Posts written by Thomas Lumley (2534)

Remember the bin lid?

Case C tested positive in MIQ at their day-12 test. They had a room next door to D and D’s child, E. D is the person who was initially thought to have been infected by touching a bin lid that C had also touched some 20 hours before.

There’s now a published paper by Jemma Geohegan and numerous co-workers, and Siouxsie Wiles has an explainer at the Spinoff:

Now we know that SARS-CoV-2 is airborne, a much more likely scenario is that case D was infected by exposure to airborne virus shed by case C. 

CCTV footage showed that C and D were never outside their MIQ rooms at the same time. But they both got their day 12 swabs taken on the same day. And these were taken from their doorways. C was tested first. Then there was a 50-second window between them closing their door and D opening theirs to be swabbed. It looks like having the hotel room doors open for as long as it took to be swabbed was enough to move airborne virus from C and their room into the enclosed and unventilated corridor and then on to D and into their room. From the genome sequencing, it looks like D then infected their child, E, and another household member, F. 

This might seem strange: we know there was a contaminated bin lid, and the opportunity for airborne^* transmission was pretty marginal, so why are we concluding it was airborne^*?

Back last year, scientists and public health people worked on the assumption that the Covid virus could spread via contaminated surfaces, because lots of microbes do and it’s a relatively easy fix.  Some of us went as far as washing individual tomatoes when we got them back from the supermarket.  It became clear fairly quickly that airborne^*  transmission was important; there were some smoking-gun examples that couldn’t be explained any other way.  It took a lot longer to decide that contaminated surfaces were pretty much never important, because most cases that could have been surface transmission could also have been airborne^* transmission. Still, there were no smoking-gun cases of transmission by contaminated surfaces, and you’d expect a few unless it was very rare.

As the numbers of active infections rose, it became harder to track down causes for any individual case.  Except, in New Zealand, it didn’t.  Since the end of the first outbreak we have had a very small number of cases, small enough that contact tracing and genome sequencing is possible for every person involved, plus the sort of hotel surveillance that’s unusual in the public sector outside prisons.  Here, it is possible to track most individual transmissions. So, the ‘bin lid’ case was interesting because it looked like an unambiguous case of transmission from a contaminated surface.

What we know now is that it wasn’t. That is, it wasn’t an unambiguous case of transmission from a contaminated surface; it could have been airborne^*  transmission.  The ‘bin lid’ case

• definitely isn’t the smoking gun for surface transmission that it first appeared
• and so, based on everything else we know, probably was airborne^*  transmission

It’s not that we have strong evidence for airborne^*  transmission from the facts of this specific case, but we don’t have strong evidence against it, and other evidence definitely points that way.

* for some definition of the word, let’s not be dicks about it.

Q: Did you see 90% of Covid deaths are due to obesity?

A: No

Q: It’s on Newshub: “90 percent or 2.2 million of the 2.5 million deaths from the pandemic disease so far were in countries with high levels of obesity.”

A: So, if that’s true, what countries are we talking about?

Q: With high levels of obesity? Australia, New Zealand, some of the Pacific Island nations, USA, UK,…

A: And what don’t those countries have in common?

Q: Lots of Covid deaths, I suppose.  But Australia and NZ and the Pacific Island countries didn’t have many deaths just because they didn’t have many cases. They don’t tell you anything about the risk

A: Neither does the ‘report’, which says, according to Newshub, “the majority of global COVID-19 deaths have been in countries where many people are obese, with coronavirus fatality rates 10 times higher in nations where at least 50 percent of adults are overweight”

Q: That sounds bad, though? Ten times higher is a lot!

A: How much do you think being overweight increases the risk of death?

Q: Well, if 50% overweight means ten times the risk, maybe a factor of twenty?

A: Yeah nah. Less than double, according to that very same report. (PDF, p13)

Q: So where do they get such a strong association?

A: Well, what are the main risk factors for dying of Covid?

Q: Um. Age?

A: And?

Q: Heart disease? Diabetes?

A:

Q:

A:

Q: Having Covid?

A: Exactly.  So we’d need to look at the proportion of infected people dying (the case fatality rate), not the total number of deaths, and we’d ideally want to compare countries with similar age profiles. Instead, Newshub shows us a graph of deaths, basically like this

Q: Why are some of the dots black?

A: They are countries that were missing from the Johns Hopkins Covid data, and so were missing from the original graph, mostly small nations like Samoa and Tonga and Timor-Leste with very few cases

Q: And what’s the dot on the left that isn’t in the graph on Newshub?

A: Afghanistan. I don’t know why it’s not there.

Q: So what about for risk of dying if you get Covid?

A: Here’s what it looks like for case fatality rate

Q: That’s…surprisingly unimpressive. Well, except for that one country…

A: Yemen

Q: Things they don’t need right now, number #1

A:  Too true.

Q: So the massive correlation?

A: Well, overweight is common in the USA, the UK, Brazil, and various other large countries that have handled the pandemic badly. While I suppose you could argue that handling the pandemic well should go with handling other public health issues well, the empirical evidence would not really be in your favour on that one.  You could also listen to a slightly different approach to the data from David Spiegelhalter and Tim Harford on BBC’s “More or Less”

• I wrote about how many people we need to vaccinate and why it’s complicated, at the Spinoff
• And an expanded version of this post about the Pfizer vaccine and obesity, for ‘Statisticians React to the News’, a blog of the International Statistical Institute
• While it’s true that basically no deaths and hospitalisations have been seen in the vaccinated groups in the vaccine trials, there haven’t been very many seen in the control groups either. As Hilda Bastian explains in the Atlantic, the trials were designed to see differences in symptomatic Covid, not serious/fatal Covid, and there are uncertainties about differences between the vaccines and about whether they are extremely effective or just very  effective. “It’s tempting to believe that a simple, decisive message—even one that verges on hype—is what’s most needed at this crucial moment. But if the message could be wrong, that has consequences.”
• The RECOVERY trial in the UK keeps churning out information about effectiveness of treatments for Covid based on large numbers of hospitalised patients. A new casualty: colchicine, a gout drug that appears beneficial in non-hospitalised patients in a smaller Canadian study.  RECOVERY is now expanding internationally, starting with Nepal and Indonesia
• “But there’s something else interesting here. The fear-and-stress argument is introduced with an historical account about a medieval experiment conducted by medieval Persian philosopher Avicenna / Ibn Sīnā. The story is *total bullshit.* Avicenna did no such experiment.”   from ‘Calling Bullshit’
• In one of Terry Pratchett’s Discworld books, there’s a personal organiser that does handwriting recognition. You show it a page of text, and it says “Yes, that’s handwiting, I’d recognise it anywhere”.  Something a bit similar is suggested by Police Commissioner Andrew Coster’s claim “There is no use of police photos for facial recognition unless it is someone who is an unidentified suspect for an offence.”. If that were true, the facial recognition system would be able to examine a photo and say “Yeah nah, don’t recognise him but he looks like a suspect “ but nothing more useful.  A facial recognition system needs basic training on faces (which is often problematic), a set of faces with known names (which is often problematic) , and a set of faces to recognise (which… well you get the idea). It matches the third to the second, using clues from the first about general face patterns.  With just the third group of photos, nothing much will happen.

Q: Is four a lot?

A: Depends on the context. Lockdowns, yes. Incursions per 46000 people through MIQ, no.

Q: We’ve had four lockdowns in Auckland, right?

A: Yes

Q: And how many cases let through MIQ for each 46000 people?

A: About four

Q:

A:

Q: ಠ_ಠ

A: My point, and I do have one, is that more people need to say what their criteria are. If you think four lockdowns is unacceptable, what’s your target that you can argue a realistic system would achieve? If you think 10 cases in 115000 MIQ visitors is a good result, what would it take to be a bad result?

Q: So what’s your threshold?

A: I don’t know. That’s why I haven’t been making comments about the overall performance being good or bad.

• About 140% of Kiwis aged 20-24 are on Facebook (NZ$ Herald) — more precisely, that’s the ratio of Facebook’s claimed “reach” to StatsNZ’s population estimates.  This is an age group where the census does relatively badly, but nowhere near that badly.
• The false-positive rate of the PCR tests for Covid has become topical again, so it’s worth revisiting how we know it is very low. In addition to knowing it should be low based on how the test works, we have simple, direct evidence that the false positive rate is low for the test as used in NZ. We have so far performed 1,720,909 tests in New Zealand and 1,717,865 of them, or 99.8%, have been negative. The 0.2% left over include all the true positives (including people with serious Covid cases), all the inconclusive results, and any false positives.
• Business$ Week reports on the countries of origin of Covid cases in MIQ.  As you’d expect, the top four are four of the top five in the world for Covid cases: US, India, UK, Russia. Brazil, the other country in the top five, doesn’t show up, but we don’t get as many immigrants or visitors from South America.  Also, as you’d expect, China is very low on the list, because we didn’t start the MIQ system until after they’d controlled their big outbreak. Image (from tweet by @victoriayoung03)
  

According to the Guardian (and various other sources), the Pfizer Covid vaccine “may be” less effective in people with obesity, based on an Italian study of antibody levels in 248 vaccinated healthcare workers, 26 of whom were classified as obese and  56 more as overweight.

In the actual randomised clinical trial of the Pfizer vaccine, there were 13,000* people classified as obese and a further 13,000 classified as overweight.  The trial considered obesity as a factor that might affect the vaccine success.  In people with obesity, the estimated efficacy based on actual Covid cases was 95.4%. In those without, it was 94.8%. This is not mentioned at all in the Guardian story.

It’s quite common for clinical trials to end up with a very non-representative subset of the population where you want to use the treatment.  That’s much less true of the US Covid vaccine trials (apart from being US-biased). They made serious efforts to recruit a wide range of people, and did pretty well.  Any risk factor that’s common in the United States is likely to have been common in the trial population, so it’s already represented in the results — even if the results weren’t specifically reported based on that factor, as they are for obesity.

There’s some good research with careful estimation efforts being made to check that efficacy in practical use matches up to the trial, but just measuring antibodies in a small convenience sample isn’t worth a headline.

* There are a bunch of different analysis populations in the trial, so the number will be different in different places. They’re all big.

• Good explanation by Jarrod Gilbert at newsroom of why gang member statistics tend to be inflated: it’s harder to count people leaving.
• Eric Crampton (correctly) argues that the Climate Commission should just publish its models so people can see what they’re sensitive to. Peter Ellis (who would know) thinks this should be perfectly feasible. I reviewed an air pollution epidemiology initiative that did this sort of thing with analysis code and pollution and health information from the 90 largest US cities, nearly two decades ago. Back then, it was kind of notable; now, not so much.
• The New York Times had a front-page visualisation of 500,000 Covid deaths, as dots over time. People in NZ aren’t the best audience for deciding how well something like this works, but there was an interesting Twitter thread criticising it.
• Truncating bar charts — failing to show them all the way down to zero — is bad in theory and I often complain about it here.  New research suggests it’s actually bad in practice, too.  It conveys inaccurate information even to trained people and even to people who are warned in advance.
• For a bit of nerd fun: Iceberger — draw an iceberg and see which way it will end up floating (motivated by the Campaign For Sideways Icebergs)
• Data collection is hard:  “I’m interviewing 5 year olds and their best friend is a pretzel, so it’s hard to map their social networks.” — Jason Chow at an education conference in the US, quoted on Twitter
• Excess mortality in 2020 in the US (from Kieran Healy)
  

The combination of descriptions about NZ as a small, empty, remote island and news about Tesla buying lots of Bitcoin made me compare this graph of blockchain transactions per day (from Wikipedia, by user HocusPocus00)

with an infographic from paymentsnz for 2019.

There are about a third of a million Bitcoin transactions per day, and nearly 1.25 million Ethereum transactions per day.  How does that compare with the NZ economy?

We get through about a million direct electronic transfers per day  — when you send money from your bank account to someone else’s, like your plumber or a guy on TradeMe or splitting an accomodation bill with your mates.  There’s about 360,000 direct debit transfers — when Vodafone sucks your phone and broadband bill out of your bank account.  We have about 2.8 million EFTPOS debit transactions per day, and about 2.3 million credit card and PayWave transactions per day at point of sale (that was 2019; I’m guessing Paywave is up and EFTPOS is down in 2020).

Now, a single Bitcoin transaction can be more complicated than a single debit card purchase, and it might actually package a whole lot of transactions together (and, no, this isn’t quite like how a single supermarket debit card transaction might include toothpaste and cheese). It’s still a little surprising how few blockchain transactions there are.

The current NZ Covid restrictions happened because someone at risk of infection in their job developed symptoms consistent with Covid and got tested.   With the first load of Pfizer vaccines arriving today, that raises a question about testing.

All the current vaccines seem to do best at preventing serious illness and death, and slightly less well at preventing mild illness. We don’t have really good data on preventing asymptomatic illness, but the information we have suggests that the trend continues and they’re somewhat less effective at preventing asymptomatic illness.  For most purposes that’s a great tradeoff — Covid is a global catastrophe only because people get sick and die, that’s how it’s different from the common colds caused by other coronaviruses.  For preventing border incursions things are more complicated.

When we vaccinate the first target group — people working at the border, the highest-risk healthcare workers, and their household contacts — we dramatically reduce the chance they will get sick or die from Covid, and we reduce, less dramatically, the chance they will get infected and pass the virus on.  We should expect fewer cases of the virus getting past the regular workplace testing — but when it does, it’s less likely to be stopped by someone stepping up and getting a test on their own.  Outbreaks are likely to be bigger by the time we see them, with a higher risk of needing lockdowns.

This doesn’t mean we shouldn’t vaccinate border workers. The whole point of the vaccine is to stop people getting sick and dying, and that’s how we should use it. The answer isn’t to use MIQ workers as coal-mine canaries. We do need to think about how to change testing to respond to the new circumstances.  Many experts have already been calling for more frequent testing of high-risk workers, ideally using new saliva-based tests to reduce the ouch factor.  The case for more-frequent tests will be much stronger as we progressively vaccinate the people at highest risk and become less likely to pick up outbreaks by waiting for people to get sick. 

First, two new vaccines.  Johnson & Johnson and Novavax put out press releases about their vaccines last week. These are two vaccines that NZ has agreements to buy.  The Novavax vaccine is (a slightly modified version of) the viral spike protein. The mRNA vaccines (Moderna, Pfizer) get your body to make this protein, and the DNA vaccines (AstraZeneca, J&J) get your body to make mRNA to make the protein; a protein vaccine cuts out the middleman.   Novavax are reporting good results, estimating 95% efficacy against the original variants of SARS-2-CoV [?Covid Classic], about 85% against the B 1.1.7 (“UK” variant) and about 60% against the B 1.351 (“South Africa” variant). That’s as good as the mRNA vaccines against the original strains. It might well be as good against the new variants — we don’t have direct randomised trial estimates of effectiveness for the new variants and the mRNA vaccines, but we have antibody tests that suggest substantial but lower protection. Johnson & Johnson didn’t provide much data, and they aren’t claiming as high effectiveness, but they are claiming to prevent serious disease and death, and it’s a single-shot vaccine. We will presumably see more information from both companies when they are evaluated by the FDA and its European counterpart, as we did for earlier vaccines.

Next, the AstraZeneca vaccine in people over 65.  The situation has clarified a bit, though it’s still not clear what number Handelsblatt were quoting.  There is very little Covid data (so far) on people over 65, because there were very few in the trial.  This will improve somewhat when data are released from trials in India and the US.  There is good reason to expect that the vaccine would work in people over 65, but very little direct experience.  In a usual drug-approval situation there is a very strong status-quo bias: the status quo is often not that terrible; the drug doesn’t realistically promise a big improvement; you don’t want to give drug companies incentives to cut corners in trials. Usually it’s better to postpone a decision for a year or so to get better information. With Covid, the status quo is a massive human and economic disaster, and the balance of risks is very different. Leaving people unvaccinated is dangerous, epidemiologically and to the global economy as well as to them personally, so it’s not clear whether the ‘safe’ approach for regulators is to approve the vaccine for all ages or carve out an over-65 exception. It probably depends on the country.

Finally, arithmetic. Israel has been doing well at delivering vaccines, and we’re starting to see rates of infection after vaccination. These aren’t straightforwardly comparable with vaccine efficacy numbers.  A story in the Jerusalem Post has the headline Just 0.04% of Israelis caught COVID-19 after two shots of Pfizer vaccine  and goes on to say (emphasis added)

According to the studies conducted by Pfizer, the vaccine had an efficacy of about 95%, which is considered very high. The Israeli data appear to confirm the inoculation’s effectiveness, showing an even more promising result.Later in the day, Maccabi Healthcare Services – one of the country’s four health maintenance organizations – released the first results of the vaccination campaign of its members, with the organization also comparing the data to a control group that did not get inoculated.
Some 248,000 Maccabi members were already a week after the second shot as of Thursday. Of those, just 66 got infected with the virus, the majority of them over the age of 55 and about half of them with preexisting conditions. All those infected experienced only a mild form of the disease, and none were hospitalized.Over the same period of time, some 8,250 new cases of COVID-19 emerged in the control group of some 900,000 people having a diverse health profile. Those who were not inoculated were therefore 11 times more likely to get the disease than those who were immunized, showing 92% effectiveness.

95% effectiveness means you’d expect 5% of vaccinated people to test positive as infected if 100% of unvaccinated people did. Or 0.05% test positive as infected if 1% of unvaccinated people did. But 1% is a very high rate — the US, at its very worst, wasn’t getting close to 1% of the population as new cases in a week.