We’re publishing an original essay today by a senior research scientist at a pharmaceutical company asking how managing an ‘asymptomatic disease’ became the main focus of Government policy in the U.K. and around the world, when the very concept of an ‘asymptomatic disease’ is nonsensical. Here’s an extract in which he tries to flesh out exactly why the concept makes so little sense.
First, let’s see why defining having a disease based purely on the presence of a pathogen is a flawed concept. This is best illustrated by reference to another virus, Epstein-Barr Virus or EBV. You’ll be forgiven if you’ve never heard of this virus, but it could be argued to be one of the most successful human pathogens because almost everyone is infected by it. Most people are infected early in life and if this happens then EBV takes up residence in your B-cells (the cells in your immune system responsible for making antibodies) where it quietly persists throughout your life. Every now and then the virus goes into active replication and makes copies of itself which get shed into your mouth, a process that you are blissfully unaware is happening. The problems with EBV generally occur if you don’t get infected early in life but avoid infection until you’re much older. Now when you get infected with EBV, you can develop a disease called infectious mononucleosis or, more commonly, glandular fever. This often happens in young adults when they become interested in close physical contact with members of the opposite (or same) sex… which is why glandular fever is sometimes referred to as “the kissing disease”.
Now let’s apply the new asymptomatic COVID-19 orthodoxy to EBV where we define having a disease purely through the presence of a viral genome. So, according to this definition, almost everyone in the U.K. (and the world) is suffering from a new disease, asymptomatic glandular fever, and if we were to do a large-scale mass screening campaign we’d discover that there were millions of ‘cases’ of asymptomatic glandular fever in the U.K. alone!
Of course, this is complete nonsense. We aren’t all ‘suffering’ from asymptomatic glandular fever. Glandular fever requires infection by EBV, but EBV infection does not necessarily lead to glandular fever. The same is true of COVID-19 and SARS-CoV-2 and so the concept of asymptomatic COVID-19 as a disease is as ridiculous as that of asymptomatic glandular fever.
We were greeted by good news yesterday. A new UK population study from the University of Oxford, based on the ONS Infection Survey, shows that in fully vaccinated people asymptomatic infections were down 70% and symptomatic infections by 90%. The Telegraph has the story:
In the first large real-world study of the impact of vaccination on the general population, researchers found that the rollout is having a major impact on cutting both symptomatic and asymptomatic cases.
Sarah Walker, Professor of Medical Statistics and Epidemiology at Oxford and Chief Investigator on the Office for National Statistics COVID-19 Infection Survey, said that Britain had “moved from a pandemic to an endemic situation” where the virus is circulating at a low, largely controllable level in the community.
The new research, based on throat swabs from 373,402 people between December 1st last year and April 3rd, found three weeks after one dose of either the Pfizer or AstraZeneca jab, symptomatic infections fell by 74% and infections without symptoms by 57%.
By two doses, asymptomatic infections were down 70% and symptomatic by 90%.
But is it all as it seems? I wrote last week about vaccine studies that have glaring issues that everyone, including the authors, seem content to gloss over. Sadly, the same appears to be true of this study.
Here’s one of the key figures. Look at diagram A in the top left. The dots represent the infection rate in seven different groups of people defined by how long before or after vaccination they are and whether they’ve had Covid before.
It starts at the top with the group of people who are more than 21 days prior to being vaccinated and who haven’t had Covid before (and who may not have a vaccine booked or even be eligible yet for a vaccine). This group is the baseline so is given the value 1, and the number of infections in other groups are compared to this as a proportion. So the next group are those people who are less than 21 days before their first jab and who haven’t had Covid before, and they had 0.28 of the rate of infections that the first group had (once adjusted for various confounding factors such as location, age and sex).
This is the first oddity. Why do those less than three weeks before their first jab have around a quarter of the infections of those more than three weeks away from their jab? What is it about crossing that three-week threshold that has such a massive impact on infection risk, by far the biggest effect in the study?
The authors do offer a brief explanation, putting it down to “changes in behaviour due to either receiving the vaccination invitation letter or knowledge that individuals from their age or risk group are about to get vaccinated in their area”. But they offer no evidence of this mass change in behaviour triggered by the approach of the vaccination, and the vaccine invitation letter includes no advice to make any new effort to avoid people. In any case, it means the headline finding of the study should probably have been that being less than three weeks before your jab cuts infections by 72% – even more than being fully vaccinated!
Many people still struggle to accept the idea that lockdowns don’t have any appreciable impact on Covid cases and deaths. After all, it’s obvious, isn’t it, that keeping people apart will stop the virus spreading?
Tom Harwood, formerly of Guido Fawkes now of GB News, tweeted a typically incredulous response to the idea: “Cannot understand how some can claim ‘lockdowns don’t work’ with a straight face. As if stopping people from mixing wouldn’t hit transmission? Sure argue the cost is too high, imposition on liberty too extreme, just don’t invent a fairytale denying the basics of germ theory.”
Even some die-hard lockdown sceptics will say that lockdowns work, in the sense of suppressing transmission for a time, but they just delay the inevitable so are pointlessly costly.
The models churned out by university academics and relied on by the Government to set policy all assume lockdown restrictions work, and even claim to quantify how much impact each intervention makes.
But what does the data say? What do the studies show that actually look at the evidence rather than just making a priori assumptions about how things “must surely” be?
There have been at least seven peer-reviewed studies which look at the question of lockdowns from a data point of view, and all of them come to the same basic conclusion: lockdowns do not have a statistically significant relationship with Covid cases or deaths. Here is a list of them with a key quote for ease of reference.
“Comparing weekly mortality in 24 European countries, the findings in this paper suggest that more severe lockdown policies have not been associated with lower mortality. In other words, the lockdowns have not worked as intended.” “Did Lockdown Work? An Economist’s Cross-Country Comparison” by Christian Bjørnskov. CESifo Economic Studies March 29th, 2021.
“Previous studies have claimed that shelter-in-place orders saved thousands of lives, but we reassess these analyses and show that they are not reliable. We find that shelter-in-place orders had no detectable health benefits, only modest effects on behaviour, and small but adverse effects on the economy.” “Evaluating the effects of shelter-in-place policies during the COVID-19 pandemic” by Christopher R. Berry, Anthony Fowler, Tamara Glazer, Samantha Handel-Meyer, and Alec MacMillen, Proceedings of the National Academy of Science of the USA, April 13th, 2021.
“We were not able to explain the variation of deaths per million in different regions in the world by social isolation, herein analysed as differences in staying at home, compared to baseline. In the restrictive and global comparisons, only 3% and 1.6% of the comparisons were significantly different, respectively.” “Stay-at-home policy is a case of exception fallacy: an internet-based ecological study,” by R. F. Savaris, G. Pumi, J. Dalzochio & R. Kunst. Scientific Reports (Nature), March 5th, 2021.
Many of these studies attribute a large part of the drop in infections and deaths to the voluntary measures introduced prior to the legally-enforced restrictions. However, this is typically introduced as an assumption with no robust evidence provided in support of it, and with no consideration of the other possible reasons that infections might have fallen, such as seasonality or growing population immunity. On the rare occasion that rigorous analysis is applied to this question as well, as with Savaris et al in their article in Naturelooking at whether people staying at home (measured using mobility data) is associated with Covid deaths, the finding is similarly negative. Voluntary measures make little difference either.
This may seem to defy “the basics of germ theory”, as Mr Harwood put it. But it doesn’t, it just means we need to understand better how the virus is getting round.
Lockdown Sceptics’ contributors Norman Fenton, Martin Neil and Scott Mclachlan, all at the School of Electronic Engineering and Computer Science at Queen Mary, have produced a thorough analysis of how many people with COVID-19 are asymptomatic. Their conclusion: not one in three, as the Government would have us believe, but one in 19. Here is the abstract:
Over the period Dec 2020 – Feb 2021, the UK Government, and its scientific advisers, made the persistent and widely publicised claim that “1 in 3 people with the SARS-Cov-2 virus have no symptoms”. In this paper we use a contemporaneous study of asymptomatics at Cambridge University to show that the claim is contradicted by the government’s own case numbers over that same period. A Bayesian analysis shows that, firstly, if the “1 in 3” claim is correct then, over this period, the actual infection rate must be at least 11 times higher than the infection rate reported by the Office for National Statistics (ONS), 0.71% ; and, secondly, if the reported infection rate of 0.71% is correct then the actual number of people with the virus, who have no symptoms, is at most 2.9% (1 in 34) and not 1 in 3. We argue that this contradiction can only be explained by the false positives being generated by RT-PCR testing. Hence, the published infection rate is estimating the number of people who test positive rather than the number of people with SARS-Cov-2 virus. When the false positive rate is correctly accounted for, the most likely explanation for the observed data, over the period in question, is an infection rate of approximately 0.375% rather than the ONS publicised claim of 0.71%. Likewise, we conclude that the actual number of people with the SARS-Cov-2 virus who have no symptoms is approximately 1 in 19 and not 1 in 3. We show that these results are robust under a sensitivity analysis that allows for a wide range of assumptions about testing accuracy and proportion of people with symptoms. Hence, the UK government and ONS claims cannot both be simultaneously true and the actual infection rates are significantly less than publicised.
There’s been a lot of worry about ‘misinformation’ around COVID-19, with numerous calls to suppress anything that doesn’t agree with the WHO’s current line, and news and social media companies all too happy to oblige.
Sometimes, though, the worst offenders are the mainstream sources themselves.
Take Wikipedia. On its main COVID-19 page – a page which cannot be edited by mere mortals as it is “protected to prevent vandalism” – it states the following in the second paragraph:
This is claiming that almost a fifth of symptomatic COVID-19 infections are severe, and 1 in 20 are critical. If these are the statistics that people are reading then no wonder they’re scared.
Wikipedia is many people’s first port of call when looking up a subject, and often comes out near the top of internet searches. So the fact that it grossly exaggerates the seriousness of COVID-19 should be concerning. Even more concerning is why it does so.
Where did Wikipedia get its stats from? Alarmingly, the reference is to the U.S. Centers for Disease Control (CDC). In its latest clinical guidance, in a section headed “Illness Severity”, the U.S. federal health agency states:
A large cohort that included more than 44,000 people with COVID-19 from China, showed that illness severity can range from mild to critical:
– Mild to moderate (mild symptoms up to mild pneumonia): 81%
– Severe (dyspnea, hypoxia, or more than 50% lung involvement on imaging): 14%
– Critical (respiratory failure, shock, or multiorgan system dysfunction): 5%
In this study, all deaths occurred among patients with critical illness, and the overall case fatality ratio (CFR) was 2.3%.
These statistics come straight from an early study on the first 44,000 Covid patients in China, published on February 24th 2020. The study does not mention hospital admissions and it appears that all of these cases were in fact hospital patients. At any rate, the figures suggest a sample heavily skewed towards serious illness.
A more accurate estimate of severity comes from the ONS. In the December peak, the ONS estimated around 2% of the population of England were infected with COVID-19 and around 0.04% of the population were being admitted to hospital each week with the virus. This means about 2% of infections were leading to hospital admission, or 1% if we allow for the estimated half of serious infections caught in hospital. This is about 20 times lower than the nearly 20% serious infections in the Chinese study.
Why is the CDC still using this early study as its main source of statistics on the severity of COVID-19 when we’ve found out so much more about the illness since February 2020? Why is Wikipedia featuring these figures at the top of its COVID-19 page? Don’t they realise how misleading and unnecessarily frightening they are?
New variants are of no concern. There is no need to cancel summer holidays. Millions vaccinated, coupled with immunity from millions of prior infections means we can surf on the crest of the third wave, rather than being remotely concerned about it. In fact, the UK should open now. And vaccine passports, certificates, or whatever name they are being given, will do nothing to improve the health of the population – all headlines we have read and heard over the past week or so.
Except, we haven’t. We have heard and read the opposite. And we are instilled with fear from TV and radio adverts, complete with ‘that scary voice’ all too eager to give listeners nightmares, be it your impressionable primary-school-aged daughter, or a frail older lady now terrified into wearing a mask outside while waiting for a bus with no one within a 50-metre radius. But the reality is that the above headlines could have been written – and all based on science. Jayanta Bhattacharya is a Professor of Medicine at Stanford University and one of the co-authors of the Great Barrington Declaration, the report that called for the focused protection of the vulnerable and no lockdowns, signed by almost 14,000 medical and public health scientists, nearly 42,000 medical practitioners and close to 765,000 concerned citizens.
I interviewed him by email and he remains a staunch lockdown sceptic.
Why have the media, politicians and many scientists sought to panic the populace about SARS-CoV-2 far beyond what the evidence would warrant? The incentives include financial motives, political goals, the desire to protect professional reputations and many other factors.
The virus is seasonal and late fall/winter is its season. It is very unlikely, given that this is the case, that the virus will spread very widely during the summer months. It is also the case that a large fraction of the UK population has already been infected or vaccinated and is immune, which will greatly reduce hospitalisation and mortality from the virus in coming months.
There are tens of thousands of mutations of the SARS-CoV-2 virus. They mutate because the replication mechanisms they induce involve very little error checking. Most of the mutations either do not change the virulence of the virus, or weaken it. There are a few mutations that provide the virus with a selective advantage in infectivity and may increase its lethality very slightly, though the evidence on this latter point is not solid.
We should not be particularly concerned about the variants that have arisen to date. First, prior infection with the wild type virus and vaccination provide protection against severe outcomes arising from reinfection with the mutated virus. Second, though the mutants have taken over the few remaining cases, their rise has coincided with a sharp drop in cases and deaths, even in countries where they have come to dominate. Their selective infectivity advantage has not been enough to cause a resurgence in cases. Third, the age gradient in mortality is the same for the mutant and wild-type virus. Thus a focused protection policy is still warranted. If lockdowns could not stop the less infectious wild type virus, why would we expect them to stop the more infectious mutant virus?
According to the three authors of the Great Barrington Declaration which, other than Dr Bhattacharya, include Dr Martin Kulldorff, Professor of Medicine at Harvard Medical School, and Dr Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford, the UK Government is creating unfounded hysteria around SARS-CoV-2. Dr Bhattacharya said:
According to a meta-analysis by Dr John Ioannidis [Professor of Medicine at Stanford University] of every seroprevalence study conducted to date of publication with a supporting scientific paper (74 estimates from 61 studies and 51 different localities around the world), the median infection survival rate from COVID-19 infection is 99.77 per cent. For COVID-19 patients under 70, the meta-analysis finds an infection survival rate of 99.95 per cent.
The CDC’s [Centres for Disease Control] and Prevention] best estimate of infection fatality rate for people ages 70 plus years is 5.4 per cent, meaning seniors have a 94.6 per cent survivability rate. For children and people in their 20s/30s, it poses less risk of mortality than the flu. For people in their 60s and above, it is much more dangerous than the flu.
Even so, this hardly warrants a new Government drive urging families to carry out tests on their children twice a week in the hope of unearthing asymptomatic cases. Especially, as the vulnerable have already been vaccinated.
The scientific evidence now strongly suggests that COVID-19 infected individuals who are asymptomatic are more than an order of magnitude less likely to spread the disease to even close contacts than symptomatic COVID-19 patients. A meta-analysis of 54 studies from around the world found that within households – where none of the safeguards that restaurants are required to apply are typically applied – symptomatic patients passed on the disease to household members in 18 per cent of instances, while asymptomatic patients passed on the disease to household members in 0.7 per cent of instances. A separate, smaller meta-analysis similarly found that asymptomatic patients are much less likely to infect others than symptomatic patients.
Asymptomatic individuals are an order of magnitude less likely to infect others than symptomatic individuals, even in intimate settings such as people living in the same household where people are much less likely to follow social distancing and masking practices that they follow outside the household. Spread of the disease in less intimate settings by asymptomatic individuals – including religious services, in-person restaurant visits, gyms, and other public settings – are likely to be even less likely than in the household.
What about mask mandates?
The evidence that mask mandates work to slow the spread of the disease is very weak. The only randomised evaluation of mask efficacy in preventing Covid infection found very small, statistically insignificant effects [Danish mask study]. And masks are deleterious to the social and educational development of children, especially young children. They are not needed to address the epidemic. In Sweden, for instance, children have been in school maskless almost the whole of the epidemic, with no child Covid deaths and teachers contracting Covid at rates that are lower than the average of other workers.
In light of this, what conclusion can we draw from the fact that the UK Government wants the entire adult population to be injected against the virus, instead of just the vulnerable? And the possibility that we’ll need to produce vaccine certificates to access hospitality and sports venues or travel overseas?
Vaccine passports are a terrible idea that will diminish trust in public health and do nothing to improve the health of the population. Vaccine certificates are not needed as a public health measure. The Government had it right previously. The country should open up now that the older, vulnerable population has been vaccinated. The rest of the population is at much greater health risk from the lockdown than they are from the virus.
The author is a staff journalistat a national newspaper group.Oliver May is a pseudonym.