A fully-referenced evidence-based review of the testing, health risks, and precautions associated with COVID-19
My passion is evidence-based thinking and decision-making.
I am a Physics graduate from University College London (UCL) with roughly five years’ worth of professional expertise in research and data analysis. In particular, I have studied, applied, and taught skills relating to open-source intelligence gathering and critical analysis.
Having dedicated hundreds of hours towards analysing data from the Office for National Statistics, the NHS, Public Health England, and more, and having sifted through thousands of pages of scientific literature, one thing is clear:
The UK government, along with many news organisations, have consistently presented dishonest and unscientific information on COVID-19.
Mainstream understandings are now riddled with fear and misinformation.
I know scientific evidence alone will not change people’s beliefs, but my hope is that it can spark a shift away from excessively cautious behaviours towards those which are far more sensible and loving.
- “PCR is just a process that’s used to make a whole lot of something out of something… It doesn’t tell you that you’re sick and it doesn’t tell you that the thing you ended up with is going to hurt you.” Kary Mullis, awarded the 1993 Nobel Prize in Chemistry for inventing the PCR test.
- Current mass-testing protocols are resulting in extremely significant proportions of false-positives. For example, 35–100% of all positive results regarding COVID-19 admissions to hospital in November were false.
- The mass deployment of PCR testing has created the false appearance of an epidemic in the past — a “pseudo-epidemic”.
- Many relevant experts have expressed concerns over the response to COVID-19, including the implementation of mass PCR testing.
- Fever, cough, and fatigue are among the most common symptoms of COVID-19.
- Estimates from various systematic reviews suggest that around 15–20% of infections are asymptomatic. This contrasts with current UK government suggestions that one in three infections may be asymptomatic.
- Asymptomatics are significantly less likely to spread the disease.
- Long-term immunity is almost certain following COVID-19 infection, and the risk from new strains is very low.
- The median infection fatality rate for COVID-19 in under-70s is 0.05% — lower than the flu.
- Since mid-May, all-cause mortality has been below levels we experience in an average January.
- The first national lockdown appears to have had little to no impact on COVID-19 infections, hospitalisations, or deaths.
- The costs of lockdown are likely to exceed by far any benefits gained, including lives lost.
- The preponderance of evidence suggests that the use of surgical masks has little to no effect on the spread of infectious viral material in real-world settings. In addition, cloth masks may be significantly worse than no masks.
- According to official Pfizer-BioNTech vaccine trial documentation, there is insufficient evidence to suggest that the vaccine is more effective than two placebo shots at reducing the risk of severe COVID-19 infection or death from COVID-19.
SARS-CoV-2 is the name of the virus which causes the disease COVID-19.
Section 1: Testing
Polymerase Chain Reaction (PCR) tests
“PCR is just a process that’s used to make a whole lot of something out of something… It doesn’t tell you that you’re sick and it doesn’t tell you that the thing you ended up with is going to hurt you.”
Kary Mullis, awarded the 1993 Nobel Prize in Chemistry for inventing the PCR test.
What are PCR tests and how do they work?
PCR tests represent the primary method being used to detect the presence of SARS-CoV-2 in patients around the world. In this context, they work as follows:
A swab, or sample, is taken from the back of a patient’s nose and/or throat. This sample is processed such that genetic material specifically associated with the SARS-CoV-2 virus is amplified. This amplification process is cyclical, whereby the genetic material is doubled with every cycle. The number of cycles required to amplify the viral material beyond a specified threshold is known as the cycle threshold (Ct) value. If the Ct value is reached within a certain number of cycles, sometimes referred to as the Ct limit, the sample is considered a positive result.
In other words, samples containing more viral material will result in a lower Ct value.
Limitations regarding the use of PCR tests in diagnostics and policy-making
- PCR tests offer the capacity to detect viral material in very small quantities. However, the test cannot explicitly determine whether this viral material is infectious or not.
- If a high Ct limit is used, the testing process will be skewed towards detecting non-infectious levels of viral material.
- PCR tests are not 100% accurate, since results can include false-positives (the false detection of viral material in the original swab) and false-negatives (the false omission of viral material in the original swab).
How have these limitations been addressed in the UK?
In scientific research, results cannot be effectively considered unless they are published alongside their associated error margins. However, neither SAGE nor the UK Government have provided details on the false negative/positive rates regarding the current mass usage of acquired PCR testing kits.
Estimations have been acknowledged in a government report titled “Impact of false-positives and false-negatives in the UK’s COVID-19 RT-PCR testing programme”, although the extent to which they are being considered seems minimal.
Furthermore, according to a Public Health England (PHE) document titled “Understanding cycle threshold (Ct) in SARS-CoV-2 RT-PCR”, a maximum of 40 cycles should be used.
Assessing these limitations based on scientific literature
This means that labs following PHE’s guidance are using a Ct limit significantly higher than that which is associated with infectious disease, and as such are skewing results towards false-positive diagnoses. In addition, anyone tested after eight days of illness is likely to test positive despite a very low probability of infectiousness.
Definitions of re-positive cases vary. However, evidence suggests that people who re-test positive for SARS-CoV-2 after having previously suffered from COVID-19 are extremely unlikely to be suffering from active re-infection.
This is supported by evidence which shows that immunity is almost certain following COVID-19 infection.
In other words, re-positive cases are almost certainly not infectious.
External quality assessments have been conducted regarding the false-positive rates in the detection of SARS-CoV-2, in addition to other RNA viruses.
In real-world settings, the false-positive rates for SARS-CoV-2 were found to be 0.3–3%, although evidence remains limited. In lab settings, the false-positive rates for SARS-CoV-2 were generally consistent with those associated with other RNA viruses, for which the median false-positive rate was 2.3% and the interquartile range (IQR) was 0.8–4.0%.
False-negative rates in the detection of SARS-CoV-2 have been found to vary over time, decreasing from 100% on day 1 of infection to 38% on day 5 (average day of symptom onset), to a low of 20% on day 8, after which the false-negative rate gradually increases again.
On average, most tests may be occurring on day 8, although evidence quality is low.
Estimating the impact of false-positives and false-negatives
The significance of these errors is dependent on the prevalence of the virus within the population being tested. If the positivity rate is low, the significance of the false-positive rate is high and the significance of the false-negative rate is low.
In the UK, as of 19 December 2020, the positivity rate was approximately 9%.
Using the IQR of 0.8–4.0% for the false-positive rate, and the 20% false-negative rate, we can estimate that false-positives currently account for roughly 8–40% of all positives, and false-negatives account for roughly 1–2% of all negatives (see Appendix 1 for calculation details).
However, the positivity rate hit a low of 0.5% in July, suggesting that the false-positive rate cannot be higher than 0.5%, compared to the 0.8–4.0% estimated above. This contradicts expectations that the false-positive rate should be higher in current real-world settings, due to the huge number of swabs being taken and handled by untrained people, and tested in inconsistent and sometimes inadequate lab conditions.
Explanations as to why the false-positive rate is higher and more significant now compared to early 2020
There are several indicators which suggest that current false-positive rates are significantly contributing to false COVID-19 diagnoses, including:
- There are virtually no admissions to hospitals and ICUs due to influenza, contrary to equivalent periods in previous years, suggesting influenza cases are being misdiagnosed (precautions are unlikely to be causing low influenza rates — see Section 3 for more information).
- The proportion of incorrectly categorised COVID-19 admissions increased to over 85% at Imperial College Healthcare NHS Trust (ICHT) in May, although the extent to which this occurred nationally and continues to occur is unknown.
- All emergency admissions are being tested for COVID-19, regardless of symptoms. Using data from the NHS, which shows that there were roughly 10,000 COVID-19 admissions out of 451,800 emergency admissions in November, the same false-positive calculations can be conducted as before — these suggest that 35–100% of all COVID-19 admissions in November were false. This finding is backed by the fact that critical care bed occupancy levels are average for this time of year (November to March), as are mortality levels.
- Deaths where COVID-19 was mentioned on the death certificate currently account for over 200% of current excess deaths. However, during the first peak, COVID-19 deaths accounted for roughly 75% of excess deaths. In other words, COVID-19 deaths appear to be over-represented in all-cause mortality figures, suggesting that they are being misdiagnosed.
The hypothesis that the false-positive rate is higher now than in April is not only supported by the above statistics.
The previously referenced evidence review by Andrew N. Cohen et al. found that contamination and human error are likely to be primary causes of false-positives, which would help to explain why the false-positive rate may not only change based on viral prevalence, but also could be higher now compared to earlier in the year…
Testing capacity has increased by more than ten-fold, largely through the creation of large private testing labs — a.k.a. Lighthouse Labs — rather than via the NHS, and concerns have been raised regarding the training, processes, and workloads of those working at these Lighthouse Labs.
Furthermore, there have been cases in the past where the use of mass PCR testing in response to a disease has created the false appearance of an epidemic — a “pseudo-epidemic”.
What do the “experts” say?
This dangerous misrepresentation of expert opinion is driving misinformed understandings across the country.
Furthermore, the models being used by the UK government to estimate the spread and impact of COVID-19 have consistently and significantly over-estimated the severity of the disease.
While PCR tests are being used to detect the presence of viral material in swabs, antibody tests are conducted to detect whether a person has antibodies against the virus in their blood. The presence of antibodies suggests that a person was infected by COVID-19, and is now immune.
However, accuracy issues have been found regarding the AbC-19 Rapid Tests acquired by the UK government, showing that in real world settings, they fall well short of performance claims made by their manufacturers.
Nonetheless, antibodies are not the only indicator of a previous infection or current immunity, particularly as most long-term studies found that antibody concentrations waned over time following SARS-CoV-2 infection, as is the case with other coronaviruses.
T cells are also part of the immune system, and studies suggest that all or a majority of people with COVID-19 develop a strong and broad T cell response. Antibody testing alone may underestimate the true prevalence of the infection or population immunity — one study has shown that 93% of “exposed asymptomatic” individuals had a T cell response to SARS-CoV-2, despite positive antibody detection in only 60% of cases.
Section 2: Health Risks
Symptoms and transmission
Fever, cough, and fatigue are among the most common symptoms of COVID-19, with myalgia (muscle pain) or arthralgia (joint pain) and olfactory dysfunction (affecting smell) or gustatory dysfunction (affecting taste) also common.
The mean incubation period — time between infection and symptom onset — appears to be roughly 5 days, with over 95% of those who develop symptoms doing so within 12 days.
Estimates from various systematic reviews suggest that around 15–20% of infections are asymptomatic, although this proportion can range from 5–80%. The proportion of asymptomatic infections in children is much higher than other age groups.
This contrasts with current UK government suggestions that one in three infections may be asymptomatic.
Furthermore, increased household exposure to young children is associated with a lower risk of testing positive for SARS-CoV-2 and lower severity of illness.
Evidence suggests that fomites — objects contaminated with infectious material — may not play a significant role in the transmission of COVID-19 in real-world settings, especially when good hygiene standards are maintained.
As such, and given that immunity is almost certain following COVID-19 infection, transmission from people who have already overcome infection is highly improbable.
SARS-CoV-2 is an extremely stable virus — much like other coronaviruses — mutating at around half the rate of influenza, so vulnerability to new strains is unlikely.
Based on data from the Office for National Statistics (ONS), the average age of death from COVID-19 in the UK has been 82.4 years, compared to the average age of death from all other causes which has been 81.5 years.
There is a strong correlation between old age and increased risk of death from COVID-19. One systematic review showed that the infection fatality rate (IFR) can be described as follows:
- 0–15 years, IFR is below 0.001%.
- 15–30 years, IFR is below 0.01%.
- 30–50 years, IFR is below 0.1%.
- 50–70 years, IFR is below 1%.
- 80+, IFR is below 10%.
Another systematic review found that the median IFR across 51 locations is 0.23%, although in people less than 70, the median IFR rate is 0.05%. This review also suggested that the number of people who have been infected with COVID-19 may be over 15-times higher than the number of lab-confirmed cases around the world.
These IFRs suggest that COVID-19 is less severe than the flu for under-50s, but more severe than the flu for the elderly.
The comorbidities most commonly associated with increased severity risk are hypertension, diabetes, and respiratory diseases, although other risk factors such as circulatory disease and obesity are commonly discussed.
Other risk factors
Increased risk of COVID-19-related death has also been associated with being male, and being non-white. Adjusting for relevant clinical and social indices does not fully account for the observed difference in mortality between white and black patients.
However, an evidence review showed that around 20–50% of people have pre-existing resistance to COVID-19.
Due to the previously discussed inaccuracies regarding PCR tests and COVID-19 diagnoses, COVID-specific death data may not present an accurate picture of current mortality risks.
All-cause mortality figures not only allow for better comparison between countries, as they avoid the problem of countries recording COVID-19 deaths in different ways, but also consider the deaths indirectly caused by the pandemic.
Furthermore, comparing age-standardised mortality rates across selected cities can reduce analytical challenges that arise from differences in population densities and age demographics.
Based on data from the ONS, mortality rates in 2020 can be summarised as follows:
- From mid-March to mid-June, there were approximately 60,000 excess deaths in England and Wales compared to the same period in recent years. However, deaths of care home residents accounted for around 50% of these additional deaths, a disproportionately high representation.
- Since mid-May, all-cause mortality has been below levels we experience in an average January.
- Through December, excess mortality levels were at only 10% of the peak reached in April.
Section 3: Precautions
The purpose of lockdown is not to stop people from getting infected, but to delay the spread of a virus in order to protect healthcare services from being overwhelmed; or, as the UK government has repeatedly said, to “save the NHS” and “save lives”.
To what extent have UK lockdowns been successful?
The first national lockdown appears to have had little to no impact on COVID-19 infections, hospitalisations, or deaths. Figures suggest that these metrics were decelerating (i.e. beginning to peak) long before lockdown could have had an effect.
The spread of the virus also appears to have followed a Gompertz curve, suggesting no external influencing factors.
Recent lockdowns and restrictions also appear to be having minimal impact on mortality rates.
Comparisons between similar US states which implemented restrictions of differing severity show no obvious differences in excess deaths (e.g. California vs. Florida, North Dakota vs. South Dakota).
Similar, albeit imperfect, observations can be seen comparing cities and countries across Europe.
This does not mean lockdowns cannot work…
However, their efficacy relies on many variables including strictness of measures, compliance and/or effectiveness of enforcement, timing of implementations, success in infection tracking, and more.
According to evidence previously discussed regarding false-positives and hospital activity, current challenges being faced by the NHS are likely to be due to patients being wrongly restricted to COVID-19-specific areas in hospitals, leading to unnecessarily overwhelming concentrations of patients.
Thus, not only is the efficacy of UK lockdowns unevidenced, but so too is the need for them.
The government has so far failed to publish an impact assessment on lockdowns.
One has been produced by Philip Thomas, Professor of Risk Management at the University of Bristol, which suggests that the additional deaths due to national impoverishment will by far exceed those due to COVID-19.
In addition, an evidence review was conducted by the Joint Committee on Human Rights (JCHR), in which current regulations were described as “the most wide-ranging restrictions on individual liberties, affecting the greatest number of people, since the Defence Regulations made during the Second World War,” and “some of the most sweeping powers seen in modern times… interfering with human rights on an unprecedented scale.”
This review raised human rights issues regarding the treatment of care home residents, students, prisoners, those being prosecuted under new regulations, data protection rights, and more.
Furthermore, current lessons learned reviews being undertaken by the government were deemed to be insufficient, particularly regarding investigations into deaths in health and care settings.
Of four observed systematic reviews on the efficacy of different face masks in real-world settings, only one suggested that face masks offer protection from clinical respiratory illness (CRI) and influenza-like illness (ILI).
The other three systematic reviews, along with a large randomized controlled trial in Denmark specifically relating to mask-protection from COVID-19, concluded there is weak to no evidence that surgical masks effectively protect the wearer from infectious viral material or that they reduce the transmission of infectious viral material.
Another randomised control trial found that the use of cloth masks was associated with significantly higher rates of infection compared to surgical masks and N95 respirators. In combination with the referenced systematic reviews, this suggests that the use of a cloth face mask is worse than no mask at all.
The Pfizer-BioNTech vaccine
Official trial documentation explains the following:
- There is evidence to suggest that the vaccine is more effective than placebo at reducing the risk of symptomatic COVID-19 infection.
- There is insufficient evidence to suggest that the vaccine is more effective than two placebo shots at reducing the risk of severe COVID-19 infection or death from COVID-19.
These are not my personal interpretations of the data. The document explicitly states whether the vaccine did or did not meet the success criteria for these endpoints.
In addition, there are several legitimate concerns about the trial methodology, including:
- There were 3,410 cases of suspected COVID-19 infection that went unconfirmed — 1,594 in the vaccine group vs. 1,816 in the placebo group. This means that they presented with COVID-19 symptoms but were not tested. The reasoning and decision-making behind these cases is not explained, but they offer the potential to impact the trial results significantly.
- Pfizer-BioNTech propose that the placebo participants should be offered the vaccine after six months, which would substantially prevent long-term assessments regarding the efficacy of the vaccine.
- Various other concerns have been raised regarding trial design and interpretation of results, as outlined by Peter Doshi in the BMJ. In addition, Dr Wolfgang Wodarg and Dr Mike Yeadon have been petitioning against the rapid rollout of the Pfizer-BioNTech vaccine, largely due to safety issues that have arisen in previous coronavirus vaccine trials which have been unaccounted for in the Pfizer-BioNTech trials.
- Industry-funded trials are notoriously biased. Given the various concerns regarding the Pfizer-BioNTech trials, prudency seems like the most appropriate stance.
Section 4: My Personal Views, Based on Scientific Evidence
- Government press conferences and mainstream news should not be taken seriously. Their consistently dishonest presentation of COVID-19 and its impact on society has been extremely damaging — economically, physiologically, and emotionally.
- Being healthy is a good thing, so don’t be scared of infecting others if you are asymptomatic or if you have previously suffered from a combination of COVID-19-related symptoms and have had a positive test. In such circumstances, the risk of being infected with COVID-19 is extremely low, the risk of being infected asymptomatically is even lower, and the risk of asymptomatically infecting others is lower still.
- Don’t get tested unless you are extremely sick with COVID-19 symptoms. Mass testing and subsequent false-positives are almost certainly creating the appearance of an epidemic when there isn’t one, driving unnecessary fear, irrational behaviours, mismanagement of health services, and increasing risk by far more than is being mitigated.
- Stop wearing masks. There is no evidence to suggest that mask-wearing protects wearers from viral infection, or reduces the transmission of viral infection. On the contrary, there is evidence to suggest that cloth face masks significantly increase the health risks to the wearer and the people around them.
- Don’t rush to get the vaccine — the need for the Pfizer-BioNTech vaccine remains unclear, as does its safety and efficacy. We should not indulge bad medical science, particularly when it means putting ourselves at risk when there is no clear need to do so.
We have overcome far deadlier diseases, including other coronaviruses, without the need for radical restrictions or medicines.
It is tragic that people have been made to feel so scared of COVID-19.
Loved ones have not been embraced for months, friendships and families have been strained, and livelihoods have been ruined, all because of this irrational fear.
But we can make 2021 the year of science, sense, and kindness.
I know this topic has fuelled passionate views on all sides, but we need to try and put our emotions and our pride to one side, for the sake of our amazing society.
Please share this.
Appendix 1: False-Positive Calculations
Given a positivity rate of 9%, per 1,000 tests we would see 90 positives and 910 negatives.
Given a false-positive rate of 0.8%-4.0% and a false-negative rate of 20%, the following simultaneous equations can be solved to estimate the number of real positives and negatives:
For a false-positive rate of 0.8%…
90 = P + 0.008N
910 = N + 0.2P
where P represents the number of real positives, and N represents the number of real negatives.