1. Apolone et al – did COVID spread for six months before we noticed?
Published in November 2020, this was one of the first papers to make policy makers feel really, really awkward. It used stored samples from prior investigations to plot a spread of coronavirus in Italy during the tale end of 2019 and the early months of 2020. It found that, according to the test results, there were two ‘peaks of positivity’ found; one in September (where 18% of Italians showed antibodies) and the other in February, where this rose to 30% (interestingly enough, this figure was 68.7% of those based in Lombardy, where the healthcare systems fell into disarray).
The results are particularly embarrassing as they indicate that coronavirus was in circulation for at least six months in Italy before anyone actually noticed, which lends credence to the politically unpopular idea that this outbreak of coronavirus should be treated like the coronavirus strains that have already been with humans for milennia (aka a proportionate harms model, aka the Swedish model), which amounted to treating people without disease as healthy and providing medical care to those who were ill. This idea explained why Bergamo and Brescia – thrown into panic when thousands of nurses and doctors were sent home, with nursing homes also gutted of their care staff – suffered so badly while next-door Verona, which maintained normal protocols, saw nothing out of the ordinary. Despite local politicians speaking out against the panic model, the Spanish healthcare system was the next to send home nurses and doctors and immediately saw the same results. And so it went. Nobody wanted to discuss the other papers that showed coronavirus was with us for several months before we actually noticed (see here, here and here), or the paper put out by Oxford University that put forward that, if this was spreading at the rate that governments were claiming, then then 50% of the British population would have been exposed by January; the paper, and their calls to actually test people to find out if this was true, were ignored.
In any case, the Apollone paper tends to be entirely ignored by pro-Government groups, as the only criticism that can be offered is that ‘test results must be treated with extreme caution’. While the striking match between locations and patterns over time (against real life outcomes) is certainly hard to ignore, this issue of test reliability is still a very relevant discussion point. Just not one you are likely to hear from those who supported the removal of basic rights and the destruction of many livelihoods on the basis of non-validated test results.
2. Chinese epidemiology – CFR was 0.04 to 0.12%
It may be difficult to do so, but sometimes it can be helpful to cast our minds back to the Diamond Princess incident, the so-called ‘floating petri dish’ for the lack of containment protocols and the ultra-close proximity in which the 1,000 staff live. The round-the-clock coverage of this saga was perhaps gave us the first signs of emerging perspectives on the crisis, with many individuals worried by the claims emerging from prior-unknown scientists pushing the idea that up to 9% of individuals who contract coronavirus would die. Of course, this was not what was seen on the Diamond Princess where, despite the elderly population and the ‘worst-case scenario’ conditions, over 80% of the passengers left their 36-day ordeal without a sniffle. Half of those who tested positive had no symptoms and the total death toll was six (0.85%).
As pointed out at the time by Dr John Ioannidis, the ‘godfather of medical statistics’, pointed out that this was very close to the expected death toll on a population of this age over this length of time and offered a more realistic estimate of the death rate for the general population at 0.125%. His views were pilloried in the media. So then this study above came along, the first large study in a real-world setting. It studied 1.9m million people and strongly supported his conclusions, running multiple analyses to determine that the CFR (Case Fatality Rate) was between 0.04 and 0.12%. Governments ignored the actual numbers, choosing to go with modelling estimates. The rest is history.
3. BMJ article on overall mortality
Whether it be in the daily headlines in the tabloids or the red numbers in the round-the-clock readouts on Sky News, the official death toll was never far away from public consciousness. But did anyone stop to ask what exactly constituted a “COVID death”? And how this compared to more known threats, such as flu?
This BMJ article attempts to do just that, noting the figures never attempted to distinguish between dying from COVID versus with COVID. They also note that this is the first ever pandemic to show zero impact on natural mortality distribution (that is to say, young and old people were affected in exact relation to their existing likelihood of death) and pointed out that, while total deaths did indeed increase compared to the years proceeding, these years actually represented the lowest death tolls on record; the comparison looks particularly different when placed into historical context:
It is of further relevance to the discussion that the UK did experience the highest mortality compared to any developed country, with 60,000 deaths occurring in the three-week window when lockdown was imposed (and the majority shown to be in nursing homes and unrelated to COVID). There were no other countries in Europe that showed excess mortality above seasonal flu rates. Why the disparity? It’s a conversation that is still lacking but, should we ever have it, perhaps it would be sensible to consider the role of the transfer of thousands of infected patients into nursing homes at a time it was made illegal for other residents to leave the home, the same home where they were starved of social interaction, frightened with tales of upcoming death, forced to sign Do Not Resuscitate Orders then over-dozed with Midazolam?
4. Toya & Skidmore – PCR testing has no impact on deaths
The widespread use of unvalidated tests, was another controversial scheme. Many pointed out the huge number of false positives that this produced, and the potential costs on society (being that via mental health issues or inability to earn a living). The ongoing argument – that the benefits of PCR testing did not justify these costs – therefore took a hit with the publication of this paper, demonstrating that there were many factors that determined COVID-19 rates. However, no evidence was found for the rate of PCR testing having a slightest impact on this. Predictably, its only impact was on the case numbers recorded.
5. DAN-MASK study: first RCT finds no benefit for masks
It’s fair to say that there are a lot of opinions when it comes to masks. But there wasn’t any high quality evidence in regards to their effect on the transmission of SARS-Cov2, which was much-needed in light of official recommendations that conflicted with existing knowledge (that the virus was simply far too small for masks to have an effect). Indeed, one oft-repeated argument for mask mandates was that we should apply the cautionary principle until there was high-quality scientific data. Leaving aside the lack of caution espoused by the same people in regards to vaccination, it was therefore very prescient when the DAN-MASK study was complete.
Almost 5,000 people were tracked over two months, with half placed in the mask-wearing group and half serving as controls. They found no statistical difference between the two groups. This added to prior trials on masking for coronavirus threats, such as that done during the MERS outbreak, that already implied this. A further article pointed out the harmful effects of masks and how no evidence exists for their usage. There is a great view article here that summarizes all the available studies, and also covers historical usage of the masks (including the WHO’s long-term statements on how ineffective facemasks are, and their inexplicable U-turn in 2020 without). It is perhaps telling that this study was immediately retracted by the journal, with the main reason provided by the journal that it disagrees with the spreadsheet models provided elsewhere. A shocking state of affairs, that surprises no-one.
A twitter thread recently showed 70 studies that support face masks (it even capitalized the word SEVENTY so we could be clear how many studies) there were. How many of these were on human outcomes in COVID-19? Five. The rest were on different diseases, modelling papers (droplet dispersion, etc), or reviews of these modelling papers. Of these five COVID-19 papers, how many tracked medical outcomes in humans, and not confounded by other interventions going on at the same time, and showed a statistically significant benefit of masks? None. To summarize these five papers:
- One tried to build conclusions from telephone interviews
- Another built a position based on the official case numbers in 18 countries that the decided were ‘mask-wearing countries’, using different 21-day windows for each, ignoring the fact that only 8 of these countries had mask mandates, that the per-capita numbers varied wildly between these countries, the testing rates in each were subject to enormous discrepancies and several were included even though they were yet to record a single COVID-19 death
- Another paper attempted to make conclusions on face mask use while limiting its data to one factor alone: official cumulative cases recorded by 29 March 2020, in 49 countries only. I do not think that this was foul play by the investigators, more a limitation of the early phases, as they clearly state the limitations of these findings: “we were unable to quantitate the intensity of face mask use per country” and “We did not have accurate data to control for these confounders [other pandemic-related policies] and… would have falsely attributed this effect to using face masks”.
- A further paper only found an effect for N95 respirator use, not for mask use (as discussed in the June 2020 paper here)
- The other noted that effects of mask wearing were not significant
Perhaps the whole issue is best summarized by article #9 in this series. The journal article, one putting forward the case for masking, stated: “no direct evidence indicates that cloth masks are effective in reducing transmission of SARS-CoV-2”.
6. Chaudry et al – Lockdown made things worse –
Back in March 2020, Sucharit Bhakti sounded the warning of the lockdown policies. No-one in power wanted to listen. Later on that year, the country went into a second lockdown as the first one had failed. It would then go on to conduct a third lockdown because the second one had failed.
This paper examines the impact of governmental actions and the role of socioeconomic factors in both the illness burden and mortality across the first wave. They find no connection between lockdowns and mortality rate. However, they found that that the biggest associations with a nation’s mortality were rates of obesity rates and social inequality. Perhaps transferring $8 trillion of public funds to the drug companies and closing gyms wasn’t a smart move, after all.
It turns out that they were not the only ones to find lockdown to be a catastrophe. A further 35 papers assessed the value of lockdown and all reached the same conclusion: it provided no medical benefits.
7. China study – asymptomatic transmission is rare
In the early stages of the saga, there were understandable concerns on how COVID was transmitted, with particular concern given to ‘asymptomatic transmission’. Concerns over spread in people without illness were indeed the basis of most lockdown policies. So it was therefore big news when a a March study that showed asymptomatic transmission was incredibly rare. A further study in China on 455 people, no evidence of asymptomatic spread, this added to, which was also the conclusions of an April study. And this one, this one, and this one on school children. Then came this huge Chinese study (which is the focus of this section) on 9.9pm people and PCR testing on 1,174 close contacts. Even the CDC were unable to refute this, with their own study showing the exact same conclusions. Maria Van Kirkhove, head of the WHO’s emerging diseases unit, admits that asymptomatic transmission has not been seen.
She went on to make a confusing climbdown in the days following, stating that sharing what the real-world data was telling us was ‘a misunderstanding’ and said that priority should be given to the models that made different assumptions. It has not been discussed by governments or the mainstream media since.
8. Wolff – flu jabs increase risk of coronavirus
Many have criticized the government’s healthcare policies as overly COVID-centric, pointing out an obsession with the 24th most deadly condition while ignoring the top 23. So-called conspiracy theorists levied accusations that the government were doing the bidding of Big Pharma. Conversely, the various governments remained on message that they simply cared enormously about the health of each of their citizens and saw COVID as the biggest threat since WWII.
Such positions began to ring false when public health policy began to aggressively pursue an increase in flu vaccines. This came in light of existing knowledge on flu vaccines, with a paper showing that flu vaccines reduced the likelihood of flu by 3% but increased likelihood of coronavirus by 36%, a phenomenon dubbed as ‘vaccine-derived virus interference’. This came on the back of previous papers, showing a 65% increase in non-flu respiratory tract infections in children during the fortnight following a flu jab.
This has never been discussed and the UK government continue to push flu vaccines in children.
9. BMJ summary on pre-existing immunity
In this article, Peter Doshi discusses the emerging data on antibody levels found in areas that had been subject to an outbreak but then, most interestingly of all, reviews the six studies into immune reactivity in people with no known exposure to SARS-Cov2.
The studies tended to use held samples (many using those held in storage for several years) and then measures the T cell reactivity when exposed to the novel agent. In the six experiments, reactivity occurred between 20 and 50% of the time.
Such findings indicate three things: that T cell responses may be much more important to the body’s defence than antibodies, that trying to make conclusions on existing herd immunity from the (low) antibody levels is highly flawed and that a significant number of the population are not permissive to infection. None of which sits well with existing policies.
10. Gao et al – Obesity is a major contributor to COVID risk
Perhaps it is not surprising that the Media-Government-Pharma complex aren’t reporting this key study, as it makes a mockery of the policy to ban healthy individuals from visiting the gym or functional health clinics throughout the summer of 2020. And let’s not forget that, right now, if you have not had the experimental jabs, millions of Americans are still banned from visiting the gym.
While the public was already very aware that the elderly were at risk, the study recorded the number of hospitalizations and deaths by demographics, focusing on age groups, existing illnesses and BMI. By tracking the number of excess cases per 10,000 persons in each demographic group, they determined that 77% of hospitalizations in the 20-39 group were explained by BMI, making a huge case for weight control as a central aspect of any health policy that aims to best protect its citizens. Instead, US citizens got free doughnuts with their jab.
Analysis by the American Diabetes Association found that individuals with type II diabetes accounted for 40% of all COVID-related deaths. A further study on 1.5m Swedish men found that higher cardiovascular fitness reduced risk of hospitalization from COVID by almost half. It was further supported by a more recent study that showed that individuals that were physically active were less than half as likely to be hospitalized. Particularly ironic is that the groupings were determined by those ‘meeting official recommendations’ on exercise; of course, the researcher refer to what has been advised for many decades (rather than during the pandemic, where gyms were forcibly shut and governmental advice centred on staying inside).
11. Garcia-Beltran et al – Vaccination can drive mutations
In March, the vaccinologist Geert Van Den Bosche (formerly employed by the Bill and Melinda Gates Foundation to develop vaccines) posted a blog piece in which he warned that vaccinating an entire population was a terrible idea and guaranteed to worsen the situation. He pointed out that a vaccine based on a single sub-unit of a single protein meant that very little mutation was necessary for strains to emerge that now had a huge competitive advantages over their ‘wild-type’ equivalents, and that this would see an initial positive effect from the rollout followed by a surge in infections amongst the vaccinated (whose immunity was based on antibody neutralization of the spike protein, something that the newer strains were better at evading). His viewpoint was pilloried across the internet by commentators who claimed he was making a straw-man argument and that he was spreading antivaccine pseudoscience.
The same commentators said little when the above paper came out, which showed that the virus could easily mutate when exposed to the vaccines. They remained silent when a further paper assessed the waning effectiveness of vaccines, noting that the Delta strain was poised to become completely immune to vaccination. Since the scenario that Van Den Bosche predicted has come to pass, the silence has been deafening.
While its important to note that accurate predictions do not automatically mean the reasoning is correct, it bears to reason that that best time to discuss these ideas was March and the second best time is now.
12. The Israeli paper – vaccinated 27x more likely to be hospitalized compared to naturally immune
Arguably one of the most pivotal studies in the COVID story, this was the first to actually investigate the idea that natural immunity may be better than that provided by vaccination. The researchers did nothing complex; they tracked 12,000 previously-infected individuals and then compared them to a group of 12,000 that were vaccinated and matched for age and other demographics. They followed each group from June 1 to August 14th.
What they found was startling: the vaccine group were 13x more likely to contract COVID and 27x more likely to be hospitalized. It turns out natural immunity is not a conspiracy theory.
13. CDC Masachussetts Study – vaccinated more likely to be reinfected/hospitalized versus unvaccinated
This paper was released into a climate of increasing doubt over the vaccination program, coming hot on the heels of various analyses of the publicly available information that showed that the vaccination were over-represented in both cases, hospitalizations and deaths.
The researchers tracked a cohort of 469 individuals in Masachusetts over the course of July. Of the cohort, 69% were fully vaccinated and 31% were classified as ‘partially vaccinated, unvaccinated and unknown”. They found that 74% of infections and 80% of hospitalizations occurred in this fully vaccinated group. Obviously questions remain as to how much the partially vaccinated contributed to the total infection burden (the researchers chose not to report this).
The fact that the median age for these infections was only 40 years old meant that such findings could not be swept away with the go-to explanation that had previously been deployed (that the only reason that the vaccinated were over-represented in cases and hospitalizations was because, due to the staged rollout, the vaccinated contained more of the elderly). Another huge problem for the CDC was that the paper measured the viral load in both the vaccinated and unvaccinated and found it to be the same.
Naturally, a CDC paper that indicates the CDC’s policies are misguided makes for awkward conclusions on the future of the vaccination program. What conclusion did the researchers come to? “Everyone should wear masks”.
14. UK Household data – no impact of vaccination on infections or viral load
This research group assessed the effectiveness of vaccines against the varying strains of COVID, using the data gathered from the ongoing population-wide monitoring projects (where households that signed up provided regular PCR testing). They reported that, in the Alpha-dominant period, 88% of infections were found in those that were both unvaccinated and not previously infected. Vaccination and prior infection (without vaccination) both appeared to provide excellent protection, making up just 0.5% and 0.6% of cases, respectively.
There was quite a contrast in the figures reported for the current, Delta-dominated period. Now they find that only 17% of infections were found in those that are both unvaccinated and not previously infected. Prior infection (without vaccination) continues to provide excellent protection (with this group making up just 1% of cases) but this is not the case for vaccination, with the fully vaccinated group over-represented in the number of infections (75-83% of the population during this time, contributing 82% of all infections). The unvaccinated actually showed lower viral loads than the vaccinated (median IQR 25.7 versus 25.3).
If ever there was a significant finding, this was it. Yet the authors seemed intent on burying this finding into a cluttered figure-laden paragraph (beneath Figure 3), before quickly moving on to discuss pretty much every other finding except this one. The paper features a total of 12 figures and 6 tables, yet not a peep in any of these as to these figures. Instead, these visuals choose to prioritize reporting on the vaccine effectiveness at 14 days. Great if you only have 14 days to live and want to avoid COVID in this time, bizarre choice of reporting for everyone else.
What was their conclusion on the lower viral load in the unvaccinated? “Differences in Ct values between those unvaccinated … and after second vaccination had attenuated substantially.” New-science speak for vaccines do not reduce viral load in Delta varients. And their conclusion on the vaccinated being over-represented in total infections? “There may be implications for any policies that assume a low risk of onward transmission from vaccinated individuals”. New-science speak for the vaccines offer no protection against Delta variants.
15. Kariko et al – mechanisms of mRNA technology and immunosuppression
While there have been calls for Nobel prizes for the scientists that made mRNA injections a feasible technology for humans, little discussion has been conducted as to the method that culminated in such an achievement. As the landmark paper in question explains, the scientists set out to overcome the natural instability of mRNA vectors in the human body and eventually managed to do so… by modifying the mRNA so that it suppressed T cell function. T cells are central to the body’s antiviral defences.
This should not be taken as a dismissal of the potential utility of rRNA technology or mRNA vaccines. As a potential therapeutic, it has exciting potential (even if understanding is still in its infancy). However, it serves to point out that the human body is complex and products that intervene in multi-layed biological processes may benefit from being tracked. How many doctors do you know that are running immune panels on their patients after administering treatment?
16. BMJ summary on the ‘post-first jab’ spike
It was seen in the original Pfizer study, then again in a real-world analysis from Israel, then again in the ONS population data from England, then again in a Danish study. Others followed. What did they all show? A ‘post first jab spike’ in infections, where a clear pattern of increased infections is seen following the first dose, with increases consistently coming in at 40-63% over the first nine days.
Particularly disappointing is the complete absence of discussion of this effect. Even more so is the way that such effects are hidden by the way that official data is compiled (with all infections and deaths that occur within the first 14 days of vaccination being reported as occurring in the unvaccinated), with this invalidated method also being adopted in the scientific literature (as I covered in Part II).
17. Rose & Crawford – how many deaths have really occurred from the vaccine?
This report is from the US Government’s website, and features an attempt by Jessica Rose to provide an accurate figure of the deaths from the COVID vaccines so far. In this paper, she notes the extreme absence of any reliable data on side effects of vaccines (and any attempt by official institutions to obtain this).
In response to the data gap, she cross-references the available sources (such as the VAERS database and calculations on the rate of underreporting, background data on overall deaths from the US and the EU, autopsy studies on from Norway and Germany) and notes that each source points to a figure near 150,000 deaths.
The report has been heavily criticized by mainstream sources on the basis that this is only an estimate and is made on flawed reporting system. However, this is the very point made by Rose in the report, who makes the same point to call for governments to actually track the side-effects. Meanwhile, the CDC have made changes to their reporting of cases amongst the vaccinated to make it much more difficult to track side-effects. On our side of the pond, the MHRA have so far refused to investigate the record number of Yellow Card reports made.
18. Cleveland Clinic study – natural immunity is superior
This was a study conducted on 52,000 employees of the Cleveland Clinic, tracked over five months. The researchers aimed to work out how effective natural immunity is against SARS-COV2 and so, to do this, they tracked how many tested positive for the virus and then how many tested positive in the months following. It turned out that 1,359 tested positive during the study. How many of them tested positive in the remainder of the study? None. Not one. Zero percent.
The study was backed up by the findings of Irish academics, who did similar research on 615,000 individuals over 10 months and found the reoccurrence rate to be 0.1%. This also tallied with the figures reported by the Israeli Health Agency, based on 835,000 people over six months. Twitter may have banned use of the term #naturalimmunity, but it turns out that it’s not a myth. This article explores 29 different studies that help us understand the role of natural immunity in COVID-19.
19. Mitchell & Naryshkin – Why did poor, Sub-Saharan African countries do so much better than rich, Western countries?
One of the most fascinating contributions to our understanding so far, not just for the subject matter but also for the dramatic results that it reports. The researchers’ primary focus was on the interplay between malaria and COVID (and, thus, any anti-COVID effects of anti-malarial drugs) but the results obtained allowed for an even more complete narrative that relates to many populations across the globe.
The researchers initially focused on six Sub-Saharan African countries with high malaria rates, their interest piqued in May 2020 by a) the declaration of many ‘experts’ that these countries would be devasted by COVID and b) the reality, where no such disaster came close to materializing.
This research paper tracked these six countries and compared them to four developed/Western countries (Spain, Italy, US and UK) over the 16 months that followed. They note that there are a lot less geriatrics in the Sub-Saharan nations, and therefore performed an age-adjusted adjustment. However, even with this adjustment (that reduced the difference six-fold), it was evident that Western countries experienced 20x more deaths. They undertook further assessment and found strong negative correlations between the rate of malaria (which is a crude measure for anti-malarial use) and the COVID death toll
While the authors were careful to cross-reference all data against both Governmental and independent sources (using John Hopkins both CSSE as further reference points), it is worthwhile noting that these findings are still correlations, and we should not confuse correlation for causation. However, the paper conducts a thorough investigation into the mechanisms and real-world evidence for various anti-malarial agents, with findings remaining consistent across a wide range of studies and multiple countries (including the c19 study, which involved 1.8m people and saw a 70% reduction in mortality).
All in all, this paper demonstrates uses hard outcomes to inject a huge amount of interesting discussion points into our conversations. Which is why I suspect it will be ignored.
20. Subramanian & Kumar – No correlation between vaccine update and death rates
I found it ironic that, on 30 September, I had a podcast discussion with Dr Purvi Parikh about COVID. In it, I pointed out that, for all the bluster and headlines, we were not seeing a drop in death anywhere using the CDC’s official data. This showed that, while official COVID numbers often drop across the country, there is an equal rise in the number of ‘unclassified’ deaths (column O in the downloadable Excel file). It turned out that, as the discussion was taking place, this paper was released to clarify the exact questions raised.
It notes that, of the five most vaccinated counties, four are considered ‘High transmission’ areas by the CDC. Conversely, the 57 counties that are considered ‘Low transmission’ areas are over-represented by counties with vaccination rates below 20%. It also compares the number of cases across 2947 countries to their vaccination status, and finds no correlation. It then does the same for 68 countries around the world, again finding no correlation:
This paper is reporting correlations, and is therefore always going to be limited. Correlation is not causation. But correlations are a fantastic starting point for further investigation. Why is this not being discussed, let alone investigated?
21. Lancet paper – Excess of 50 million people suffering from depression
Another paper that shows the folly of governmental policies, another paper that has been widely ignored by the mainstream media (the same people who insist that recent measures are ‘all about health’). The paper notes that mental health conditions were already the biggest driver of the global health burden, and that this represents an increase of 27.6% in depression. The paper is particularly pertinent in the light of some of the above papers, which clearly show that lockdown served no medical benefits and that current vaccination-based policies have no impact on deaths.
There is a wealth of studies here that give us insight into topics that have had unprecedented impact on our lives. No single paper should ever be extracted as ‘proof’ of a particular point, but instead held up against the bigger picture so that due consideration can be given to what it means. In a healthy setting, different viewpoints may exist as to this meaning, which is where discussion and analysis allows us to iron out any conflicts that may remain and come together with a coherent idea of how to resolve challenges we face. This is not happening.