Colds can protect against Covid, study finds

COVID Quickly, Episode 21: Colds Build COVID Immunity, and the Omicron Vaccine Delay | Scientific American - Josh Fischman & Jeffery DelViscio, 60-second Science (transcript):

January 19, 2022 - "From early in the pandemic, its been clear that not everyone is equally vulnerable to SARS-CoV-2, the coronavirus that causes COVID. Some people get really sick, while others have mild symptoms or none at all. And this was true before any of us were protected by vaccines. Overall about 80 percent of infected people get a mild illness. The virus is so wildly infectious, though, that the 20 percent of serious cases have been a global catastrophe. 

"But in people who don’t get very ill — what’s protecting them? We hear a lot about neutralizing antibodies, but this is a new virus to us. You don’t get antibodies until after you’ve been exposed or vaccinated.  

"It turns out some people might be getting help from another part of the immune system — T-cells, which were triggered years ago by exposure to different but related coronaviruses. These microbes have been with us forever, and they cause sneezing and runny noses: the common cold. The cold coronavirus and the pandemic coronavirus are distantly related. But they do share similar proteins. Early in the pandemic, scientists noticed that T-cells that reacted to the cold virus also reacted to the pandemic virus. These are called cross-reactive T-cells. But since researchers saw this in test tubes, they had no idea what this meant for immunity in real life. 

"Ajit Lalvani, an infectious disease physician at Imperial College London, decided to find out. England has a really good contact tracing system. It allowed Lalvani and his colleagues to find 52 people who lived with individuals who tested positive for COVID. These 52 started out negative themselves. Within about 3 days, half of that group, or 26, turned positive. The other 26 stayed negative.

"Taking a closer look at the negative people, who didn’t get COVID, Lalvani’s team found that 7 of them had a lot of these cross-reactive T-cells. None of the people who got COVID had such cells. Zero. Lalvani told me this is a very substantial protective effect, and his team just published the results in the journal Nature Communications. The T cells, primed by the older cold virus, recognize the new pandemic virus because it has those similar proteins, and they work to fight it off.

"Immunologist Alex Sette, from the La Jolla Institute for Immunology, told me he thinks this research is on the right track. Other studies have linked recent exposure to these cold viruses with less severe COVID, says Sette, who was not involved in Lalvani’s work. Research has also tied pre-existing T-cells to a stronger immune response to a COVID vaccine....

"T-cells ... point to new targets to add to second-generation vaccines. The proteins these T-cells reacted to went beyond the well-known spike protein at the heart of the first-gen shots. The cells respond to other proteins called N and O-R-F, for instance. Adding those to a new vaccine formula, both Lalvani and Sette suggest, could widen the protection of vaccines against variants.;;; 

"A few companies have such 'T-cell enhanced' vaccines in the works. Gritstone and ImmunityBio, biotech firms in California ... have started clinical trials.  Including more of these proteins in a shot could create a vaccine capable of boosting your immunity against whatever variant comes along."

Read more: https://www.scientificamerican.com/podcast/episode/covid-quickly-episode-21-colds-build-covid-immunity-and-the-omicron-vaccine-delay/ 

Read study: https://www.nature.com/articles/s41467-021-27674-x

Lockdown zealots embrace conspiracy theory

Quacks in the Ivory Tower: How Conspiracy Theorizing Took Over Lockdown Science | American Institute of Economic Research - Philip W. Magness:

June 19, 2021 - "Is the Great Barrington Declaration (GBD) simply a front for a secret global eugenics plot, hatched at AIER [American Institute of Economic Affairs] by the British Ministry of Defence and financed by the Koch Brothers as part of an ongoing effort to force climate change, tobacco, and Covid-19 infections on our senior citizens? Such claims may sound like the farcical ravings of an internet paranoiac, yet precisely this narrative has gained a shocking amount of currency among ostensibly serious public health scientists and journalists since the Declaration launched last October 4th....

"Nafeez Ahmed, a writer from a London-based blog called the Byline Times ... has spent the better part of the last year posting conspiratorial bromides against scientists who question lockdown ideology. Fresh off of an unsuccessful Twitter campaign to flood the GBD’s website with fraudulent signatures during the week after it went live, Ahmed shifted his tack in mid-October with a new charge. In a succession of blog posts, he purported to show that the GBD was part of an elaborate scheme by libertarian billionaire Charles Koch to force the reopening of the American economy in spite of Covid’s risks, using AIER as its front. After outlining the financial portion of his conspiracy theory, Ahmed quickly appended a new 'partner' in the alleged plot: the British Ministry of Defence, which he implied to be the source behind the GBD’s website. To top it off, this growing plot was allegedly orchestrated through the owner of a resort hotel in Wales....

"Contrary to Ahmed’s wild imagination, Charles Koch had absolutely no involvement with the Great Barrington Declaration or AIER’s hosting of the conference that produced it  ... (the few exceptions where a Koch-network organization has weighed in on the subject at all tend to take a pro-lockdown stance, such as the Mercatus Center’s Tyler Cowen, who awarded a research prize to Neil Ferguson of Imperial College for his Covid-19 lockdown model. AIER was one of the first high-profile critics of Ferguson’s model and continues to track its abysmal performance over the last year.)

"Although Ahmed’s batty narrative about the GBD’s origins does not withstand even minimal scrutiny, his conspiracy theories spread like wildfire on the pro-lockdown side of the epidemiology profession, and among the journalistic outlets that support them.... Paul Krugman ... promoted the product of Ahmed’s ravings.... Several of the most widely-quoted critics of the Great Barrington Declaration in the press seized on the same narrative and began repeating tall tales about nonexistent funding sources and the wholly imaginary British Ministry of Defense website scheme. Some even added new fringe theories of their own.... 

"David Gorski, a professor of medicine at Wayne State University and one of the media’s favorite go-to sources for quotes denouncing the GBD, published a blog post on October 12th where he liberally quoted and endorsed Ahmed’s conspiracy theories.... Gorski appended his own paranoid attack by branding the GBD a 'eugenics-adjacent' plot to cull and 'sacrific[e] the elderly' in the name of economics.... Gorski’s attacks are not only symptomatic of a deeply disturbed state of mind – they’re unbecoming of a scientific professional, let alone one that the media enlists for expert quotations as a primary interlocutor of the GBD.

"So did Eric Feigl-Ding*, one of social media’s most aggressive promoters of school closures and the fringe 'Zero Covid' theory. Deepti Gurdasani* of Queen Mary University in London, a principal organizer of the pro-lockdown John Snow Memorandum, similarly promoted the Byline Times conspiracy theories about the GBD’s funding. As did Gabriel Scally*, a UK-based epidemiologist who serves the pro-lockdown 'Independent SAGE' group. David Fisman*, a Canadian epidemiologist who aggressively pushed for lockdowns and school closures in Ontario, is another fan of Ahmed’s conspiracy theories....

"Justin Feldman*, a self-described 'epidemiologist of social inequality' at Harvard, ... has published a nonstop stream of frenzied allegations against lockdown critics in the public health profession, usually consisting of unsubstantiated innuendo about shady pecuniary motives behind their scholarship.

"Duke University epidemiologist Gavin Yamey* offered a 'huge shoutout to Dr. Nafeez Ahmed' for supposedly uncovering the bizarre conspiracy linking the GBD website to the British Ministry of Defense and the Welsh hotel proprietor. Elsewhere he praised the Byline Times’s 'great investigative journalism' about the GBD. Half a year later he still asserts that 'Charles Koch shaped [pandemic] policy in the US' through a group of scientists who have no tangible connection to Koch’s philanthropy.

"Columbia University virologist Angela Rasmussen*, another frequent critic of the GBD in the press, embraced the funding conspiracy theory ... accusing the three scientists of being part of a 'propaganda campaign' in the service of AIER’s supposed goal of 'ignore the pandemic, let’s get back to making money via unfettered capitalism.'

"Martin McKee*, a public health professor at the London School of Hygiene and Tropical Medicine who denounced the GBD as a 'fringe view' shortly after its publication, has a habit of giving his endorsement to Ahmed’s conspiracy theories about the very same document. Ahmed’s crazy tales of intrigue have another fan in Robert Dickinson*, a professor of medicine at Imperial College London and signer of the pro-lockdown John Snow Memorandum. Snow Memorandum signer Hisham Ziauddeen* promotes the same conspiratorial claims, in addition to his own blogging against the GBD for Ahmed’s outlet. Epidemiologist Gregg Gonsalves of Yale, another of the media’s favorite sources for ad hominem-laced anti-GBD hot takes, apparently concurs with Ahmed’s paranoid ravings. So does the University of Washington’s Carl Bergstrom*, another press favorite for expert statements defending lockdowns. 

"Keep in mind that these endorsements of Ahmed involve claims that are not simply dubious or uncharitable interpretations – they are factual falsehoods that have entered the talking points of scientific experts who simply agree with their associated political connotations and believe that repeating them enough will discredit an opposing viewpoint. As matters of scientific analysis though, it would not be inaccurate at this point to state that leading academics on the pro-lockdown side of the Covid political debate are now regularly relying upon the paranoid ravings of a conspiracist blogger as one of their primary sources for attacks upon the Great Barrington Declaration."

Read more: https://www.aier.org/article/quacks-in-the-ivory-tower-how-conspiracy-theorizing-took-over-lockdown-science/

This work is licensed under a Creative Commons Attribution 4.0 International License.

* - signatories of the John Snow Memorandum

UK Freedom Day delay based on obsolete data

Data used by Boris Johnson to delay Freedom Day 'was out of date and underestimated the effectiveness of vaccines' against Indian variant, MPs are told | Daily Mail - Shaun Wooler & Connor Boyd:

June 16, 2021 - "Boris Johnson delayed lockdown based on outdated modelling that is likely to have exaggerated the impact of ending restrictions, MPs have heard.

"Under the most pessimistic scenario, Imperial College London warned Britain could experience 203,824 more deaths by next June. Even more optimistic forecasts from other universities suggested more than 50,000 would die. But the figures were based on old estimates of jab effectiveness, which assumed they would work less well, the Commons science and technology committee heard yesterday.

"Imperial worked on the basis that the AstraZeneca jab would reduce hospitalisations by between 77 and 87 per cent after two doses. But real-world data from Public Health England shows the vaccine is 92 per cent effective at preventing hospitalisation. The power of the Pfizer jab was also underplayed by several university groups.

"It means the modelling considered by the Scientific Advisory Group for Emergencies and the Prime Minister overestimates the likely number of deaths. Readjusting the models could see Imperial's death estimates fall from 203,824 to 26,854. 

"The PHE figures were made public on Monday, after the modelling to support delaying June 21 – but Dr Susan Hopkins, deputy director of PHE's national infection service, told MPs the Government was aware of the figures since last Friday. Tory MP Aaron Bell told the committee: 'The models that we seem to be relying on to justify the extension of restrictions don't appear to be using [the PHE] numbers'.... 

"Latest analysis by Public Health England estimates that Pfizer's vaccine slashes the risk of being hospitalised by the Indian variant by 96 per cent after two doses and AstraZeneca's jab cuts it by 92 per cent.... PHE's study of 14,000 cases of the Indian 'Delta' variant also found a single dose of either vaccine provides roughly 75 per cent protection against being admitted to hospital three weeks after getting the jab. It suggests the vaccines work just as well at reducing Covid hospital rates from the Indian variant – which spooked ministers into delaying the final easing of lockdown – as they do against the previously dominant Kent version.... 

"Officials had always believed the jabs would work well against the variant but there were growing doubts after multiple studies found it made vaccines significantly weaker at preventing infections.... This, combined with the fact the Indian variant is around 60 per cent more transmissible than the Kent version, has led to England's June 21 Freedom Day being delayed by a month.  

"SAGE warned there could be up to 500 Covid deaths a day if Boris Johnson were to press ahead with next week's unlocking. But the group's models were based on older, more pessimistic data about the Indian variant."

Read more: https://www.dailymail.co.uk/news/article-9695199/Data-used-Boris-Johnson-delay-Freedom-Day-date-MPs-told.html

Modelling the effect of lockdowns

Covid and the lockdown effect: a look at the evidence | The Spectator - Simon Wood: 

April 14, 2021 - "What forces Covid into reverse? To many, the obvious answer is lockdown.... It’s often said that all else failed. The Prime Minister said on Tuesday that lockdown, far more than vaccines, explains the fall in hospitalisations, deaths and infections. But how sure are we that only lockdown caused these falls — in the first, second and third wave? Or were other interventions, plus people’s spontaneous reactions to rising cases, enough to get R below one?...

"Let’s start with the events of last March. Imperial’s Neil Ferguson, whose modelling inspired the government’s decision to go into lockdown in March, told MPs in June that ‘had we introduced lockdown measures a week earlier, we would have reduced the final death toll by at least a half’. Last December, a paper called Report 41 was published by Imperial College along these lines. It said: 'Among control measures implemented, only national lockdown brought the reproduction number [the "R number"] below 1 consistently. Introduced one week earlier, it could have reduced first wave deaths from 36,700 to 15,700'....

"Ferguson’s Imperial team had developed a simple model of the spread of Covid-19 and fitted it to data on the daily deaths ... to tell them how, when and by how much the R number changed.... [T]he Imperial team’s model assumed that they knew when and how R was changing. They only needed the data to tell them by how much it changed. I repeated Imperial’s analysis, but with one important difference: the data were used to also determine when and how R changed. The Imperial model then gives a very different result. It suggests that R was already below 1 before lockdown. If that is the case then, rather than surging, new infections were already in decline.... I also used a different approach, bypassing the Imperial model altogether, to directly estimate the daily number of new fatal infections from the data on daily deaths and fatal disease duration. This direct approach also strongly suggests that infections were in substantial decline before lockdown, and that R was already below one. The graph below shows what this second approach found around the time of the first lockdown.

"The same approach can be used again at the second and third lockdowns. Before the second lockdown it was argued that the tier system was ineffective and that cases were surging. But the reconstructions suggest that fatal infections — and by implication Covid infections generally — were not surging. They were in decline having peaked earlier. The third peak is between Christmas and New Year.... It is possibly worth noting that although the estimated fatal infections were in retreat before each lockdown, the daily deaths were surging each time that a lockdown was called. The psychological pressure that this puts on the decision makers is obvious....

"This brings us back to Imperial’s Report 41.... With professor Ernst Wit of Università della Svizzera Italiana in Switzerland, I repeated the Report 41 analysis as reported in a pre-print (a not yet peer-reviewed study) on medRxiv. The Report 41 assumptions around the first lockdown are even more restrictive than in Imperial’s earlier study, and we again replaced them with an approach that allows the data to tell us when and how R changed, as well as by how much. Because far more data are involved this time, the scope for our own assumptions to bias our results is lessened, but we nonetheless took an approach designed to minimise such problems.

"As we went through Report 41 we also noticed some unusual things: Imperial’s model was using key input measures that were shorter than the times given in the published papers cited as sources.

• The time Imperial used from infection to symptoms (the ‘incubation period’). It’s stated as 4.6 days, citing Lauer et al. But that study says ‘the estimated mean incubation period was 5.5 days.’ A careful subsequent analysis by McAloon et al combining several studies, including Lauer et al, gives 5.8 days.
• The time Imperial gave from symptoms to hospitalisation. Imperial give a mean of 4 days, citing Docherty et al. But that paper gives 4 days as the median time. For the model they use, the corresponding mean days from onset to hospital is 6, not 4.

These two changes subtract three days from the model time between Covid infection and hospitalisation, compared to the values given in the cited literature. This is not a small issue if so much is to be made about every day mattering. Two other points from Imperial’s Report 41 were troubling:

• The model structure forced the average time from infection to infection to be quite a bit longer than the times reported in the literature.
• The model-fitting appeared to be set up in a way that attributed unusually low weight to the actual data, relative to assumptions built into the model.

"We tried to correct each of these four issues. The resulting model and analysis are very far from perfect, but we think that the results can give a somewhat more accurate picture of what the data imply than the original. Below is the picture we got for infections, by region and in total, around the time of first lockdown. Again the results imply that infections were in retreat before lockdown was called....

"So even taking the most negative view of our work, and the most positive view of the Imperial study, it is hard to see the latter as providing robust evidence for lockdown having caused R to drop below 1. Let alone as providing a reasonable basis on which to compute the number of lives that an earlier lockdown might have saved.  Even if our study’s assumptions are no better than Imperial’s, just different (which we would dispute), we have clearly shown that the Report 41 results are too strongly dependent on the model assumptions to provide reasonable evidence for the life-saving potential of earlier lockdowns claimed in the press."

Read more: https://www.spectator.co.uk/article/covid-and-the-lockdown-effect-a-look-at-the-evidence

Read study here: https://onlinelibrary.wiley.com/doi/10.1111/biom.13462

The importance of Sweden

The importance of Sweden

by George J. Dance

"One baffling feature of 2020," British Tory MP Neil O'Brien recently wrote in the New Statesman, "was that so much energy was wasted puffing up Sweden." Over at The Guardian, OpenDemocracy editor Peter Geoghegan echoed: "why, as the UK experienced one of the world’s worst Covid outbreaks, did so many prominent public figures spend so much of 2020 talking about Sweden?" 

I cannot say for sure why they talked about it (and I suspect neither writer wants an answer anyway), but I can explain why I keep writing about it. (Full disclosure: I have written seven articles on the country.) 

First, Sweden notoriously did not have a lockdown in 2020. Lockdowns were an unprecedented experiment in totalitarianism, justifiable only if they were the only way to prevent mass death. Whether lockdowns can be justified, then, depends on the factual question of whether they prevented mass death; which can only be answered by comparing real-world outcomes between countries that locked down and those that did not. Outlier nations like Sweden that remained open act as controls in the real-world lockdown experiment; the question to be asked about them being, how well did they do versus the closed societies? Without those real-world examples to give us real facts, we would have only guesses embodied in models. 

The original lockdowner prediction, from a model, was that Sweden's health care system would collapse, and it would suffer ~96,000 deaths by May. Contrary to an internet rumor, that modelling was not done by Neil Ferguson and Imperial College; it did, though, borrow its assumptions from Ferguson's infamous March 16 Imperial death model report, which forecast 200,000 British deaths under mitigation (also by May) versus just 20,000 with suppression. All three of the above forecasts were wildly wrong – one would say, comically wrong, if there were anything comical about human death – hence the need for real-world comparisons like the one Sweden offered to the locked-down countries of Europe. If every government had been pushed into locking down, there would be no factual basis for comparison, and we would have to rely on the guesswork of specialists. 

The British government opted in March for a Swedish-style mitigation strategy, but was forced to pull a U-Turn a week later in the face of widespread media and public criticism (including a nasty Twitter campaign accusing Boris Johnson of genocide). Britain was locked down on March 23; that very same day, a similar media campaign began against Sweden. The past 40 weeks since have seen a steady stream of articles – more than five every week – pronouncing the failure of the Swedish "experiment" [sic], like a stopped clock. (See my bibliography, "Sweden-bashing in the media".) The intent may be to convince the world that mass death has occurred in Sweden, or to panic the Swedes into their own U-Turn; either could be used as a victory for lockdowners in their propaganda war.

That brings me to my second reason for writing on the topic: many of those articles are pure propaganda, full of out-of-context claims, cherry-picked facts and outright misrepresentations that call out for rebuttal. On the other hand, even a stopped clock can be right; such articles should be checked not only for what may be wrong in them, but for what may be right.      

Admittedly, I sometimes get tired of writing in the genre. Over and over I find myself reading the same assertions I have already dealt with, and writing the same things in reply. This writing is not a creative act, but simple drudge work I sometimes wish someone else would do. Balancing that off, though, is my third reason for writing them: my articles have been somewhat popular, one getting a satisfactory 2,000 reads. So it is once more into the breach, to deal with these two latest examples. 

Geohegan's article is actually about a shadowy conspiracy involving "backbench Tory MPs, anonymously funded thinktanks and ubiquitous media commentators" that oppose lockdowns, so he deals with the alleged failure almost in passing: 

We now know with certainty what public health experts have long predicted: a light-touch coronavirus approach does not work. Sweden has recorded far higher death rates than its Nordic neighbours, while suffering a similar economic hit. Even the country’s king thinks it has 'failed'."

The first sentence is recycled claims I have dealt with before (and deal with again, later on), but the second is new. Geohegan seems to be implying that the King of Sweden was calling the country's 'light-touch' strategy a failure. Where did he get that idea? Probably from an earlier Guardian story, "King of Sweden blasts country's 'failed' coronavirus response," which declared in the subhead that the royal "Criticism of anti-lockdown stance comes as hospitals struggle to cope with surge in cases". Reading down, though, the story clarifies that that was not the king's message: 

The comments were initially taken as a criticism of Sweden’s controversial anti-lockdown strategy, but the royal court later said the king was referring 'to the whole of Sweden and the whole society. He is showing empathy for all those affected.' 

I am glad The Guardian clarified that point, though it is a pity that clarification did not make it into the headline. 'Tis a pity, as well, that Guardian reporters do not read the articles in their paper.

Sweden did have obvious failures, and the king deserves credit for acknowledging them. The most glaring were the failure early on to shield senior citizens (particularly those in care homes) and immigrants, two groups that suffered disproportionately. That has little if anything to do with lockdowns, though, as the same failures occurred in countries that did close down. So much for his majesty's comments. 

O'Brien's article, on the other hand, makes several criticisms that need a deeper dive. So let me turn to those, giving his points followed by my responses.  

Recent days have seen Sweden’s Nordic neighbours Finland and Norway offering emergency medical assistance as Stockholm’s hospitals have been overwhelmed, infections and deaths have spiked dramatically upward, and the King of Sweden has made an unprecedented criticism of the government’s bungled strategy.

None of which is an argument for Sweden bungling into a lockdown strategy strategy instead. It would be if the European countries that locked down had managed to avoid a second wave, or at least had  less deaths from it; but neither of those are the case. Infections and deaths have been "spiking" since November all over Europe, and barring a vaccine miracle they will continue to do so until spring. In the process, one locked-down country after another has been recording a worse outcome, in terms of deaths per capita, than Sweden. None of which matters to O'Brien, who recycles just one cherry-picked comparison (that I last saw reused by Dominic Lawson in a recent Sunday Times): 

Unprecedented, but hardly surprising: Sweden has suffered a death rate that is roughly ten times that of neighbouring Norway and nine times that of Finland. 

As with Lawson, O'Brien makes no attempt to look for reasons to explain the difference; the reader is left to infer that those countries' lower death rates must be due to lockdowns (although neither Norway nor Finland had a fall lockdown, and Norway arguably had no spring lockdown, either). O'Brien also makes no attempt to deal with, explain, or even mention the larger European context.

At the beginning of autumn, Sweden had the fifth highest death rate per capita in Europe, behind only Belgium, Italy, the UK, and Spain; all of the last four had suffered more deaths while the country was locked down. By November, Sweden had fallen from #5 to #10, being passed in deaths per capita by France, North Macedonia, Bosnia and Hercegovina, Montenegro, and Czechia. In the following months it has since slipped further to #15, being overtaken in the body count by Bulgaria, Slovenia, Hungary, Croatia, and Switzerland. Switzerland has lockdowns on the state level only; the other 13 have all relied on national lockdowns, most of them at least twice.    

Graph courtesy CTV News

   

A searing government report concluded the state had failed to protect the vulnerable. Mats Persson, a former UK government adviser, said of his home country: 'For a social model largely designed around the state levelling the odds and caring for the vulnerable, this will leave a very difficult moral legacy.'

Yes, Sweden's biggest failure came in care homes, which were locked down (just as they were in Britain and elsewhere) with disastrous results. Perhaps the UK government and its 'advisers' should take the beams out of their own eyes before preaching 'morality' to countries with less deaths. 

O'Brien does seem aware that Sweden has had less deaths than the UK, though he never mentions it, as he comes up with a couple of reasons why he thinks that should be. 

To understand the magnitude of what’s gone wrong, it’s worth noting that Sweden started the pandemic with several huge advantages. First, it’s a far less urban nation than the UK, for example, and the virus spreads much more rapidly in dense, built-up areas. While the UK has 273 people per square kilometre, Sweden has just 25.

Perhaps O'Brien is not being misleading, but has simply been misled from seeing that "25" figure (as I have) out of context on the internet. In any case the figure is misleading, as Sweden's population is not spread out more or less uniformly across the whole country but concentrated in a few urban areas.  Norrland, which covers more than half the country, is almost uninhabited, with less than 5 people per square kilometre. On the other hand, the population density in urban areas, where most of the people live, is more than 200 times greater. Wikipedia, the likely original source for O'Brien's "25" figure, puts it in context by noting that "87% of the population live in urban areas, which cover 1.5% of the entire land area." That works out to an urban density of just over 1,330 people per square kilometre; but Wikipedia gives an even higher figure for the whole settled area: "The average population density is just over 25 people per km2 (65 per square mile), with 1,437 persons per km2 in localities (continuous settlement with at least 200 inhabitants)." 

Second, Sweden has the highest rate of people living alone in the world: 42.5 per cent of households are single people, compared to just 29.9 per cent in the UK. Obviously, it’s much easier for the virus to spread within the home, and places with large, multigenerational households suffer most.

This statistic, that also appeared in Lawson, is not wrong but is not relevant either. The places in Sweden where infections occurred most were care homes, then hospitals, and then new immigrants' households (which tend to be larger and more intergenerational than the national average). None of those could conceivably have been prevented by closing restaurants and bookstores. Confining the non-essential to their households by lockdown might have even boosted household deaths among the last group. 

O'Brien then compares Britain with an imaginary Britain – another comparison used by Lawson, and a favorite one for lockdown aficionados – which he combines with his earlier comparison into a logical pretzel:    

To form an idea of the consequences that would have followed if the UK had followed the Swedish model, you would need to compare Sweden’s outcomes to its similar neighbours. Given the country’s death rate is ten times higher, imagine the chaos we’d have seen if we had multiplied the UK death rate by a factor of ten.

This is not a sound argument, but a non sequitur. I suppose the best way to show the non-sequitur is to substitute terms: 

"To form an idea of the consequences that would have followed if Sweden had followed the British model, you would need to compare the UK's outcomes to its neighbors. Given the UK’s death rate is more than 2.5 times that of its neighbor Ireland, more than 3 times that of its neighbors the Channel Islands, and 4 times that of its neighbor the Isle of Man, imagine the chaos the Swedes would have seen if they had multiplied their death rate by a factor of 2.5 to 4." 

Nor has there been an economic upside for Sweden: in fact, they saw a bigger hit to their economy than their neighbours, as well as much worse health outcomes. 

O'Brien is relying on economic data from August, not to mention from a notorious Sweden-bashing site. In October, Danske Bank forecast that Sweden's economy would fall 3.3% in 2020 (versus 3.6% for Norway, 4.5% for Finland, and 8.3% for the locked-down euro zone).   In November, SEB Bank estimated Sweden's GDP hit at 3.1% (versus 1.9% for Norway, 4.0% for Finland, and 7.6% for the euro zone). In any case, actually calculating economic damage will require more than just looking at short-term changes in GDP, but also looking at things like bankruptcies, unemployment, crime, and suicide. The countries that locked down will be totting up the damage for years. 

Swedish virologist Lena Einhorn concluded: “Sweden’s strategy has proven to be a dramatic failure.” This matters, not only because health and lives are in danger, but also because the Swedish experiment reveals the failures of the underlying theories of Covid-sceptics. 

The Swedish experiment of mitigation has to date resulted in less loss of life than the British experiment of continual interventions, roughly 20% less. Sweden has had more confirmed cases – over 4% of the population versus 3.5% – but it is unclear what that means in terms of health outcomes, other than a lower case fatality rate. Pace Dr. Einhorn, there is no actual evidence here of failure; cases that result in recoveries rather than deaths are not failures.  

Sweden’s controversial state epidemiologist Anders Tegnell predicted in the summer that because the country had a high rate of infection in the spring, it would have “a high level of immunity and the number of cases [in the winter] will probably be quite low”.

We now know he was disastrously wrong. But he wasn’t alone; his theory was exactly the same as that still relied upon by the UK’s Covid-sceptics.

Tegnell admitted he was wrong about that prediction a month ago. It is a stretch to call that wrong prediction a "failure" – it was not a failure of the theory, but a matter of not having adequate data – and jumping the shark to call it "disastrous." (If O'Brien's is implying that Sweden did not prepare for a second wave, that implication is false.) Sweden's consequences to date have been less "disastrous" than Britain's, or those of 14 other European countries that have had lockdowns. Of course, there are also European countries that have locked down and had less deaths as well; but the 14 counterexamples to date are reason enough to not attribute the latter countries' fewer deaths to their lockdowns.  

Sunetra Gupta, a lead author of the “Great Barrington Declaration”, promised in May that, in the UK, “the epidemic has largely come and is on its way out in this country… due to the build-up of immunity”. One leading Covid-sceptic, Michael Yeadon, wrote that thanks to “prior immunity”, “the pandemic is effectively over.”

It’s these same failed theories that still lead Covid-sceptics to argue it is safe to let the virus rip 

What exactly are "Covid-sceptics"? Does O'Brien mean "lockdown sceptics", as in his title, or "Covid deniers"? If the latter, he is attacking a strawman, and a rather dimwitted one at that: someone who doubted there was a virus would have a hard time believing that a person could develop antibodies specific to it. If the former, O'Brien is still wrong. The sceptic case against lockdowns is that their economic damage has been underestimated, while their value in saving lives has not been demonstrated. All that "immunity" adds to the discussion, for me, is the observation that the coronavirus does not "rip" (a word that sounds like what a Covid Cultist would say); on the contrary, community outbreaks can be expected to slow down with the buildup of immunity as people in the community catch it and recover. 

and attempt to shelter the old and vulnerable. 

Those are the groups that have taken the most casualties. Anyone who is serious about reducing deaths from Covid-19 should be concerned with trying to shield them; even politicians who locked down their states have paid lip service to the concept. I do not see any reason for that to be controversial at all.  

One baffling feature of 2020 was that so much energy was wasted puffing up Sweden, while countries such as Australia, Japan, South Korea, Taiwan and New Zealand revealed to us genuinely successful models based on hard suppression of the virus with decisive action: all suffered a fraction of the European average rate of coronavirus cases.

Fine; let us briefly look those countries. All but Japan are effectively small, isolated islands (South Korea's only land border is impassable), which were able to eliminate the virus by using what I've called a 'NIMBY strategy': quarantining everyone entering the country and the relatively few cases found or traced in the community. Iceland (followed by Norway, and Finland) used the same strategy successfully in Europe. In no case was a lockdown needed (although Australia and New Zealand did have a couple, possibly for the political theatre). It is hard to see how that strategy could be made to work in the European Union. I do not think anyone has yet figured out exactly what Japan did, but we do know that it has not included wholesale lockdowns or business shutdowns either. 

There is, of course, no scope for triumphalism here. 

Of course not; nor any grounds for it, either. If Sweden does end up with far more deaths than any of the locked-down countries - say, the 66,000 that Imperial College estimated would have happened by May without NPIs - that would be something for lockdown champions to gloat about. But Sweden sitting in the middle of a pack of lockdown countries, the latter all markedly different in their mortality rates (by as much as 0.2% of the whole population) does not even indicate a correlation between locking down and reducing deaths. 

Things can always change. The pandemic looks likely to continue until spring; Sweden could always have an even greater winter surge, followed by overwhelmed hospitals and mass deaths. On the other hand, that can happen in any of the locked-down countries as well. One can only wait and see. I am sure this is not the last time I will be writing about the subject. 

In search of exponential growth

by George J. Dance

Imagine an invasive species of water lily that takes over a pond, choking off all other life; it grows exponentially, doubling its area every day. In 60 days the pond is completely full of lilies. When was the pond half full? When was the pond 1% full?

I expect most readers got the first answer: The pond was half full on day 59. The second is a bit trickier, and I found it much harder to estimate. It looks intuitive that if the pond is full in two months, it must have been at least 1% full after one month. However, that intuition does not account for the phenomenon of exponential growth. If the pond was full on day 60, then it was half full on day 59 (as you figured out), 1/4 full on day 58, 1/8 full on day 57, and so on. On day 53, the pond was only 1/128 full of lilies; so it was not 1% full until day 54. For more than seven weeks, there was nothing to see; then suddenly, in less than a week, the infestation had ripped through the pond killing everything in its path.

I read that story on the web in March. It was meant to illustrate the scary potential of the novel coronavirus, which at the time appeared to be growing exponentially. The warning was clear: even though, outside of New York, the virus was not even noticeable on this side of the ocean, it was only a matter of time before we had as many sick and dead as New York. The numbers got even scarier when one projected exponential growth into the future: epidemiologists at the Imperial College of London calculated that, by August 2020, 250 million Americans would be sick and 2.2 million would die, most in the last month.

Imperial College's report panicked Britain and the United States into lockdowns; and it was only lockdowns, we are told, that prevented its nightmare scenario from coming true: Without lockdowns, those countries would have suffered exponential growth and almost 3 million dead. The conclusion was inescapable: if those countries abandon lockdown, they once again face exponential growth and close to 3 million dead.

However, American state lockdowns have proved to be unsustainable. While still hugely popular with voters, they are being increasingly defied and ignored; and state governments (with a few exceptions like California) have found it necessary to lift or at least relax them. Since then the virus has resurged, and if the lockdown theory is right we can expect it to now resume exponential growth. Which is exactly why it is important to establish whether the virus is and/or will grow exponentially, anywhere.

Last week I looked at the numbers for the United States as a whole. I found that, while the virus grew exponentially in March (when total numbers were small), by mid-April (with 43 American states under lockdown) growth had become linear instead; the virus still spread, but at a steady rate. Even more encouragingly, after states began lifting and easing lockdowns, growth stayed linear.

One helpful comment I received on that column suggested I look at the Republican states that have had newsworthy recent surges: Arizona, Florida, Georgia, and Texas. All four states have Republican governors, who have drawn extensive criticism, and lost some support with voters, for easing lockdowns already. If exponential growth were re-occurring anywhere, surely it would be there. So, leaving Georgia for another time, I did a similar dive into the numbers for those states, again using Worldometer's "United States" coronavirus pages for data. In addition, I used the numbers for California (a state that has has maintained a strick lockdown, but has also seen a surge) as a control. My tabulations appear at the end.

For the three "Republican" states, I found that while cases have risen dramatically in all of them, growth in all cases has stayed linear. Both Florida and Texas have seen a big surge in cases, to more than 10,000 a day; however, neither state's figures show the constant doubling that indicates exponential growth. Florida went from 50,000 cases to 100,000 in 30 days; from 100,000 to 200,000 in 13 days; from 150,000 to 300,000 in 15 days; and from 200,000 to 400,000 in 19 days - a big speedup, followed by a slowdown. For Texas, the comparable time intervals were 30, 18, 18, and 21 days. Arizona's cases doubled from 20,000 to 40,000 in 15 days; from 40,000 to 80,000 in 14; from 60,000 to 120,000 in 20; and from 80,000 to 160,000 in 25 days - a consistenty slower growth.

It was otherwise with California, the lockdown state. California, with a larger population than either Florida or Texas, has only a slightly higher number of cases than either; and while its death total is more than double that of either, it per capita death rate is below that of both Florida and Arizona. California's numbers seem low; but then, so did the number of lilies on the pond back on day 30. Given exponential growth, low numbers early on mean little.

And in California, exponential growth may be occurring. The state's caseload doubled from 50,000 to 100,000 in 26 days; from 100,000 to 200,000 in 29 days; from 150,000 to 300,000 in 26 days; and from 200,000 to 400,000 in 26 days. That steady increase, though happening slowly, looks like constant doubling and therefore exponential growth  While it is too early to say for sure (and the pattern may change at any time), California could be in serious trouble by the fall.

California was the earliest state to lock down, on March 19. The state's strict lockdown has already been lauded by at least one academic study, a National Bureau of Economic Research paper published in April, which concluded that the lockdown had prevented "as many as 1,661" deaths from COVID-19 in its first month. One can only hope that those, and subsequent months', prevented deaths in the state remain prevented..   

===Arizona===
Deaths (time to add 500)
Jun. 5 - 1,012 (28 days)
Jun 24 - 1,490 (19 days)
Jul. 9 - 2,038 (15 days)
Jul 16 - 2,492 (7 days)
Jul 22 - 2,974 (6 days)

Cases  (time to add 20,000)
Jun. 1 - 20,123
Jun 16 - 39,097 (15 days)
Jun 24 - 59,974 (8 days)
Jun 30 - 79,215 (6 days)
Jul. 5 - 98,089 (5 days)
Jul 11 - 119,930 (6 days)
Jul 17 - 138,523 (6 days)
Jul 25 - 160,041 (8 days)

===California===
Deaths (time to add 1,000)
Apr 17 - 1,041
Apr 30 - 2,029 (13 days)
May 13 - 2,966 (13 days)
May 28 - 4,039 (15 days)
Jun 12 - 4,988 (15 days)
Jun 29 - 5,976 (17 days)
Jul 11 - 7,026 (12 days)
Jul 22 - 8,045 (11 days)

Cases (time to add 50,000)
Apr 30 - 50,347
May 26 - 100,208 (26 days)
Jun 13 - 150,921 (18 days)
Jun 24 - 194,415 (11 days)
Jul. 2 - 249,094 (8 days)
Jul. 9 - 303,323 (7 days)
Jul 15 - 355,285 (6 days)
Jul 20 - 399,898 (5 days)
Jul 25 - 453,121 (5 days)

===Florida===
Deaths (time to add 1,000)
Apr 23 - 987
May 18 - 1,997 (25 days)
Jun 16 - 2,996 (19 days)
Jul. 9 - 4,009 (23 days)
Jul 19 - 4,985 (10 days)
Jul 27 - 5,933 (8 days)

Cases (time to add 50,000)
May 23 - 50,127
Jun 22 - 100,217 (30 days)
Jun 30 - 152,434 (8 days)
July 5 - 200,111 (5 days)
Jul 10 - 244,151 (5 days)
Jul 15 - 301,180 (5 days)
Jul 19 - 350,047 (4 days)
Jul 24 - 402,312 (5 days)

===Texas===
Deaths (time to add 1,000)
May 6, - 1,006
Jun 14 - 1,996 (29 days)
Jul. 9 - 3,052 (25 days)
Jul 18 - 4,007 (9 days)
Jul 25 - 5,069 (7 days)

Cases (time to add 50,000)
May 18 - 49, 684
Jun 17 - 99,304 (30 days)
Jun 27 - 148,845 (10 days)
Jul. 5 - 202,146 (8 days)
Jul 10 - 254,319 (5 days)
Jul 15 - 302,817 (5 days)
Jul 20 - 347,135 (5 days)
Jul 26 - 397,992 (6 days)

The story of the exponentially growing virus

by George J. Dance

I have to admit that I am no statistician; rows of numbers usually bore me. By April, though, I figured that if I was to understand the coronavirus event, I had to learn enough to be able to read the numbers and get an idea of what they were saying. So I began looking at the statistics, and asking questions on social media.

The first thing I ran into was the assertion that the virus grew "exponentially". Unlike linear growth (at which something grows at a constant rate) exponential growth meant that, the more the virus grew, the faster it would grow, with infections and deaths both doubling at a constant rate. People were pushing this idea all over social media, of course, but in more academically respectable venues as well. For example, the infamous Imperial College Report 9 by Neil Ferguson et al modelled "an exponentially growing rate (with a doubling time of 5 days)," to forecast almost 250 million infections and over 2.2 million deaths in the United States by August.

Ferguson's predictions were instrumental in pushing both the U.S. and Britain into lockdown; and although they never came true, that had a simple explanation: the lockdowns save us. Consequently, the assumption of exponential growth has gone unchallenged; rather than study the virus's growth in real time, these days scientists are more interested in studying why "American participants mistakenly perceive the virus’s exponential growth in linear terms (conservatives more so than liberals)."

With the ending of lockdowns in some states, their easing in others, and the people increasingly ignoring them in others, many fear that the virus is again spreading exponentially. Not just in the United States; the mayor of Calgary, Alberta, announced today that Calgary is experiencing exponential growth. That got me concerned enough to finally look at the numbers.

I began with a quick look at doubling times of deaths in the United States, to see if there were any change. Thanks to extensive media coverage, it is easy enough to google the day of any number of deaths in the country; so I did a few quick searches for doubled values, and used the dates of the newspaper stories. Here are the results: 

Deaths - exponential (time to double)
2,500 - Mar. 30
5,000 -  Apr. 1 (2 days)
10,000 - Apr. 6 (5 days)
20,000 - Apr 11 (5 days)
40,000 - Apr 20 (9 days)
80,000 - May 11 (21 days)
160,000 -

That told me the disease was spreading exponentially in March and (though slower) in early April. However, after mid-April, the time it took to double increased: to twice as long, then four times as long. Then, in May, deaths stopped doubling. So I decided to check whether growth had gone linear instead. Using the same method as above, I began checking the time interval to add a further 10,000 deaths (the amount of the last true doubling).

Deaths  - linear (time to increase by 10,000)
10,000 - Apr. 6
20,000 - Apr 11 (5 days)
30,000 - Apr 15 (4 days)
40,000 - Apr 20 (5 days)
50,000 - Apr 24 (4 days)
60,000 - Apr 29 (5 days)
70,000 - May 5 (7 days)
80,000 - May 11 (6 days)
90,000 - May 17 (6 days)
100,000 - May 28 (11 days)
110,000 - June 6 (9 days)
120.000 - Jun 21 (15 days)
130,000 - July 6  (15 days)
140,000 - Jul 19 (13 days)

That was more informative. As I suspected, the virus continued to spread, in a linear fashion, through the lockdown period.  The surprising thing is that even after some states began ending lockdowns, deaths continued to grow in a linear fashion, and at an even lower rate. The recent shortening of the increase time, though, shows that the death rate is trending up again, and this may indicate the onset of exponential growth; it is too soon to say anything.

So I decided to check confirmed cases as well. Cases give a less reliable measure of the disease due to under- and over-counting, as well as testing capacity and differing test specificities; however, they give earlier information, which can indicate what the death toll will look like in the future. This time I used a more precise data source, the figures provided on the Worldometer site (a great source of data). Again I checked time to double for the earlier period:

Cases - exponential (time to double)
Feb. 15 - 15
Feb. 20 - 35 (5 days)
Feb. 27 - 60 (7 days)
Mar. 3 -  124 (5 days)
Mar. 6 -  319 (3 days)
Mar. 8 - 540  (2 days)
Mar. 10 - 994 (2 days)
Mar. 13 - 2,185 (3 days)
Mar. 16 - 4,611 (3 days)
Mar. 18 - 9,333 (2 days)
Mar. 20 - 19,608 (2 days)
Mar, 23 - 44,325 (2 days)
Mar. 26 - 86,669 (3 days)
Mar. 31 - 194,114 (3 days)
Apr. 7 - 411,116 (7 days)

When, once again, the time needed to double itself began doubling, I assumed linear growth, and began recording the time it took to add another 100,000 infections:

Cases - linear (time to increase by 100,000)
Mar 31 - 194,114 (3 days)
Apr. 4 - 319,444 (4 days)
Apr. 7 - 411,116 (3 days)
Apr 10 - 512,010 (3 days)
Apr 13 - 597,642 (3 days)
Apr 16 - 685,712 (3 days)
Apr 20 - 801,820 (4 days)
Apr 23 - 890,436 (3 days)
Apr 26 - 991,670 (3 days)
Apr 30 - 1,100,105 (4 days)
May  3 - 1,193,573 (3 days)
May 7  - 1,298,462 (4 days)
May 11 - 1,393,513 (4 days)
May 15 - 1,494,646 (4 days)
May 20 - 1,606.295 (5 days)
May 24 - 1,701,574 (4 days)
May 29 - 1,810,430 (5 days)
Jun. 2 - 1,899,666 (4 days)
Jun. 7 - 2,010,569 (5 days)
Jun 11 - 2,093,253 (4 days)
Jun 16 - 2,212,264 (4 days)
Jun 19 - 2,300,667 (3 days)
Jun 22 - 2,391,754 (3 days)
Jun 25 - 2,506,490 (3 days)
Jun 27 - 2,597,454 (2 days)
Jun 29 - 2,682,781 (2 days)
Jul. 1 - 2,781,217 (2 days)
Jul. 3 - 2,898,358 (2 days)
Jul. 5 - 2,994,393 (2 days)
Jul. 7 - 3,101,020 (2 days)
Jul. 9 - 3,224,892 (2 days)
Jul 10 - 3,297,170 (1 day)
Jul 12 - 3,417,795 (2 days)
Jul 13 - 3,483,584 (1 day)
Jul 15 - 3,621,637 (2 days)
Jul 16 - 3,695,025 (1 day)
Jul 17 - 3,770,012 (1 day)
Jul 19 - 3,898,550 (2 days)
Jul 21 - 4,028,569 (2 days)

The first thing I noticed is that cases are rising much faster than deaths – a good sign, as it means that the death rate is steadily decreasing. The second is that, while the amounts differ, both cases and deaths obey the same trajectory: increasing exponentially at the beginning, slowing to linear growth during lockdown, and then slowing even more before rising again later in the summer. The third is that cases and deaths change trajectory in lockstep, with deaths following cases by three weeks.

That allowed me to conclude that, though deaths will increase through mid-August, their growth will stay linear for now. "For now" is important; with both cases and deaths increasing, there is no guarantee that we will not suddenly be witnessing exponential growth. Although I found no evidence of exponential growth after the very earliest stage, that does not prove it cannot happen.

In addition, I realize that my conclusions could be wrong; as I said, I have not studied statistics, and I could very well be overlooking something. Which is why I rejected my earlier title for this piece, "The myth of the exponentially growing virus" - while my research shows no evidence for exponential growth, I cannot conclude at this time that exponential growth is only a myth.

Accordingly I welcome detailed, informative criticism of my observations. In addition, I intend to keep these tables updated,, and most likely do a few for other geographic areas.

Investigating Sweden's coronavirus response

by George J. Dance

Lockdown advocates hate Sweden, for good reason. Back in March, Europe (not to mention the rest of the world) seemed to face a choice between mass lockdown or mass death. Only Sweden and Britain resisted the pressure to confine their societies and shut down their economies (and British resistance crumbled in two weeks). Immediately Sweden was condemned for its risky "experiment" with its citizens' lives. (Never mind that nation-wide lockdown was actually the novel, untried experiment.)

Predictions were dire. Neil Ferguson of Imperial College estimated that 80,000 Swedes would die – and that was before the forecast collapse of the country's health care system (which was the real fear driving the lockdowns). Modellers who looked at the latter scenario found even more deaths, as in this study's summary:

Our model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). 

Now it is July, and there have not been 96,000 deaths from COVID-19 in Sweden – or 80,000 – or even the low-ball estimate of 52,000. There have been 5,400, in a population of 10 million. For comparison, Quebec has 5,500 COVID-19 deaths in a slightly smaller population (8.5 million). Nor, as the historical chart makes clear, is the country on track to reach 52,000 deaths anytime soon:

COVID-19 deaths in Sweden, 11/3/20 - 29/7/20 - courtesy statista.com

The mass death never happened. Yet that has not stopped the pro-lockdown media from pretending it has, giving us lurid stories about the "COVID-19 disaster" of Sweden's "shocking death toll," "the highest number of COVID-19 deaths per capita in Europe," even "one of the highest per-capita rates of coronavirus death in the world." And the false narrative continues: In its latest iteration, even the Swedes have recognized the danger of letting people work, shop, and visit friends. This week, Business Insider reported:

Sweden's prime minister orders an inquiry into the failure of the country's no-lockdown coronavirus strategy

• Sweden has launched an inquiry into its no-lockdown policy after thousands of coronavirus deaths in the country. 
• Sweden now has the fifth-highest per capita death rate in the world with a larger death toll than all of its neighbours' combined....

Sweden's prime minister has ordered an inquiry into the country's decision not to impose a coronavirus lockdown after the country suffered thousands more deaths than its closest neighbours.
"We have thousands of dead," Swedish prime minister Stefan Lofven said at a press conference on Wednesday, while admitting that the country's handling had exposed Sweden's "shortcomings," The Times of London reported.
"Now the question is how Sweden should change, not if." (stress added)

Notice that Lofven does not himself mention "lockdown" (despite the word's prominence in the article). There is a reason for that, though you have to read to the end of the article to discover it: he was not criticizing the country's "no-lockdown policy," but a different part of the national strategy:

The inquiry announced by Lofven will first consider why approximately half of Sweden's deaths have taken place in its care homes, The Times of London reported.
"We did not manage to protect the most vulnerable, the elderly, despite our best intentions," the prime minister said.

Precisely: Just like in Quebec (and Ontario, and many U.S. states), Sweden's policy failed to protect the vulnerable elderly in nursing and long-term-care homes. That failure was a tragedy and a scandal, but it cannot have been caused by the country's "no-lockdown policy," since the same failure occurred in multiple jurisdictions that did lock down.

That tragedy and scandal should be investigated, in Sweden and in those other jurisdictions; not to assign blame, but to figure out how to prevent such a tthing from ever happening again.  If the lockdown advocates are interested in helping in that goal, their assistance is welcome. But if they are only interested in propagandizing for their new pet social engineering scheme, thanks but no thanks.

3 problems with Imperial's 3 million deaths model

Comment on Flaxman et al. (2020, Nature, https://doi.org/10.1038/s41586-020-2405-7):
The illusory effects of non-pharmaceutical interventions on COVID-19 in Europe

Authors: Stefan Homburg, Leibniz University Hannover, Department of Public Finance, Germany

Christof Kuhbandner, University of Regensburg, Department of Human Sciences, Germany

Correspondence to: christof.kuhbandner@ur.de

June 17, 2020 - In a recent article, Flaxman et al.[1] allege that non-pharmaceutical interventions imposed by 11 European countries saved millions of lives. We show that their methods involve circular reasoning. The purported effects are pure artefacts, which contradict the data. Moreover, we demonstrate that the United Kingdom’s lockdown was both superfluous and ineffective.

A key concept in epidemiology is the effective reproduction number, R(t), where t denotes time. This function represents the expected number of infections generated by one infected individual. Ceteris paribus, the effective reproduction number starts at R(0), referred to as the basic reproduction number, and decreases monotonically. The monotonic decrease is due to the fact that the number of individuals susceptible to the infection but not yet infected declines as the virus spreads. Of course, the function R(t) can be influenced by non-pharmaceutical interventions (NPIs) as well as by voluntary behavioral changes. However, in case of a finite population, the effective reproduction number falls automatically and necessarily over time since the number of infections would otherwise diverge.

The model of Flaxman et al.1 contradicts this elementary insight. They estimate R(t) from daily deaths associated with SARS-CoV-2 using as an a priori restriction that R(t) may only change at those dates where interventions become effective. Such an approach does not prove that NPIs were effective but rather begs the result, i.e., involves circular logic. The true effective reproduction number declines continuously, and when its estimates are allowed to change only at intervention points, it is clear that profound discontinuities, which attribute strong effects to the interventions, will emerge. Flaxman et al. (p. 2) conclude that while most NPIs had unidentifiable effects, lockdowns reduced the reproduction numbers instantaneously by 82%.

Taking the United Kingdom as an example, Fig. 1 illustrates the ineffectiveness of social distancing etc. in the analysis of Flaxman et al. as well as the enormous effect of the lockdown from 23 March.

Figure 1: Estimate of the effective reproduction number by Flaxman et al. (Fig. 1). The authors assumed R(t) constant before 14 March and after 23 March. Changes were allowed only on the four dates [when] NPIs became effective.

Flaxman et al. (p. 2) infer their estimate of the basic reproduction number from the initial growth of reported daily deaths. Our Fig. 2 shows the actual growth of reported daily deaths. 3

Following the presumption of Flaxman et al. that deaths are more reliable than cases, we see growth of reported daily deaths as a good empirical proxy that mirrors the development of the effective reproduction rate. Of course, deaths follow infections after a long delay – a fact which is taken into account below.

Figure 2: Growth factor of daily deaths (Fig. 2). Source: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/891710/2020-06-11_COVID-19_UK_deaths_time_series.csv. Moving averages, 7 days. Retrieved 14 June 2020. Given daily deaths dt, growth factors were computed as dt/dt–1. Note that if daily deaths show exponential growth, any 52 moving average will also show exponential growth.

Disregarding noise in the data, the growth in daily deaths associated with the coronavirus declined steadily during March and April. Moreover, reported daily deaths follow infections with a median delay of 23 days, consisting of a 5 days incubation period[2] and a median delay of about 18 days from symptom onset to death[3]. Note that this delay also underlies the estimations by Flaxman et al. (p. 22 of their supplementary information).

 Considering a total delay of 23 days between infection and death, possible effects of the 23 March lockdown should only become visible in the data around April 15. However, the series does not show the slightest break in mid-April. Hitherto, the growth factor had already declined from 1.54 to 0.97, and thereafter it continued its slowdown. Contrary to the findings of Flaxman et al., Fig. 2 strongly suggests that the UK lockdown was both superfluous (it did not prevent an otherwise explosive behavior of the spread of the coronavirus) and ineffective (it did not slow down the death growth rate visibly).

The argument of a delay of 23 days between infection and death can also be used in the opposite direction. With the growth rate of daily corona deaths falling since mid March, the underlying growth rate of daily infections must have started receding in the second half of February, long before the problem was recognized and any measures were taken. The continuous decrease in the growth factor shown in Fig. 2, even at dates before any NPI could have become effective, corroborates the theoretical insight that R(t) falls automatically over time. We have checked that the growth factors in the remaining 10 countries considered by Flaxman et al. show a similar pattern.

Our final remark regards Sweden, the only country in the dataset that refrained from strong measures, but has lower corona deaths per capita than Belgium, Italy, Spain, or the United Kingdom. In the absence of a lockdown, but with an effective reproduction number that declined in the usual fashion, Flaxman et al. (Extended Data Fig. 1) attribute the sudden decline in Sweden’s R(t) on March 27 almost entirely to banning of public events, i.e., to a NPI that they found ineffective in all other countries. This inconsistency underlines our contention that the results of Flaxman et al. are artefacts of an inappropriate model.

References
1. Flaxmann et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature in press, 2020. https://doi.org/10.1038/s41586-020-2405-7.
2. Lauer, S. A. et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) from Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med in press, (2020). https://doi.org/10.7326/M20-0504.
3. Verity, R. et al. Estimates of the severity of COVID-19 disease. Lancet Infect Dis in press, (2020). https://doi.org/10.1016/S1473-3099(20)30243-7.

Author Information
The authors declare that they have no conflicts of interest. Correspondence should be addressed to C.K. (christof.kuhbandner@ur.de).

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.

"Lockdowns saved 3 million lives" claim not proven

Professor Lockdown Now Claims to Have Saved 3.1 Million Lives | American Institute for Economic Research - Phillip W. Magness:

June 9, 2020 - "The problem of causal inference presents one of the great challenges of empirical analysis. While it is relatively easy to find patterns in data that appear to move over time in response to overlapping events, it is much harder to show that those events specifically caused the data to move as expected. Think about how presidents often cite positive economic data such as GDP growth or the stock market as vindication of their own economic policies. Prior to early March 2020 this was a favorite tweeting topic of Donald Trump, although his predecessors almost all made similar claims....

"[C]onsider the ongoing question about the effectiveness of the COVID-19 lockdown policies employed in several U.S. states as well as other countries. A causal inference test of the lockdowns would require clear evidence of different outcomes between states that adopted shelter-in-place rules and states that did not. Given the complex multitude of confounding variables affecting COVID-19 transmission and mortality rates, isolating causality from the lockdown policies is no easy task.

"That brings us to a new report published in the journal Nature by the epidemiology team at Imperial College-London (ICL).... This is the same epidemiology research center whose agent-based simulation model convinced the American and British governments to switch to a lockdown strategy.... The paper [in Nature] and an accompanying press release from the university put numbers to this claim, asserting that the lockdowns saved an estimated 3.1 million lives in Europe.

"Although this headline-grabbing claim will likely be treated as a vindication of the lockdown approach by its political supporters, a closer look at the analysis suggests the Imperial College team reached this conclusion without offering a viable causal inference strategy. As they describe in the paper, 'By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted in our study period' [stress added - gd]....

"Put differently, the epidemiologists reached their estimates by taking the difference between observed deaths and their own agent-based simulation.... They then depict the difference as if it demonstrates the validity of their own simulation model, despite providing no evidence that their original simulation was correct....

"If that line of argument sounds familiar, it’s because Donald Trump beat the Imperial College team to the punch. Citing the now-infamous March 16th ICL report by Imperial’s Neil Ferguson, the American president now regularly claims vindication for his own support of the lockdowns on account of the difference between its 2 million-plus projected death toll and the actual count of just over 100,000 as of this writing. As Trump tweeted on May 26, 'For all of the political hacks out there, if I hadn’t done my job well, & early, we would have lost 1 1/2 to 2 Million People, as opposed to the 100,000 plus that looks like will be the number.'

"Whether used by Imperial College or Trump, this line of argument falters as social science because it assumes the validity of the very same forecast it purports to demonstrate. Rather than testing the causal inference that lockdowns reduced the COVID death rate, it takes [its] own forecasted death rate as a given and then purports to calculate the number of lives saved by simple subtraction from its own model....

"The new paper essentially acknowledges as much in noting that 'the counterfactual model without interventions is illustrative only and reflects our model assumptions.' Rather than investigating this seemingly-crucial assertion further, let alone subjecting it to empirical testing, the authors indulge in a handwaving exercise that simply declares: '[all scenarios broadly show the same trends]. Given this agreement in differing scenarios we believe our estimates for the counterfactual deaths averted to be plausible'....

"The result is not a valid exercise in social scientific analysis, nor is it even an empirically robust test of the ICL model’s performance. Like Trump’s tweeting about both the economy before March 2020 and his own claimed role in reducing COVID deaths after the lockdowns, it is an exercise in statistical astrology. Sadly, unlike Trump’s tweets, however, the ICL managed to convince Nature, a top journal in the profession, to publish these unfounded claims."

Read more: https://www.aier.org/article/professor-lockdown-now-claims-to-have-saved-3-1-million-lives/

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The man who forecast 2.2 million US deaths

When it came to dealing with an unexpected surge in infections and deaths from SARS-CoV-2 (the virus causing COVID-19 symptoms), federal and state policymakers understandably sought guidance from competing epidemiological computer models. On March 16, a 20-page report from Neil Ferguson's team at Imperial College London quickly gathered enormous attention by producing enormous death estimates. Dr. Ferguson had previously publicized almost equally sensational death estimates from mad cow disease, bird flu and swine flu.
 - Alan Reynolds, "How One Model Simulated 2.2 Million U.S. Deaths from COVID-19," Cato Institute, April 21, 2020 (stress added)

Six questions that Neil Ferguson should be asked | The Spectator - Steerpike:

April 27, 2020 - "Below are six questions Steerpike would like to see Neil Ferguson pressed on the next time he embarks on a media round:

"In 2005, Ferguson said that up to 200 million people could be killed from bird flu. He told the Guardian that ‘around 40 million people died in 1918 Spanish flu outbreak… There are six times more people on the planet now so you could scale it up to around 200 million people probably.’ In the end, only 282 people died worldwide from the disease between 2003 and 2009. How did he get this forecast so wrong?

"In 2009, Ferguson and his Imperial team predicted that swine flu had a case fatality rate 0.3 per cent to 1.5 per cent. His most likely estimate was that the mortality rate was 0.4 per cent. A government estimate, based on Ferguson’s advice, said a ‘reasonable worst-case scenario’ was that the disease would lead to 65,000 UK deaths. In the end swine flu killed 457 people in the UK and had a death rate of just 0.026 per cent in those infected. Why did the Imperial team overestimate the fatality of the disease?....

"In 2001 the Imperial team produced modelling on foot and mouth disease that suggested that animals in neighbouring farms should be culled, even if there was no evidence of infection. This influenced government policy and led to the total culling of more than six million cattle, sheep and pigs – with a cost to the UK economy estimated at £10 billion. It has been claimed by experts such as Michael Thrusfield, professor of veterinary epidemiology at Edinburgh University, that Ferguson’s modelling on foot and mouth was ‘severely flawed’ and made a ‘serious error’ by ‘ignoring the species composition of farms,’ and the fact that the disease spread faster between different species. Does Ferguson acknowledge that his modelling in 2001 was flawed and if so, has he taken steps to avoid future mistakes?

"In 2002, Ferguson predicted that between 50 and 50,000 people would likely die from exposure to BSE (mad cow disease) in beef. He also predicted that number could rise to 150,000 if there was a sheep epidemic as well. In the UK, there have only been 177 deaths from BSE. Does Ferguson believe that his ‘worst-case scenario’ in this case was too high? If so, what lessons has he learnt when it comes to his modelling since?

"Ferguson’s disease modelling for Covid-19 has been criticised by experts such as John Ioannidis, professor in disease prevention at Stanford University, who has said that: ‘The Imperial College study has been done by a highly competent team of modellers. However, some of the major assumptions and estimates that are built in the calculations seem to be substantially inflated.’ Has the Imperial team’s Covid-19 model been subject to outside scrutiny from other experts, and are the team questioning their own assumptions used? What safeguards are in place?

"On 22 March, Ferguson said that Imperial College London’s model of the Covid-19 disease is based on undocumented, 13-year-old computer code, that was intended to be used for a feared influenza pandemic, rather than a coronavirus. How many assumptions in the Imperial model are still based on influenza and is there any risk that the modelling is flawed because of these assumptions?"

Read more: https://www.spectator.co.uk/article/six-questions-that-neil-ferguson-should-be-asked/amp

The flawed model behind the shutdowns

How One Model Simulated 2.2 Million U.S. Deaths from COVID-19 | Cato @ Liberty - Alan Reynolds:

April 21, 2020 - When it came to dealing with an unexpected surge in infections and deaths from SARS-CoV-2 (the virus causing COVID-19 symptoms), federal and state policymakers understandably sought guidance from competing epidemiological computer models. On March 16, a 20-page report from Neil Ferguson's team at Imperial College London quickly gathered enormous attention by producing enormous death estimates. Dr. Ferguson had previously publicized almost equally sensational death estimates from mad cow disease, bird flu and swine flu.

The New York Times quickly ran the hot news about this new COVID-19 estimate: 'The report, which warned that an uncontrolled spread of the disease could cause as many as 510,000 deaths in Britain, triggered a sudden shift in the government’s comparatively relaxed response to the virus. American officials said the report, which projected up to 2.2 million deaths in the United States from such a spread, also influenced the White House to strengthen its measures to isolate members of the public.'

A month later that 2.2 million estimate was still being used (without revealing the source) by President Trump and Doctors Fauci and Birx to imply that up to two million lives had been saved by state lockdowns and business closings and/or by federal travel bans....

[Dr. Ferguson's model] came up with 2.2 million deaths by simply assuming that 81% of the population gets infected* – ­268 million people – and that 0.9% of them die.... Neither the high infection rate nor the high fatality rate holds up under scrutiny.

To project that nearly everyone becomes infected the report had to assume that each person infects 2.4 others and those people, in turn, infect 2.4 others and so on, with the result that the number infected doubles roughly every four days. This 2.4 'reproduction number' (R0) was 'based on ... the early growth-rate of the epidemic in Wuhan.' But the reproduction number always appears highest during the early phase of an epidemic (partly due to increased testing) and has now fallen to nearly zero in China....

The worst-case Imperial College estimate of 2.2 million deaths if everyone does 'nothing' did not simply mean no government lockdowns, as a March 31 White House graph with two curves implied. It meant nobody avoids crowded elevators, or wears face masks, washes their hands more often, or buys gloves or hand sanitizer. Everyone does literally nothing to avoid danger. The Ferguson team knew that was unrealistic, yet their phantasmal 2.2 million estimate depended on it.... The obvious reality of voluntary self-protective actions makes it incorrect to allude to the extreme Ferguson death estimate, consciously or not, as evidence that heavy-handed government interventions prevented 'hundreds of thousands' of deaths....

The key premise of 81% of the population being infected should have raised more alarms than it did. Even the deadly 'Spanish Flu' (H1N1) pandemic of 1918-19 infected no more than 28% of the U.S. population. The next H1N1 'Swine Flu' pandemic, in 2009-10, infected 20-24% of Americans.

To push the percentage infected up from 20-28% to an unprecedented 81% for COVID-19 required assuming the number of cases and/or deaths keeps doubling every three or four days for months (deaths were predicted to peak July 20). And that means assuming the estimated reproduction number (R0) of 2.4 remains high, and people keep mingling with different groups....  Long before 8 out of 10 people became infected, however, a larger and larger percentage of the population would have recovered from the disease and become immune, so a smaller and smaller share would still remain susceptible....

In short, the Imperial College projection that 81% of the U.S. population could be infected if everyone just did literally nothing to protect themselves or others is inconsistent with rational risk avoidance, history and recent experience. Even with a much smaller percentage infected, however, deaths could still end up extremely high if nearly 1% of those infected died, as the Ferguson team assumed.

The assumed 0.9% death rate (within a range of 0.4% to 1.4%) was tweaked from a smaller estimate in a study of deaths in China by Robert Verrity and others, which found a 'case fatality rate' (CFR) of 1.38% among known and tested cases. By assuming that such confirmed cases underestimated actual infections by only about half, they inferred an 'infection fatality rate' (IFR) of 0.66%.

Epidemiologists have since found growing evidence that the number of undetected cases with few symptoms or none is much larger than merely doubling the small number of known and tested cases. A review of such research by the Oxford University Centre for Evidence-Based Medicine finds 'a presumed estimate for the COVID-19 IFR somewhere between 0.1% and 0.36%.' A middling estimate of 0.22% would by itself reduce the infamous 2.2 million death estimate to half a million even if 81% were somehow infected....

The trouble with being too easily led by models is we can too easily be misled by models. Epidemic models may seem entirely different from economic models or climate models, but they all make terrible forecasts if filled with wrong assumptions and parameters.

Read more: https://www.cato.org/blog/how-one-model-simulated-22-million-us-deaths-covid-19


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*Prof. Ferguson's foundation for the 81%  figure was his estimation of the initial reproduction number (Ro, or 'r-nought'): "In such scenarios, given an estimated R0 of 2.4, we predict 81% of the G.B. and U.S. populations would be infected over the course of the epidemic." However, the herd immunity threshhold for the disease is a standard function of Ro: (Ro-1)/Ro ; if Ro=2.4, herd immunity would be reached at 58.33% of the population. -gd