Noah Carl

Putting the Pandemic’s Death Toll Into Perspective

There are two ‘official’ death tolls on the Government’s COVID-19 dashboard. 138,852 is the number of deaths within 28 days of a positive test. 162,620 is the number of deaths with COVID-19 on the death certificate.

The main reason the latter is larger than the former is lack of testing during the first wave. In the spring of last year, about 15,000 people in whose death COVID-19 was a contributing factor died without being tested.

So is 162,620 the pandemic’s true death toll? No. And that’s because it includes a large number of deaths that probably would have happened anyway.

How do we know this? Because if we calculate the excess deaths – the number of deaths in excess of what we’d expect based on previous years – we get a much lower number.

The official death toll for England and Wales, based on death certificates, is 147,031. Yet if we add up all the deaths since the start of March 2020, and subtract the average over the last five years, we get a figure of 117,476 (about 20% lower).

What’s more, due to population ageing, the average over the last five years understates the expected number of deaths. Hence the true number of excess deaths is about 15% lower. Taking this into account, the pandemic’s total death toll in England and Wales is about 100,000.

However, when it comes to events like pandemics, estimating the total death toll isn’t the best way to gauge the impact on mortality. Consider an example.

Japan and Mexico have about the same population, but there are more deaths each year in Japan. How can this be, when everyone knows Japan is a very long-lived country? The reason is simple: there are more elderly people in Japan, so there are more people at high-risk of dying each year.

A better way of comparing the level of mortality in Japan and Mexico is to use the age-standardised mortality rate or life expectancy. Both of these measures take into account the risk of dying at different ages, as well as the age-structure of the population. (In 2019, Japan’s life expectancy was 84, whereas Mexico’s was only 76.)

Last year, the U.K.’s age-standardised mortality rate rose by 12.8%. Although this is the largest one-year change since 1940 (the first year of the Blitz), the level to which mortality rose was lower than in 2008. And even the change should be put into context: 2019 was a year of unusually low mortality.

I previously estimated that the life expectancy in England and Wales last year was 80.4 – down from 81.8 in 2019. (Other researchers have reported similar figures.) So despite tens of thousands of excess deaths, life expectancy was still around 80.

Lockdown: Where Did ‘The Science’ Come From?

In a previous post, I looked at where ‘The Science’ of community masking came from. Here I’ll do the same thing for lockdowns.

As many lockdown sceptics (including myself) have noted, lockdowns represent a radical departure from conventional forms of pandemic management. There is no evidence that, before 2020, they were considered an effective way to deal with influenza pandemics.

In a 2006 paper, four leading scientists (including Donald Henderson, who led the effort to eradicate smallpox) examined measures for controlling pandemic influenza. Regarding “large-scale quarantine”, they wrote, “The negative consequences… are so extreme” that this measure “should be eliminated from serious consideration”.

Likewise, a WHO report published mere months before the COVID-19 pandemic classified “quarantine of exposed individuals” as “not recommended under any circumstances”. The report noted that “there is no obvious rationale for this measure”.

And we all know what the U.K.’s own ‘Pandemic Preparedness Strategy’ said, namely: “It will not be possible to halt the spread of a new pandemic influenza virus, and it would be a waste of public health resources and capacity to attempt to do so.”

As an additional exercise, I searched the pandemic preparedness plans of all the English-speaking Western countries (U.K., Ireland, U.S., Canada, Australia and New Zealand) for mentions of ‘lockdown’, ‘lock-down’ ‘lock down’ or ‘curfew’.

Only ‘curfew’ was mentioned, and only once – in Ireland’s plan. The relevant sentence was: “Mandatory quarantine and curfews are not considered necessary.” None of the lockdown strings was mentioned in any of the countries’ plans.

So where did ‘The Science’ of controlling Covid using lockdowns come from? As everyone knows, China implemented the first lockdown (of Hubei province) in January of 2020. Yet it wasn’t until March that lockdowns became part of ‘The Science’.

Did Denmark Achieve Focused Protection in the Second Wave?

Before the vaccines arrived, lockdown proponents argued that the only way to prevent large numbers of Covid deaths was by completely suppressing viral transmission. A focused protection strategy, they maintained, was just not workable.

The basic argument is as follows. Because the virus is so transmissible, and society is so interconnected, it would have been impossible to protect vulnerable people if we’d allowed community transmission to proceed unchecked. Without a lockdown, the virus would inevitably have found its way into hospitals and care homes, leading to lots of deaths.

It’s not an unreasonable argument, but I don’t buy it. (And let’s put aside the fact that even if lockdown does prevent more Covid deaths than focused protection, the total costs almost certainly outweigh the benefits.)

We already know that places like Utah, Sweden and South Dakota, which refused to lock down last year, did not do substantially worse than places that did lock down. We can argue about exactly how to do the comparison; the fact is that none of the dire predictions made for these locations actually came to pass.  

But is there an example of a country that achieved focused protection? Denmark might well be the closest. If we zoom-in on the second wave, and compare the country’s infection rate to that of the U.K., it isn’t dramatically lower:

Assuming the numbers are indeed comparable (which I’ll admit is a big assumption), Denmark saw 30% fewer infections between August of 2020 and May of 2021. Denmark did do more testing over this time period, but the U.K. had a higher share of positive tests.

If the lockdowners’ argument against focused protection is right, we’d expect Denmark to have had only 30% fewer deaths than the U.K. during the second wave; or at most, perhaps 50% fewer. After all, the country’s infection rate peaked at over 600 per million.

But this isn’t what we find. According to Karlinsky and Kobak, Denmark has had only 1% excess mortality since the pandemic began; the U.K.’s figure, by contrast, is 20%.

Now, more than half of Britain’s excess mortality was sustained in the first wave (which Denmark managed to avoid). But suppose that eight percentage points of the 20% were sustained in the second wave.

This would mean that Denmark’s deaths were not 30% or 50% lower than the U.K.’s, but almost 90% lower. Despite experiencing a moderately high infection rate in the winter, Denmark managed to keep deaths to a minimum.

Note: I’m not suggesting the country didn’t lock down; it did. (Though there was never a stay-at-home order, and the average stringency index was much lower than in Britain). My point is that some degree of focused protection apparently is achievable. There’s no necessary relationship between the infection rate and the death toll.

It doesn’t follow that Britain could have done as well as Denmark, which tends to finish at the top of every international league table. But with a bit of ingenuity, we could have done better than we did – in terms of both lives saved and collateral damage avoided.

The recent House of Commons report described the U.K.’s initial approach as “fatalistic”. But what was really fatalistic was assuming the only way to stop people dying of Covid was shuttering the economy and throwing civil liberties out the window.    

The House of Commons Report Ignores the Risks of a Suppression Strategy

One of the main conclusions of the recent House of Commons report is that our first lockdown “should have come sooner”. The authors even take seriously Neil Ferguson’s ludicrous suggestion that if we’d locked down one week earlier, “we would have reduced the final death toll by at least half”.

As I noted in my response, this ignores the fact that suppressing the epidemic in the spring could have led to an even bigger epidemic in the winter, when the NHS would have been under greater pressure.

In other words, even if you only consider Covid deaths (i.e., ignore all the collateral damage from lockdown), suppressing the first wave wasn’t necessarily the right thing to do. The boffins in SAGE were actually aware of this, as the report notes:

Modelling at the time suggested that to suppress the spread of covid-19 too firmly would cause a resurgence when restrictions were lifted. This was thought likely to result in a peak in the autumn and winter when NHS pressures were already likely to be severe.

However, the report’s authors dismiss this very legitimate concern on the basis that suppressing the first wave would have “bought much needed time”. And that’s true, but so is the point about risking a perfect storm in the winter.

The correct way to frame the issue (again, ignoring the costs of lockdown) would be to say: the UK faced a trade-off between the benefits of buying time versus the risks of postponing the epidemic until winter. Acknowledging this (or any other) trade-off was apparently too much to ask of the report’s authors.

As a side note, suppressing the first wave would have probably required us to act in January, and we’d have needed to completely seal the borders, in addition to imposing a temporary lockdown. The horse had already bolted by the time anyone knew what was going on, so this discussion is mostly academic anyway.

One simple way to illustrate the risks of postponing the epidemic until winter is to compare European countries that got hit in the first wave with those who missed the first wave but got hit in the second.

To do this, I noted for each 42 European countries whether the official COVID-19 death rate reached 5 per million before 1st September, 2020. Those where it did reach this level were deemed to have been hit in the first wave. Those where it did not were deemed to have missed the first wave.

I then calculated average excess mortality since the pandemic began in the two groups of countries, using the estimates reported by Karlinsky and Kobak. Note: I’m not pretending this is a comprehensive analysis. But it’s still informative.

If the benefits of buying time outweigh the risks of postponing, you’d expect excess mortality to be lower in the group that missed the first wave. However, it was actually slightly higher in this group: 21%, compared to 19% in the other group.

What’s more, the 42 countries in my sample include places like Iceland and San Marino, which you might say aren’t really comparable to the UK. If we remove all six countries with a population of less than 500,000, the disparity is even greater: 22%, compared to 16%.

Now, there are of course other factors to consider, and it’s possible that once you took those into account, there wouldn’t be any disparity, or there’d be a slight disparity favouring the first group. But there’s no evidence that ‘buying time’ led to substantially lower excess mortality.

Someone might respond as follows: it’s implausible that suppressing the first wave would have made a difference in the second. After all, only about 10% of the population had antibodies by December of 2020, and that’s nowhere near herd immunity.  

There are two points I’d make in response. Some people may have cross immunities to Covid, so the 10% figure could be an underestimate. But even if it’s about right, we know that transmission is driven by super-spreaders, and such individuals will be heavily overrepresented among the 10% who got infected in the first wave.

All else being equal, therefore, transmission would have been greater in the second wave if those individuals had not acquired immunity in the first. (Recall that age-adjusted excess mortality was actually lower in the second wave.)

The House of Commons report is in no sense a disinterested attempt to consider the arguments for and against lockdown, so it’s hardly surprising the authors would brush aside the risks of a suppression strategy. We can only hope that the official inquiry next year takes a less tendentious approach. But I wouldn’t bet on it.

BMJ Publishes Belated Attack on the Great Barrington Declaration, but It Doesn’t Hit the Target

The Great Barrington Declaration, which advocates a focused protection strategy for dealing with COVID-19, was published in October last year – before many countries around the world imposed their winter lockdowns.   

Recently, The BMJ Opinion – a journalistic offshoot of the well-known medical journal – published a very belated hit piece against the authors. As you might expect, it’s light on scientific arguments and heavy on tactics like ad hominem, guilt by association and appeals to authority.

The authors, David Gorski and Gavin Yamey, really don’t mince words. For example, they describe the Declaration (which has been signed by hundreds of scientists and healthcare professionals) as a “well-funded sophisticated science denialist campaign based on ideological and corporate interests”.

Not exactly a respectful way to talk about your colleagues. But it’s hardly the first time the Declaration’s critics have sunk to this level. Just last month, Jay Bhattacharya became the subject of a censorious petition which claimed that he “sows mistrust of policies designed to protect the public health”.

Gorski and Yamey begin their article by criticising the Declaration’s authors for collaborating with the American Institute for Economic Research, which they claim is a “libertarian, climate-denialist, free market think tank”.

I’m not sure why this is a ‘gotcha’. Lockdown is about as un-libertarian a policy as you could imagine, so it’s not really surprising that a libertarian think tank would oppose it. And in any case, the Declaration’s website clearly states that the document was “was written and signed at the American Institute for Economic Research”.

Martin Kulldorff has since clarified that the AIER president and board did not know about the Declaration until after it was published. But even if they had done, so what? As Kulldorff notes, universities like Duke and Stanford have received money from the Koch brothers. Should we therefore completely disregard what their academics have to say?

Gorski and Yamey’s next move is to cite social media censorship of lockdown sceptics as evidence that their arguments constitute ‘misinformation’. (Incidentally, that term – which basically means ‘information that’s missing from the mainstream narrative’ – appears no fewer than six times in the article.)  

However, this argument relies on circular logic: ‘Something was censored on social media? Therefore, it’s misinformation. How do we know? Well, misinformation is what social media companies censor.’ In reality, of course, the fact that something was censored is no indication whatsoever that it’s factually incorrect.

The authors then allege that when Sunetra Gupta and Carl Heneghan met Boris Johnson in September of last year, they were successful in “persuading him to delay” a ‘circuit breaker’ lockdown, which could have forestalled the second wave of infections.

As historian Phil Magness has already noted, this argument is deficient on two counts. It’s not clear that Gupta and Heneghan did persuade the Prime Minister to shelve the ‘circuit breaker’ idea. But even if they did, there’s no reason to believe that policy would’ve prevented a large number of deaths.

Finally, Gorski and Yamey compare lockdown sceptics to ‘climate science deniers’, insofar as both groups “argue that evidence-based public health measures do not work”. They call for experts to push back against the Great Barrington Declaration by highlighting “scientific consensus”, citing the John Snow Memorandum.

Of course, the pro-lockdown John Snow Memorandum is just another public statement signed by scientists and health professionals. If it constitutes “scientific consensus”, then so does the Great Barrington Declaration. I’m only aware of one attempt to gauge overall expert opinion on focused protection: the survey by Daniele Fanelli.

He asked scientists who’d published at least one relevant paper, “In light of current evidence, to what extent do you support a ‘focused protection’ policy against COVID-19, like that proposed in the Great Barrington Declaration?” Of those who responded, more than 50% said “partially”, “mostly” or “fully”.  

Regardless of the exact number of experts who support focused protection, claiming there is a “scientific consensus” against it is simply false. Long before the Declaration itself was published, many scientists had proposed some version of precision shielding. In fact, this was basically the U.K.’s plan until the middle of March, 2020.

On March 5th, Chris Whitty told the Health and Social Care Committee that we are “very keen” to “minimise economic and social disruption”, and mentioned that “one of the best things we can do” is “isolate older people from the virus”.

Another prominent scientist who has argued in favour of focused protection is Sir David Spiegelhalter. In an article published on May 29th, he and George Davey Smith said that we ought to “stratify shielding according to risk” because lockdown is “seriously damaging many aspects of people’s lives”.

They noted that this would require “a shift away from the notion that we are all seriously threatened by the disease, which has led to levels of personal fear being strikingly mismatched to objective risk of death”.

Among the ad hominems, appeals to authority and repeated uses of ‘misinformation’, finding a scientific argument in Gorski and Yamey’s article is not easy. And given that the content’s almost a year out of date, I’m not sure why the authors felt the need to publish it.

Preventing Covid Infections Among Healthy Children Is Pointless

Thanks to school closures, children missed out on in-person teaching, as well as regular face-to-face interaction with their friends, for the best part of a year.

The main rationale for closing schools was to help ‘flatten the curve’ of total infections, and thereby prevent the NHS from being overwhelmed. (We’ve known since early on in the pandemic that children’s risk of death from Covid is vanishingly small – lower even than their chance of dying from seasonal flu.)

However, evidence suggests that neither lockdowns in general, nor school closures in particular, were necessary to prevent healthcare systems from being overwhelmed; and the harms from school closures were substantial.

Once the Government conceded it was time for schools to reopen, there came a new justification to keep them closed: protecting teachers. Yet studies have repeatedly shown that teachers are not at elevated risk of death from Covid.

Even after schools finally did open up, pupils faced a rigamarole of mask mandates, regular testing and stints of mandatory self-isolation. Since the vast majority of vulnerable people (and most teachers) had been vaccinated by this point, it’s unclear exactly why things couldn’t just return to normal.

As far as one can discern, the specific rationale seems to be: ‘something to do with case numbers and/or long Covid’. Why we should care about case numbers in an age-group that faces a higher risk of death from season flu has not been explained.

As to long Covid, the latest data suggest that only a tiny number of children (less than 2%) continue to report symptoms 12 weeks after infection. One study found that symptoms were no more common among children who’d had the virus than among those who’d never been infected.

Despite all this, demands for more restrictions in schools can still be heard. On 3rd September, scientists associated with Independent SAGE, as well as various other individuals and organisations, co-signed a letter in The BMJ Opinion calling for the Government “to protect children, our wider communities, and the NHS”.

Their “nine point plan” includes such measures as: reinstating face coverings; offering vaccines to all 12–15 year-olds; and reinstating contact tracing “with a strict policy on mandatory isolation”.  

But according to Chris Whitty, “roughly half” of children have already have Covid, and it’s reasonable to assume that “the great majority” are “going to get it at some point” because “this is incredibly infectious”.

Now that almost all vulnerable people have been vaccinated, why are we trying to stop children getting the virus if “the great majority” of them are going to get it at some point anyway? Offering the vaccine to those with an underlying health condition makes sense, but apart from that, why do anything at all?

In fact, shouldn’t we actively encourage young people to get the virus, so as to build up more population immunity before the winter?

Immunity to Covid Is Still Present 12 Months After Infection

Back in October of 2020, the John Snow Memorandum was published as a letter in the Lancet. Originally co-signed by 31 scientists, hundreds of others have since added their names.

Although it does not explicitly name the Great Barrington Declaration, the Memorandum is widely understood as a response to that document. It refers to “a so-called herd immunity approach”, which proponents claim “would lead to the development of infection-acquired population immunity in the low-risk population”.

However, the Memorandum states: “This is a dangerous fallacy unsupported by scientific evidence.” And it goes on to claim “there is no evidence for lasting protective immunity to SARS-CoV-2 following natural infection”.

According to the organisers’ website, more than 6,900 scientists, researchers and healthcare professionals have signed the Memorandum to date (including names from Oxford and Harvard). So almost 7,000 people with supposed expertise deemed it plausible that natural immunity would not provide any lasting protection against reinfection.

Incidentally, the language used in the Memorandum may be partly responsible for the Great Barrington Declaration being mischaracterised as a ‘herd immunity strategy’. As the authors have been at pains to point out, this is like describing a pilot’s plan to land a plane as a ‘gravity strategy’. (Their approach is more properly described as ‘focused protection’.)

It’s now one year on from the John Snow Memorandum. Is there any evidence for “lasting protective immunity to SARS-CoV-2 following natural infection”? Yes, in fact, there is.

A recent systematic review (which has not yet been peer-reviewed) found that natural immunity confers a high degree of protection against reinfection. The researchers analysed 10 studies, and calculated a weight-average risk reduction of 90%.

But is this protection lasting? According to a new study published in Clinical Infectious Diseases, immunity persists for at least 12 months in the vast majority of convalescents (those who’ve previously been infected).

Chinese researchers carried out a “systematic antigen-specific immune evaluation” on 74 individuals, 12 months after their original infection. They found that “humoral immunity is present within ~95% of convalescents and T-cell memory against at least one viral antigen is measurable among ~90% of subjects at 12m post-infection”.

Note: ‘humoral immunity’ refers to the type of immune response mediated by antibodies, whereas ‘cellular immunity’ refers to the type mediated by T-cells (as well as phagocytes and cytokines).

Although the researchers also had data from 28 healthy controls (individuals who’d never been infected), their sample was not large enough to estimate the protective effect of natural immunity on reinfection. Though it’s worth noting that not a single participant reported reinfection.

A study published last year analysed data on ten healthy males over a period of three decades, to see how often reinfections with seasonal coronaviruses occurred. They found that the median reinfection occurred after 30 months, suggesting that protective immunity lasts for years, not decades.

If SARS-CoV-2 is anything like the four other coronaviruses, we can expect immunity against reinfection to wane on a similar timescale. However, this seems more than sufficient to achieve focused protection, in the sense of shielding the vulnerable through the initial epidemic, and allowing time for treatments and vaccines to be developed.  

Lockdown proponents might respond that lockdown need only have lasted as long as it took to develop the vaccines. But this argument completely ignores the costs side of the ledger. Focused protection could have worked, if only we’d bothered to try it.

Stop Press: The Brownstone Institute has compiled a list of 29 studies showing that natural immunity to SARS-CoV-2 is “robust, long-lasting, and broadly effective”.

Firing Nurses Who’ve Worked Through the Pandemic Is a Disgrace

Across the United States, nurses and other healthcare workers are being fired for not getting vaccinated. Is there any better illustration of the folly of our public health establishment?

These nurses have served on the frontline for more than eighteen months, helping to treat Covid patients day after day, while most of the people demanding vaccine mandates were sitting at home on their laptops.

“Thanks for all your hard work. Oh, you don’t want to get the vaccine? Well in that case, sayonara.” In addition to being mean-spirited, the policy of firing unvaccinated healthcare workers doesn’t really make any practical sense. And that’s putting it charitably.

A large percentage of frontline healthcare workers have already been infected. This means the protection they have against reinfection is actually better than what the vaccines provide.

I reviewed some of the evidence in a recent post. But don’t take my word for it. New undercover footage shows Pfizer scientists saying that natural immunity is “probably” better than immunity from the vaccines.

As I mentioned before, this doesn’t mean that nobody stands to benefit from vaccination. But it does undermine the case for making those who’ve already been infected get the jab. Their natural immunity works just fine.

This point has been made eloquently by the Great Barrington author Martin Kulldorff. In a recent article, he argued that hospitals “should hire, not fire, nurses with natural immunity”. Yet within a matter of hours, Kulldorff’s article had been censored by LinkedIn. (Such petty interference in the scientific debate is now routine on social media.)

So there’s absolutely no case for firing healthcare workers who have natural immunity. What about those who haven’t been previously infected?

Even here, the case for mandates is weak at best. We know that vaccine-induced immunity against infection wanes over time. Six months after vaccination, you’re not that much less likely to become infected than someone who’s never been vaccinated.  

This means that mandating vaccines for healthcare workers is no guarantee of safety. If there’s a ward full of vulnerable patients, ensuring that every nurse is vaccinated won’t necessarily prevent someone from catching Covid, and then spreading it to the rest.

The only surefire way of protecting vulnerable patients is testing everyone before they go into the ward. Positive test? Well, you’ll have to stay at home or work in another part of the hospital for the next few weeks.

There are also the rights of workers themselves to consider. If the vaccines had no side effects and offered lasting protection against infection, the case for mandates would be strong. But the vaccines do have side effects (albeit rare ones) and they don’t provide lasting protection against infection.   

As Oxford philosopher Julian Savulescu argues, autonomy is a core principle of medical ethics, so any policy that violates autonomy (such as mandatory vaccination) can only be justified if it confers substantial third-party benefits. Yet it’s not clear that Covid vaccines do confer such benefits.

What’s more, getting vaccinated isn’t the only way to reduce one’s risk of infection. Avoiding large gatherings is another. Should hospitals be able to require that their staff avoid large gatherings, so as to reduce the risk of infection even further? Most of us would say “no” because it violates individual autonomy.

Of course, keeping Covid out of high-risk hospital wards is an important goal. And although mandatory vaccination is no guarantee of safety, it probably does have some effect, at least for the first few months.

Surely there’s a workaround for nurses who opt against vaccination? For example, they could be tested three times a week until they acquire natural immunity. The costs of such testing could even be deducted from their pay checks (although given the number of unused tests lying around, I don’t see the need).  

Firing healthcare workers who haven’t been infected is mean-spirited and unnecessary. Firing those who have been infected makes no sense at all. Never mind vaccine mandates; we need mandatory training for bureaucrats to make them understand natural immunity.

More Evidence that Natural Immunity Beats Vaccine-Induced Immunity

I previously wrote about the Israeli study which found that natural immunity provides much better protection against infection than the Pfizer vaccine.

Sivan Gazit and colleagues tracked two groups of people over time: fully vaccinated people who’d never tested positive; and unvaccinated people who had tested positive. Of the 257 cases that were detected at follow-up, 93% occurred in the vaccinated group, and only 7% occurred in the previously infected group.

And indeed, a recent systematic review (which has not yet been peer-reviewed) confirms that natural immunity confers a very high degree of protection against infection. The researchers analysed 10 studies, and found that the “weighted average risk reduction against reinfection was 90.4%”.

Compare this to studies of the vaccines’ efficacy against infection. In July, the Israeli Ministry of Health reported that the Pfizer vaccine’s effectiveness had dropped to just 39%. And a pre-print study by Qatari researchers found that it fell to zero after six months. (Though the vaccines’ efficacy against severe diseases appears to hold up well.)

Adding to the evidence outlined above, there are now studies comparing natural and vaccine-induced immunity at the cellular level.

I should mention that I am not qualified to evaluate the methods used by these studies, so I will have to assume the authors have done things properly. With that qualification in mind, it’s worth briefly discussing what they found.

In a recent paper published in Cell Reports, researchers from Minneapolis compared the memory B cells generated after natural infection versus mRNA vaccination. Memory B cells (MBCs) are part of the adaptive immune system; they are responsible for recognising antigens, and triggering a secondary immune response.

The researchers found that “infection-induced primary MBCs have better antigen-binding capacity and generate more plasmablasts and secondary MBCs of the classical and atypical subsets than vaccine-induced primary MBCs”. As a result, infection-induced MBCs “produce more robust secondary responses”.

In a second study, published as a preprint, researchers from Boston compared the durability and breadth of antibodies after natural infection versus mRNA vaccination. They found that infection-induced antibodies “exhibited superior stability and cross-variant neutralisation breadth than antibodies induced by a two-dose mRNA regimen”.

In other words, individuals who’d already been infected had better immunity against the then-novel Delta variant, as compared to ‘naïve’ individuals who’d received an mRNA vaccine.

Taken together, the statistical and immunological evidence suggests that natural immunity provides better protection against infection than the mRNA vaccines. This does not mean that nobody stands to benefit from vaccination. The vaccines are still an important way of achieving focused protection for high-risk groups.

But it does undermine the case for vaccine passports, and for vaccinating 12-15 year-olds. As Jay Bhattacharya wrote back in July: “Any infection-blocking effects are probably short-term unless the vaccine does very much better than natural immunity, which is rare in medicine.”

Why Are People’s Risk Perceptions So Skewed?

Yesterday I noted that, 18 months after the start of the pandemic, a sizeable chunk of Americans still dramatically overestimate the risks of Covid. In a recent poll, more than one third said the risk of being hospitalised if you’re not vaccinated is at least 50%.

Of course, you’d expect some people to get the answer wrong just because we’re dealing with a small quantity, and there’s always going to be some degree of overestimation. But many people were off by a factor more than 10. What accounts for this?

Interestingly, Democrat voters’ guesses were much higher than Republican voters’ – about twice as many Democrats said the risk of being hospitalised if you’re not vaccinated is at least 50%. This suggests a role for ideology.

Throughout the pandemic, the ‘Democrat position’ has been to support restrictions and mandates, whereas the ‘Republican position’ has been to oppose such measures. This is clearly visible in a plot of U.S. states by average stringency index. Almost all the ‘red’ states are on the left-hand side, while almost all the ‘blue’ states are on the right.

Given that partisans (on all sides) like to avoid cognitive dissonance, they tend to adopt beliefs that are consistent with their party’s platform. Since Democrat politicians have been busy imposing all sorts of restrictions and mandates, Democrat voters have adopted beliefs that imply those measures were justified.

Most survey respondents don’t know numbers like ‘the risk of hospitalisation for people who aren’t vaccinated’ off the top of their head. Instead, they probably make a guess based on all the relevant information they can recall.

Democrat voters, who’ve spent the pandemic consuming media like MSNBC, CNN and NPR, will recall numerous incidents of pundits saying that Covid is extremely dangerous, and we have to do whatever we can to stop the spread.

They will also recall that they were locked down for months, that their kids’ schools were closed, and that they had to wear a mask whenever they went to the grocery store. 

Putting all this information together, they will tend to assume that the risk of being hospitalised from Covid is extremely high. ‘Why else,’ they might ask, ‘would there have been so many restrictions?’

Note: Republicans also overestimated the risk of being hospitalised from Covid, albeit to a lesser extent than Democrats. This indicates that people’s skewed risk perceptions cannot be blamed solely on the content of left-wing media (or the policies implemented in ‘blue’ states).  

The psychological quirk that may account for people’s skewed risk perceptions has a name in psychology: the availability heuristic. As Steven Pinker notes, “people estimate the probability of an event or the frequency of a kind of thing by the ease with which instances come to mind”.

Because plane crashes always make the news, people tend to overestimate the risks of air travel. And they may overestimate the risks of Covid for the same reason.

Since the start of the pandemic, we’ve been treated to morbid ‘daily death numbers’ – but for only one cause of death. Perhaps if these figures had been reported for all causes of death, people’s risk perceptions would be slightly less skewed. (Or perhaps they’d just be terrified of everything…)

During a pandemic, we obviously do want people to take precautions; we don’t want them nonchalantly walking into a care home when they have a high fever and a nasty cough. Yet – contrary to what some in government seem to believe – we don’t want people to be utterly terrified either.

There’s been so much attention on people claiming Covid is “just the flu” that the media has largely ignored the other end of the spectrum: people who believe Covid is the bubonic plague!

We can agree it’s bad if people underestimate the risks. But it’s also bad if they overestimate the risks. We want them to have the right risk perceptions. That way, they can make informed decisions.