I replicated my estimation of population-average IFR for Africa-ex-South-Africa (henceforth "Africa"), using the same methodology as my India calculations. Africa is significantly demographically younger than either India or the US -- the top quintile of age in Africa starts at 39, India at 49, and the US at 61.

I estimate that the age effect creates an Africa population-average IFR 20% that of the US rate (i.e., a US rate 4.94× greater), assuming age-uniform infection rates and no difference in medical care. This effect is driven by the reduced population share of age>70 in Africa (just 18% that of the US).

My work is here, and here's the primary chart:

In my India analysis, I wrote:

The effect of medical care differences on IFR-by-age curves is of first-order importance to this analysis; as an example, if lack of care were

Abstract:

India's demographic average age is younger than that of the US. This implies that the strongly age-varying Covid-19 infection fatality rate (IFR) could cause a lower population-average IFR in India than in an older nation such as the US, all other factors being equal (spoiler: they're not).

I estimate that the age effect creates an India population-average IFR 39% that of the US rate (i.e., a US rate \(2.58\times\) greater), assuming age-uniform infection rates and no difference in medical care. This effect is driven by the reduced population share of age>70 in India (just 35% that of the US).

I do not attempt to model age-varying infection rates (which I expect would slightly decrease India fatality rates relative to the US), do not attempt to model selection pressure on patients' immune systems (which I expect would make India fatality rates modestly lower), and do not model environmental

I wrote in January about vaccines and public health, and I wanted to retract my bottom-line recommendation about which vaccine to get -- if you have a choice -- in Hong Kong. Appointments opened to residents 16+ yesterday, so this post is coming a bit late, but oh well. Here we are.

If you're in Hong Kong and have choices, my personal recommandation is that you get an appointment for the BioNTech (Pfizer) vaccine as soon as possible. (If you are in Hong Kong and have a HKID, the link to book a vaccine in English is here -- click the red "Book Vaccination" box at the left.)

In the rest of this post, I'll describe how my thinking has changed on the argument I expressed in my January post.

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When I wrote in January, I was looking at a massive shortfall in vaccine demand in the US and assuming that it couldn't happen here in Hong Kong. In hindsight, I was extremely wrong.

In the first

Zvi Mowshowitz's new policy is not to link to the New York Times, and he's willing to entertain the policy of not linking to NYT reporters' Twitters (though hasn't pulled the trigger yet). I understand where he's coming from -- Cade Metz's piece on Scott Alexander was really, really not good.

Scott Aaronson has a numbered list of 14 theses issues and won't talk with Cade Metz, even to explain quantum complexity, without a full explanation on how the piece on Slate Star Codex happened. Also understandable; the article really was quite bad.

Then there's social pressure going around not to read the Times. I think this is a mistake. It is important to understand what rhetoric the paper chooses to use, for the same reason that it's important to occasionally look at what's happening on the other side of a chessboard. I wouldn't claim it's in the top-5 most important things to read to understand the world (or even the top 10), but I believe it's part of

In Zvi's 2/11 Covid update, he turned to Metaculus for help. He looked at the numbers. Becase the man is an inveterate trader, he saw odds that were Wrong On The Internet and just couldn't stop himself from creating an account to bet against it. And then he saw the payout structure and decided he was done after making a single prediction.

I spent some time with the Metaculus site and figured out how they borked this one up enough to drive away Zvi Mowshowitz. I'll try to explain it here.

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Here's a presumably current description of the scoring function that I found on the FAQ, slightly abridged:

Your score \(S(T;f)\) at any given time \(T\) is the sum of an "absolute" component and a "relative" component: \[S(T;f)=a(N)\times L(p;f)+b(N)\times B(p;f),\] where \(N\) is the number of predictors on the question.

If we define \(f=1\) for a positive resolution of the question and \(f=0\) for a negative resolution, then \(L(p;f)=\log_2(p/0.5)\) for \(f=1\) and \(L(p;f)=\log_2((1−p)/0.5)\) for

Another may be sick too, and sick to death, and this affliction may lie in his bowels, as gold in a mine, and be of no use to him;
but this bell that tells me of his affliction, digs out, and applies that gold to me: if by this consideration of another's danger, I take mine own into contemplation, and so secure myself...

I was talking with a friend the other day, and the topic turned to vaccines. It's expected that the Sinovac and Pfizer vaccines will become available roughly simultaneously in Hong Kong, and so the question was, which vaccine we'd would prefer to receive.

Two topics that came up were safety and efficacy...

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On safety, one can ask whether the Sinovac vaccine should be trusted quite as much as the ones developed in the West. (Hey, one can ask just about anything...)

Well, medically speaking, CoronaVac is a relatively conventional killed-virus vaccine. There's significant trial data available on it, as it has been given to more than

Weird year, right? Still, some things don't change -- I remain committed to using at least 10% of what income I earn to make the universe better.

Here's how I'm thinking about doing that at the end of 2020.

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Back in February 2019, I was randomly (yes, randomly) selected to direct $500k of funds from a donor lottery, and have spent a fair amount of time since then thinking about how to direct those funds most efficiently.

So far, I have recommended grants of $165k to the Good Food Institute in the spring, and just over $200k to a number of Covid-19 interventions in May and June. You can read about those grants here and here.

The work that I did to research and decide on those grants was most of the thinking about effective giving that I did this year; it's been written up elsewhere, and so this post will mostly not cover the same ground. (If you're just looking for new ideas from these posts, see