Meanwhile in Melbourne

Victoria had a literal earthquake yesterday, an event that is unusual in Australia. However, the bigger news over the past three days is a series of protests in Melbourne that have resulted in violent clashes with police. Here’s the ABC on yesterday’s protest:

“More than 200 protesters have been arrested after a stand-off with police at the Shrine of Remembrance, with two police officers sustaining injuries in the showdown. The protesters were given penalty infringement notices, with some charged with more serious offences for discharging flares, and throwing golf balls, tap handles and batteries at police.

The two police officers injured were struck in the head with bottles, while another was admitted to hospital with chest pains.

Hundreds of protesters gathered at the war memorial on Wednesday to protest against the coronavirus lockdown and mandatory vaccines.”

It’s worth adding a degree of scepticism as to why a protest turns violent when there are riot police involved but overall these appear to be more than just rowdy protests that became violent once police attempted to disrupt them.

Ostensibly, the protests were because of new restrictions on construction sites due to anti-covid measures. Construction work had been allowed during recent lockdowns but under strict rules to prevent the spread of covid on sites. However, due to poor compliance with these measures, the Victorian government had indicated that tougher measures would need to be implemented. After Monday’s protest, they shut down most construction work for two weeks.

And this is where things get murky. Monday’s protests focused on the construction worker’s union, the CFMEU and appeared to be construction workers unhappy with lockdown measures and mandatory vaccinations for people on construction sites. However, the CFMEU doesn’t support mandatory vaccinations and quickly alleged that many of the protestors were not members of the union and also that many might not be construction workers at all (or “tradies” i.e. people in associated trades). Observers pointed out that while protestors were wearing the characteristic hi-vis clothing, that often the clothing was new and unlabelled. (Having said that, wearing clothes without elements that make it easier for you to be identified would be a smart thing to do regardless.)

The counter-claim, which has a lot of substance, is the protests were predominantly anti-vax/anti-lockdown protestors with some construction workers, as well as far-right groups and (of course) in a big city there are going to be at least some people who are all three of those things. News reports are also suggesting that the proportional makeup of the protests has shifted over the past few days so that the number of construction workers involved has reduced.

There’s a longer analysis of the protests here:

“The far right has really sought to mobilise frustrated people and push them more toward right-wing narratives, particularly white nationalist narratives. There is a strong historical animosity toward trade unions (as the vanguard of the political left) by the far right. It would be disingenuous to view the far right as unintelligent thugs. They are learned in the history of national socialism and fascism and the preconditions for its rise.

So you see the far right working very hard to undermine trade unions and the way they represent the organised working class. There is an attempt to undermine trust in trade unions and paint them as traitors and sell-outs who are in bed with the government.

Among the protesters there was a really self-conscious effort to represent themselves as themselves as tradies and workers. Some observed protest organisers encouraging people to wear hi-vis clothing to these rallies.”

Anatomy of the backlash against covid measures

Partly this came out of trying to describe the journey some right-wing figures have taken during the covid pandemic. Once you step into right-wing social media there’s often a Gish Gallop of stuff on covid with a broad conspiratorial message (i.e. the idea that covid is somehow a plot by the government against everybody). The problem is you get such a mix of things that reasonable and semi-reasonable positions are mixed in with utter crackpot stuff. For example, there are legitimate questions about lockdown measures and about heavy-handed police tactics that are actually becoming harder to discuss because those issues keep getting hijacked by gibberish.

The overall goal is to undermine the consensus and effectiveness of public health measures by tapping into a. legitimate fears and b. existing nonsensical fears. By legitimate fears, I mean for example the usual kind of policing & surveillance powers governments grab in a crisis but also legitimate questions about jobs or the psychological & social impacts of the current situation.

If you want an example of the kind of omelette of covid-conspiracy being served up, here is Peter Grant

If you are careful you can pick out things that are semi-reasonable amid the things that are nonsense, and some of the nonsense has plausible elements.

I’m playing with a list of common elements in these kinds of positions to help clarify how far down the conspiracy trail particular figures have gone. Some of these elements are, by themselves, reasonable positions or, at some point in time, were reasonable positions to have questions about. Others are just nonsense or built from more general anti-vaccine or anti-government tropes reapplied to the current crisis.

This is a rough initial list and not presented in a particular order. I would expect a reasonable, non-conspiracy theory minded person to at least have some sympathy or accept the reasonableness of some of these points i.e. many of these in isolation don’t make you a covid-denier by any means and several of them have their own spectrum that runs from reasonable doubts to full-on conspiracy-mongering.

  • Blames China: this is more of general tone of attributing Covid-19 as the fault of China in a vague sense. More spin and framing than conspiracy theory if presented in isolation.
  • Claims China created the pandemic: this is an overt conspiracy theory, more common in the early days of 2020, that the virus is a deliberate policy of the Chinese government. This grew less popular on the right because it sits poorly with the other conspiracy idea that covid-19 is a minor ailment.
  • Lab leak hypothesis: a hypothesis isn’t a conspiracy theory and currently it’s not impossible that covid-19 arose in a medical laboratory researching corona viruses. Not impossible…but also the evidence remains thin and circumstantial. The path into conspiratorial thinking is the step were a hypothesis is asserted as fact despite the paucity of evidence AND the idea that the ‘truth’ is being hidden by governments internationally.
  • Covid statistics/reporting is false: obviously medical statistics are imperfect and as we’ve seen in other fields, any legitimate uncertainty in figures can be used to cast doubt on everything. We legitimately don’t know the ‘true’ rate of infections because cases of covid can be asymptomatic but that’s not the same as people having no idea at all.
  • Low mortality claims: a more specific claim about covid stats is that the number of deaths is exaggerated. More common last year but still present and usually based on the idea that older people dying of covid may have been close to death anyway. Obviously, there are going to be edge cases with cause-of-death reporting but that’s always true. This is also an example where the initial situation when health officials had little information to go on is used to discredit official information in general (i.e. the figures changed over time as people got better data).
  • Low infection rates claims: similar to the above but with the degree to which data on infection rates is imperfect and subject to change.
  • Strawman claims social distancing doesn’t work: arguably it doesn’t “work” because by itself it doesn’t stop covid but the claim here is were people ignore that social distancing is part of multiple strategies to slow the spread. The arguments presented by the more conspiratorial minded treat social distancing as a strawman where it was supposed to (somehow) stop covid completely…and as it hasn’t therefore everything was a big lie etc.
  • Strawman claims masks don’t work: this one has had a long evolution and was exacerbated by initial confused messaging about masks. You can find people on the right who were initially pro-mask when the official advice was at best mixed about masks (first quarter of 2020) who shifted to being anti-max when the advice changed in favour of masks. Rules mandating masks have been an obvious point of friction and understandably so. However, the efficacy of masks follows arguments similar to ones about social distancing i.e. if they are imperfect then they must (somehow) be useless with no territory inbetween 100% effective and 0%. Also, it really isn’t impossible that when all is said & done and long term studies of mask policies evaluate their effecitvness, that perhaps those policies didn’t do much (or maybe the opposite and they saved many lives). Imperfect knowledge is part of the nature of dealing with a new disease.
  • Claims that messaging on anti-covid measure were lies because they changed: these are agument intended to discredit whatever the current advice is and tie into questions about social distancing, masks and lockdowns or other restrictions. There’s an undeniable fact there that public health messaging changed over time but the reasons are obvious. Firstly imperfect knowledge and secondly public policy is always going to be a trade off based on multiple political factors.
  • Claims lockdowns don’t work: another big spectrum of claims that range from reasonable criticism to absurdities. International and regional approaches to lockdowns have been varied and the implimentation of them has raised many legitimate questions. Mixed in with that spectrum of discussion are variations on some of the same style of strawman arguments discussed above.
  • Lockdowns are damaging: this is undeniable. Clealry being stuck in a house is psychologically unpleasant at best and very difficult for many people. There are clear economic impacts as well. However, the impact of lockdowns is not easy to quantify and in the more conspiratorial social media space you quickly find poorly sourced claims that impact is much higher than has been documented.
  • Lockdowns are some sort of plot: this is more overt conspiracy mongering i.e. the idea that government are trying to trap people in their homes for nefarious reasons. It’s hard to deny the authoritarian streak in many governments but that streak has always been accompanied by those same governments wanting people going to work and if not working, going to the shops.
  • Survelliance issues: government covid tracking apps or sign-in apps have created an issue where the balance of a public health crisis meets the genuine fear of how the government or police might abuse the information they collect. Many abosultely 100% legitimate concerns but also an entry point into broader conspiracies.
  • Food shortage predictions: these were more common mid-2020 with the idea that lockdown measures were going to stop farmers growing food or a general economic collapse because of covid.
  • Anti-Fauci: specifically in the US. This is one of the simplest and most direct indications of somebody going a long way down the covid-denial rabbit hole. Memes or rhetoric attacking Dr Anthony Fauci because of his high profile role as chief medical advisor to the US President. In other countries, this might be directed at similar figures who have had equivalent roles.
  • Experts were wrong: this is similar to the issue about changing messaging on public health advice but with a specific focus on claiming that key experts (such as Dr Fauci) were wrong at some point in the pandemic but in particular focusing on what was said in the first several months of 2020. The point here is to discredit medical expertise in general (as opposed to just government public health advice in general but obviously the two are connected).
  • Experts lied: the more extreme version of the point above but with the added twist that what was said was lies or intended to decieve the public for nefarious reasons.
  • Pro-Hydroxychloroquine: the drug did really once look like it might have some effect against covid-19 but systematic trials showed that whatever benefits it might have were slim (at best) compared to the risks. So there’s a bit of a time spectrum here, somebody saying in March 2020 “hydroxychloroquine might be a cure” is speculating whereas somebody saying that it is a cure in March 2021 is ignoring medical evidence. Again, medical understanding changes and who knows, somebody might discover a way it treats covid in some people or in some circumstances at some point in the future…or they very well might not. Claiming it is a cure now is making claims that run counter to known facts.
  • Pro-Ivermectin: there genuinely were studies showing some effectiveness of this anti-parasite drug against covid but those were in-vitro studies with high concentration. Clinical trials led to a mess of information when a number of low-quality (and possibly fraudulent) trials showed amazing success, along with other trials that showed little or no positive results. So again, there’s a time factor here. A reasonable person could have looked at the available evidence late in 2020 and concluded that ivermectin had promise. Not changing your mind about that in the face of evidence is a different matter. There’s another dimension here which is the socioeconomics of the pandemic. With access to vaccines being far more limited in many developing nations, the use of ivermectin has continued because of its relative availability as an anti-parisitic drug for humans and animals. If, on the other hand, you are an affluent person in a affluent city taking horse paste instead of a vaccine then, yes, you deserve at least some mockery.
  • Other fake cures: by this is mean the more obvious non-science based quackery. If somebody is selling homeopathic cures for covid for example.
  • General anti-vaccine nonsense: there is a two-way street here. There are reasonable and semi-reasonable issues listed above that help bring some people along into more weird positions. Similarly, people who were already anti-vaccine follow a path to adopting other positions (or joining an anti-lockdown protest). In principle somebody could be anti-vaccine but pro-lockdowns or pro-vaccine specifically as an alternative to lockdowns but the further down the conspiracy path you go, the more the whole set of beliefs gets adopted.
  • Covid vaccine dangers: vaccines don’t have zero risks but they have low risks compared to other common medication. Hyping up actual side effects or claiming false causality when a vaccinated person suffers some unrelated ailment, are standard anti-vaccine tactics. Again, there’s a spectrum here of reasonable concern through to conspiratorial nonsense.
  • Claims covid vaccines don’t work: this varies from ‘just asking questions’ stances as to why the vaccines haven’t been miracle cures already to overt claims that the vaccines don’t work. This is often accompanied by misinformation or misleading stats (e.g. pointing to the proportion of covid cases in the vaccinated v unvaccinated in countries with 70%+ of the population with at least one vaccine dose).
  • Claims covid vaccines aren’t vaccines: this maybe specifically pointed at vaccines such the mRNA style vaccine such as the Pfizer vaccine on the ground that they don’t work in the traditional way. This is often a lead into nuttier anti-vaccine conspiracies.
  • Nuttier anti-vaccine conspiracies: too many to list but these are the more obviously out-there claims about tracking chips and 5G networks etc.
  • Anti-booster shot: many vaccines need multiple shots. Chicken pox, for example, is the gift that keeps on giving and past infection doesn’t give you lifelong immunity but instead the chance of getting shingles in later life. Influenza adapts to human immunity with such agility that yearly vaccinations are needed. However, the fact that covid vaccines might need additional shots is being used as a rhetorical point to bolster other claims from the nuttier ones to stoking fears of side-effects.
  • Anti-vaccine mandate: can and should governments make you take a vaccine? What about employers? There are legitimate ethical questions there but add in any of the more conspiratorial aspects listed above and the idea that people will be pressured or legal obligied to take covid vaccines takes on a more sinister aspect.

I’m not sure if I should group these or rank them (e.g. from reasonable to utter nonsense) or group them thematically and then rank them. I want to stress again that I’m including reasonable points (or points that start reasonably or which were reasonable at some point prior to further data) not to demonise people who have sensible concerns but to try and get a sense of the spectrum of the issues and to see the entry points to more radical beliefs.

Baseline shenanigans

Vaccine denial and vaccine hesitancy have a very long history and as with many of these things, overt nonsense is mixed in with genuine concerns. Many socially and economically disadvantaged groups have reasons to be wary of the medical profession and many developing nations have reasons to be wary of initiatives from Western governments and/or companies for example.

However, what we’ve seen almost in real-time is vaccine misinformation shift from being a fringe belief only partly connected with ideology to something that is increasingly not just political but politically partisan.

As always, my canary in the coal mine for tracking this is Theodore Beale aka Vox “I’m not a neo-Nazi” Day. Now Day has a long history of pushing vaccine misinformation and fears, including hyping up concerns about mercury in vaccines and the false claims of vaccines being a cause of autism. Yet despite a history of raising fears about “vaccine safety” that date back to at least 2004, the volume of his posts about vaccines split approximately 50/50 from prior to 1/1/2021 to after 1/1/201*

His latest (false) claim is that “Vaccinated 5x More Likely to Die” He is wrong and demonstrably so but how he gets to this conclusion is instructive. If you guessed that he gets there via bad numeracy and poor reasoning you will be correct. If you also guessed “via second-hand sources” you would also be correct.

Now we will have to do some arithmetic but it’s only division.

There is usually a nugget of truth somewhere and in this case, the nugget is a UK government report on the prevalence of different variants of the SARS CoV-2 virus (alpha, delta, alligator etc). You can find the report here

Vox D then grabbed some other persons attempt to spin one table from that report and then did some basic arithmetic to jump to an erroneous conclusion.

“Do the math. An unvaccinated individual in the UK who contracts COVID has a 1 in 597 chance of dying. A fully-vaccinated individual has a 1 in 117 chance of dying, which is 5.1 times greater.”

Where is the error? Not in the division. He divided some numbers and got those values but simply doing a calculation correctly isn’t the same as doing the right calculation.

The data he worked with is from a table on pages 18 & 19 from the report above. In fact, he’s mainly using the last row of the table which spills over onto page 19. I’ve put together the rows and columns he used but I won’t include all the other columns to focus on the numbers he uses:

Total2 dosesUnvaccinated
All Deaths (delta)742402253
All delta cases30001047008151054
Per cent0.25%0.86%0.17%
1 chance in404.3261456116.9353234597.0513834
Data taken from Table 5. Attendance to emergency care and deaths of confirmed and provisional Delta cases in England by vaccination status
(1 February 2021 to 2 August 2021) with added calculations

Out of context, that looks alarming and confusing! Yeah but no. It’s horribly misleading and relies on two errors. The first is that you aren’t comparing like with like from the data in the table itself and the second is a baseline error. Day claims that a “fully-vaccinated individual has a 1 in 117 chance of dying” but that conclusion is demonstrably false.

So the first error can be seen directly in the table. The data split cases by age: <50 years old and ≥50 years old. That’s obviously important with mortality statistics (and even more so with COVID19) but also with vaccination status.

Do the same calculations again but this time use the data from the table that is specific to the age groups.

Total2 dosesUnvaccinated
Deaths <50711348
Cases <5026574925536147612
Per cent0.03%0.05%0.03%
1 chance in3742.9436621964.3076923075.25
Deaths ≥50670389205
Cases ≥5033736214723440
Per cent1.99%1.81%5.96%
1 chance in50.3522388155.1979434416.7804878

The first problem with Day’s calculation becomes apparent. The proportions of the two age groups is different between the 2 dose group and the unvaccinated group. A greater proportion of the unvaccinated over 50s died compared to the 2 doses over 50s.

So OK, Day screwed up that “5.1 times greater” figure and screwed up the probability by comparing two groups that were demographically dissimilar but don’t the data still point to their being an issue? After all for the under 50s that small percentage who died is still bigger than the percentage for the unvaccinated? Still no because it ignores a key fact about the UK for the time range the table applies to.

Britain started its vaccine program very early and by 2 August 2021, a majority of people had been vaccinated The vaccines aren’t foolproof, they do reduce the risk of catching COVID19 and do reduce the chance of hospitalisation and death substantially but there’s always some chance. So we also have to factor in the proportions of the population vaccinated.

By 2 August, 71% of the UK population over 16 had received two doses of a COVID19 vaccine. 86.2% had received at least one dose, which leaves 13.8% unvaccinated at all. We can’t directly apply those numbers to the numbers above because obviously, those proportions shifted over the several months worth of data shown in the table above. The number of cases from fully vaccinated people comes from a much bigger group of people than those from the unvaccinated. Not only that but you are unlikely to have the same patterns of hospital admission between vaccinated and unvaccinated people as well as other demographic differences between the two.

*[based on using date ranges in a site-specific Google search – your mileage may vary]

A short follow up to yesterday’s post

The Guardian’s data blog has an article with a graph that shows visually the socio-economic dimension I was discussing.

The four lines are quartiles of socioeconomic status with the darkest lines being the most advantaged and the lightest being the most disadvantaged.

You can see in mid to late June that most infections are in Quartile 4 but in early July, infections were being observed increasingly in Quartile 1 areas,

Still in lockdown

Greater Sydney (basically metropolitan Sydney and surrounding communities within commuting distance) is still in lockdown as cases of covid still are being detected at around 200+ a day.

I mentioned at the start of this current lockdown the bizarre social-class dimension to covid outbreaks. With Sydney, this outbreak started out in relatively affluent suburbs and the state government hoped to contain the outbreak without a fall lockdown. It wasn’t quite a “lockdowns are the last resort” mentality but the tougher measures were still delayed by crucial days. The result of trying to avoid a lockdown was a) you get a lockdown anyway and b) it is longer because community transmission is more widespread.

Covid’s cruel punch-line to the social class dimension is that while the outbreak started in wealthier suburbs, it has become entrenched in poorer suburbs — mainly in the southwest part of Sydney’s urban sprawl. So as the measures have become harsher, the places and people feeling the worst of it are people with less income and fewer choices.

In 2020 state and federal politicians showed more willingness to help people stay home rather than attempt to force people to stay home.

Across New Zealand and Australia, we’ve had 15 months of experience with managing outbreaks with mixed results. Other countries circumstances may be different (especially in terms of the practicality of limiting internal movement between major urban centres) but overall there is a clear pattern of what works: lockdown early and pay people to stay home by supporting impacted businesses. That approach leads to shorter lockdowns and quicker returns to normality. What doesn’t work is dithering about lockdowns and trusting in aggressive policing to get people to stay home. We’ve known since at least March last year that cases rise exponentially when unmanaged and that there is can be a delay of days in terms of knowing the extent of the spread.

Australia has also messed up two other aspects.

Firstly the hotel quarantine system has been doubly inadequate. It’s firstly been the source of multiple community outbreaks due to poor controls in place leading to infections spreading and by having the majority of quarantined travellers in the CBDs of major cities when infections spread from people returning, it is straight into areas with high population density. Secondly, the numbers that can be accommodated are too small to manage the number of Australians who want to return home but can’t get flights because of caps on numbers. As a stop-gap in 2020, the hotel-quarantine policy made sense but the government has had months to develop better options.

Secondly, the vaccine roll-out has been shambolic. Again, there was some wisdom in Australia taking a slower approach than say the UK at the start of the year. Even with the current outbreak, covid rates are relatively low compared to other countries but luck was playing a role in that.

A slower approach didn’t stop major errors, the biggest of which was initially putting hope in just one vaccine: Astra-Zeneca. A rare blood-clotting side effect meant that the vaccine was subject to shifting health advice, which in one sense couldn’t be predicted but in another sense was inevitable. Once vaccination happens en-masse, inevitably differences between the various vaccines would become more obvious and not just side-effects but potentially in efficacy or ability to cope with variants. Trusting in just one was a gamble. Even given that, health advice and vaccine availability has been muddled and difficult to navigate.

Overall, Australia has escaped much of the worst aspects of the global pandemic through luck, circumstance, geography, timing and some good policies. Looking at events now what worries me more is not covid but the next virus. There will be another global pandemic at some point, possibly another SARS type disease, possibly something else. That governments are still making avoidable errors with covid makes me apprehensive that the next big pandemic will proceed as badly as this one. It may even be worse given the more entrenched opposition to public health policy that has developed this cycle.

Covid won’t necessarily get naturally less deadly by itself

The topic of consensus has come up recently and it is interesting to look at the flip side of scientific consensus and look at broad rules of thumb that exist in wider society. With diseases caused by viruses, bacteria and parasites etc there is a reasonable (but flawed) assumption that over time a specific disease will become less deadly. The assumption rests on a rough sketch of how evolution works. An infected person needs to be alive for the virus to grow and spread and so, killing the infected person is of less advantage to a virus than leaving the person alive and walking about. It’s a reasonable idea because we are all hosts to a wide range of viruses that cause common colds that usually just make us snotty and miserable rather than dead.

But it is no more than a rule of thumb and the reasonableness of the idea hides a whole pile of complexity. Also, there’s an underlying cognitive error we all fall into when considering how natural selection works that makes us pretend that there’s some sort of agency behind how these changes happen. A virus doesn’t want to kill people, it has no wants or any capacity for anything like wants or a direction nor does evolution strive for perfection. An additional reasoning error is a more subtle version of the old anti-evolutionary argument “if humans evolved from apes, how come there are still apes?” Evolution spawns new varieties of reproducing things rather than just replacing old ones with shiny upgraded versions.

A moment’s thought about examples of long term diseases that humans have faced shows that many diseases remain very deadly despite long histories. Evidence of smallpox is present throughout most of recorded history and its deadliness was reduced not by the virus becoming less virulent by itself. Obviously, there are related viruses to smallpox that are less deadly but humanity had to live with those as well as smallpox. Improved care reduced the deadliness, inoculation as practice (intentional infection of people with matter from a smallpox-infected person possibly first used in China) reduced the impact of the disease and eventually, vaccination led to the disease being wiped out.

Influenza keeps working its own happy way through the evolutionary gambling tables each year, throwing up variations that are more or less injurious. Every living (or not quite living) thing is a glitchy, cobbled-together trade-off of adaptations. “Less deadly” is one direction but there’s not a simple genetic switch or “deadliness” parameter a virus can turn up or down without affecting other features of the virus.

Now I’m not a virologist or even a biologist. I don’t know what the odds of new variants of covid being more deadly are. The rule of thumb isn’t utter nonsense, all other things being equal, I can see why it makes sense that maybe a more infectious & less dangerous version of the virus might become more dominant and maybe (if we were very lucky) also give people sufficient immunity that the nastier versions would fade away. I wouldn’t bet money on it though. Again, appealing to what we can see, covid currently is relatively slow to kill people and there’s plenty of time for an infectious person to spread the virus before they feel so sick that they aren’t out and about spreading the disease. Also, many infected people are asymptomatic, so the deadliness is not much of a disadvantage to the disease.

But we really can only get so far trying to think these things through with general knowledge and a critical eye. Expertise matters and literally whatthehelldoIknow. Expertise matters much more, particularly when evaluating multiple competing factors. Here’s an article by experts in microbial evolution and mathematical biology explaining some of the issues far better than I can:

That article also links to an academic paper looking at the potential evolution of the SARS-CoV-2 virus. That paper looks at multiple ways the virus may evolve into new strains. On this specific topic it notes:

“A crucial question is how virulence will evolve [28]. As discussed above, direct selection on virulence is weak (Figure 3D,H). Thus, virulence evolution will be driven largely by the indirect effects of pleiotropy. In Figure 4, we consider two potential examples. First, consider mutations that couple a higher transmission rate, the βs, with higher mortality, ɑ (positive pleiotropy, Figure 4A,C), as might occur if mutations increase viral replication rates. In this case, evolution will lead to higher mortality (see inset bars), as an indirect consequence of selection for increased transmission (see Supplemental Information and also [12,29]). Alternatively, consider a mutation that alters tissue tropism such that the disease tends to preferentially infect cells of the upper respiratory tract, rather than the lower respiratory tract. Such infections could lead to a higher transmission rate but be less virulent (negative pleiotropy) [30]. This would generate indirect selection for lower mortality rates (Figure 4B,D).”

On the evolutionary epidemiology of SARS-CoV-2, Troy Day, Sylvain Gandon, Sébastien Lion, and Sarah P. Otto Current Biology 30, R841–R870, August 3, 2020

[Word of the day: pleiotropy – when a gene impacts two or more unrelated traits]

So there you go, right? I’ve got experts and an academic citation from a paper with maths in it AND GRAPHS! Case closed, right?

Not really. I like the argument I just wrote but it is far from immune from being BS. I’m smart, STEM-educated and I can find academic papers and quote from them (and thank the Humanities for those skills). Yet, I’ve no real idea whether the academics I quoted are actually good at their jobs. I don’t know whether the two essays I’ve quoted are actually making well-known errors in the field of evolutionary virology or pushing some heterodox minority position. For all I know, the field of evolutionary virology is currently engaged in raging flame wars on this very issue and there’s a really, really strong argument that (aside from a few exceptions) viruses nearly always get substantially less deadly for reasons other than better treatment or vaccines. It’s not just that I’m not an expert on these topics but also I don’t know anything about the community of people who ARE experts.

Now, given the currency and high profile nature of this issue, I’m fairly confident that I’m not making an ass of myself and quoting a paper that virologists are scorning. Yet, this takes me back to the real topic of this post: consensus and truth not just in science but in any body of knowledge/field of expertise.

A body of knowledge is not a set of textbooks but a community of expertise in which opinions and experience matter. Those communities are flawed. They will have biases. They will be slow to adopt new ideas that are actually more true than old ideas. They will be vulnerable to professional and commercial pressures. These things are true because science is done by humans and communities of humans have these issues. That means we should not unthinkingly accept what any given community of experts say as the unimpeachable truth. However, the odds are that a community of expertise that adopts methods of self-correction and reasoning is far more likely to be a source of truth than our naive intuition about complex issues.

Do I have one more rhetorical trick up my sleeve to convince you that covid won’t necessarily get less deadly? I have lots but as this is a portmanteau essay on many things, including the art of rhetoric then I shall use a failed student of the art to convince you that covid can get worse:

“These utterly ignorant idiots don’t understand that it is the flawed vaccines that are causing the next variant to be worse, not the unvaccinated. If it had been left to progress naturally through the population, the virus would have become more infectious and less harmful, like every other virus in history. It’s already doing that, which is why the Delta variant is estimated to be 10x less lethal than the original one.”

Vox Day,

I call the move I’m making here the anti-appeal to a lack of expertise 😀

Covid: Trajectory

A few weeks in and Australia is still struggling to get on top of the recent delta-variant outbreak of covid. It’s apparent that the initially muddled and delayed lockdown in Sydney was too late to keep the virus bottled up and it is now spreading in more rural areas.

As regular readers will know, I’ve tried to stick with one fairly consistent way of looking at covid numbers: cumulative confirmed cases per population size. There are no perfect numbers and these figures have the same issues in terms of being dependent on testing rates. However, my point about the graphs has been that it is not so much the magnitude as the slope of the graph. Lower testing rates may obscure or delay how that graph gets steeper but when the virus is spreading exponentially it will show up in the numbers.

On the “delay” aspect with testing, I thought I’d illustrate that with a simplified graph. The underlying data is a number that increases proportionally by 1.2 times the previous “day’s” number. Two lines show 5% of the current day’s total (blue) and 1% of the current day’s total. Obviously, testing rates do impact the number of reported cases but inevitably the numbers go wooosh regardless.

Putting the curves side-by-side helps show how the pattern is similar but these two lines look identical if plotted with different scales on the vertical axis. The underlying shapes are similar in a mathematical sense where the difference is scale.

Anyway, still locked down for the time being and the curve is still going up.

I am in Lockdown [updated]

[Update: the state government has decided the plan was too complicated and now the Greater Sydney Area is all in lockdown. So my situation is basically the same but less ambiguous.]

The delta variant has officially come to town in Sydney and once again the oddly social class nature of Covid is on display. There’s nothing new about infectious diseases having a socio-economic aspect – social interaction and mobility play a role, as do access to healthcare and resources. However, at different stages Covid has had an odd impact on wealthier areas, often radiating out from areas of affluence (e.g. in the early international spread via ski resorts). That impact, of course, affects people of all income levels not just rich people.

For Sydney, the location of the airport and quarantine hotels is at the Eastern end of this very asymmetrical city and that’s also where the posh suburbs are. Not just posh suburbs of course and not just wealthy people. The suburb of Redfern is caught up in this core area and while that suburb is undergoing rapid gentrification it still retains a significant Aboriginal community. The current outbreak has put four local government areas into lockdown and basically, those areas are the main CBD (the bit with the opera house and the bridge that gets blown up by aliens) and the suburbs directly east of the CBD with the beaches, gazillion-dollar homes, pricey shops and (not so many any more) scruffy backpackers etc.

On a personal note, I’m safely well away from that area, way out in the sprawling areas that are prone to bushfires but less prone to sporadic Covid 19 outbreaks. But…the lockdown also applies to people who have worked in CBD in the last two weeks for at least three days. Which…isn’t quite me, did two days of work in the CBD the other week but better safe than sorry. So according to the official advice I’m not obliged to be in lockdown but I’m going to err on the side of caution.

The slow and fumbled vaccine roll-out here is part of the problem. There are still lots of vulnerable people not vaccinated and this current outbreak involves an unvaccinated limo driver whose job was ferrying flight crew to their hotel.

Covid graph update

I left off doing these for a while as the situation wasn’t shifting much globally. Today, I’ve picked a grab-bag of countries that we’ve looked at before or are in the news.

The good news is that both Israel and the UK which had very swift vaccination programs have levelled off. However, that is not necessarily showing cause and effect.

The main news story on the pandemic is the disastrous second wave in India. Using the style of graph I picked, the numbers are misleading. India is a big country in both area and population and the per-capita figures belie the impact of this new wave. As I have said before, it is the trajectory that matters with these graphs and that becomes clearer when India is graphed by itself.

I’ve been sceptical about the utility of looking at the death rates in these graphs for various reasons but with the vaccines in play now, we should expect to see an impact.

I’ve focused on the last few months so as to keep the vertical scale manageable.

The world is not out of the woods yet.

Some graphs for February

I’m jumping between two different topics rather than doing separate posts.

Firstly, global temperatures. As per usual, I’m looking at the satellite data set from UAH, not because it’s the best but because it avoids a couple of bad faith arguments about the data:

La Niña slowing things down right now, making for a relatively wet summer in Australia. Not wet enough to avoid bushfires though

Jumping to the pandemic, a question I was asked is when we will see vaccinations make an impact on Covid cases? I haven’t found an article on that but it is a good question. The short answer is “not yet” looking at the graphs and it might not ever.

Currently, Israel has the most intensive vaccination program (but foolishly not originally for everybody). This chart shows does administered per 100 people for a range of countries:

Here is the cumulative case numbers relative to population size for the top three countries for vaccination roll out. It’s way too early to see any impact.

Vaccines may not impact these numbers at all ( ) but should impact mortality and other impacts (i.e. covid might well stick around but do less damage).