positive, and all the stats on which tested people are asymptomatic that I've seen (Iceland, Diamond Princess) give about 1/2 asymptomatic at time of testing (presumably many later get sick).

I'm particularly unimpressed by the dot points noting things that happened to very few people:

There were a few dengue in Australia and Florida where it is unusual...

Difficulties in False Negative Diagnosis of Coronavirus Disease 2019: A Case Report. Note that this was a highly symptomatic person...

One person had persistent negative swab, but tested positive through fecal samples...

“Chinese journalists have uncovered other cases of people testing negative six times before a seventh test confirmed they had the disease.”

This seems pretty hard to evaluate because with a large number of published pre-prints on the outbreak, it's not very surprising that there would be many suggesting higher-than-expected spread. The question is how that weighs up against the opposing evidence, and to evaluate that I'd have to look at all the opposing evidence, which I don't want to do. That being said, broadly I am unconvinced. Notes on some of the dot points:

10% of 650,000 UK users of their C19 symptom tracker app showed mild symptoms. Thus 6.5m people in UK are infected

Presumably some of these people are hypochondriacs or have the flu? Also, I bet people with symptoms are more likely to use the app.

IFR=0.12% (95%CrI: 0.08-0.17%), several orders of magnitude smaller than the crude CFR estimated at 4.19%.

This isn't very important but 0.12 is only 1.5 orders of magnitude smaler than 4.19, which I wouldn't call "several".

High proportion of special populations are infected (celebrities, athletes and politicians).

Couldn't this be explained by those populations travelling more, shaking more hands, meeting more people, etc.?

Widespread testing (which isn’t random) in Iceland suggests an even lower IFR [than 0.3%].

Iceland has 2 deaths and 97 recoveries. I would say that isn't good evidence for an IFR of under 0.3%. Admittedly the number of deaths so far is 0.2% of the total number of cases, but given exponential spread most of the cases will be new and won't have had time to die yet, so the deaths to recoveries ratio seems more important (although upward-biased given who gets tested).