Pandemic Addendum: 13-Sep-2020

Like many, concern about the Covid-19 pandemic started in Jan-2020 as reports about a new Covid-19 virus spreading from China started to become mainstream news. The first death in the UK was reported on 31-Jan-2020, after which some preliminary analysis was based on the statistics being collected by a website called The statistics of infections and deaths between March and early April was collated and presented on the opening page of the analysis – see The Covid-19 Pandemic. This addendum will start in a similar fashion by providing a comparative statistical update from the same website, dated 13-Sep-2020.

Rank Country Population Infections % Deaths %-Inf %-Pop
Global 7,700,000,000 28,944,152 0.38% 924,577 3.19% 0.012%
1 USA 329,227,746 6,676,601 2.03% 198,128 2.97% 0.060%
2 India 1,352,642,280 4,754,356 0.35% 78,614 1.65% 0.006%
3 Brazil 212,347,956 4,315,858 2.03% 131,274 3.04% 0.062%
4 Russia 144,438,554 1,057,362 0.73% 18,484 1.75% 0.013%
9 Spain 50,800,000 576,697 1.14% 29,747 5.16% 0.059%
13 France 67,000,000 373,911 0.56% 30,910 8.27% 0.046%
14 UK 67,772,000 365,174 0.54% 41,623 11.40% 0.061%
20 Italy 60,500,000 286,297 0.47% 35,603 12.44% 0.059%
21 Germany 82,800,000 260,546 0.31% 9,427 3.62% 0.011%
38 Sweden 10,343,403 86,505 0.84% 5,846 6.76% 0.057%
69 Ireland 4,947,870 30,730 0.62% 1,783 5.80% 0.036%

The table above focuses on the reported infections, but now shows the deaths as both a percentage of the infections and as a percentage of the population. It can immediately be seen that there is a marked variance in the percentage of deaths relative to infections across the countries shown, which is far less obvious when deaths are presented as a percentage of the population. This suggests that the inaccuracy lies in the number of infections, where it is suspected that all countries have grossly under estimated the actual number of infections in the population, due to initial limited and unreliable testing for the virus. It is now suspected that potential infections, and associated immunity, may have been much higher than the statistics suggested, which is an issue to be considered later in this addendum. Finally, the green colour bars highlight two countries, i.e. Brazil and Sweden, that did not enforce a ‘lockdown’, and can be compared to the UK, orange bar, that did. Again, comparing the percentage deaths per population suggests no obvious benefit has been accrued from the lockdown policy in the UK. In fact, the original analysis under the headings All-Cause Mortality and Data Models and Data Reality suggested that the UK lockdown may have caused more excess deaths than can be attributed to the Covid-19. We might now complete the statistics provided by the website in the next table.

Rank Country Population Active % Recovered % Serious %
Global 7,700,000,000 7,426,865 0.10% 20,592,710 0.27% 60,885 0.0008%
1 USA 329,227,746 2,528,119 0.77% 3,950,354 1.20% 14,366 0.0044%
2 India 1,352,642,280 973,147 0.07% 3,702,595 0.27% 8,944 0.0007%
3 Brazil 212,347,956 631,163 0.30% 3,553,421 1.67% 8,318 0.0039%
4 Russia 144,438,554 165,343 0.11% 873,535 0.60% 2,300 0.0016%
9 Spain 50,800,000 0.00% 0.00% 1,136 0.0022%
13 France 67,000,000 253,942 0.38% 89,059 0.13% 635 0.0009%
14 UK 67,772,000 0.00% 0.00% 79 0.0001%
20 Italy 60,500,000 37,503 0.06% 213,191 0.35% 182 0.0003%
21 Germany 82,800,000 15,819 0.02% 235,300 0.28% 233 0.0003%
38 Sweden 10,343,403 0.00% 0.00% 13 0.0001%
69 Ireland 4,947,870 5,583 0.11% 23,364 0.47% 9 0.0002%

As it has been suggested that the number of infection cases is probably being grossly under estimated, the percentage figures shown in the table above are relative to population, not infections. Even so, we might still have to question the accuracy of these figures; especially as countries like the UK do not seem to know the number of active and recovered cases. In addition to the accuracy concerns raised above, the number of deaths, directly attributable to Covid-19, might also be questioned based on the suspicion that many deaths attributed to Covid-19 only reflects the potential presence of the virus at the time of death and not the actual cause. For example, Professor Walter Ricciardi, scientific adviser to Italy’s minister of health, has stated that possibly only 12% of death certificates show a direct causality to the coronavirus, such that 88% of deaths were actually attributable to one or more pre-existing morbidities. If so, the average 0.06% deaths per population in the first table might be reduced to 0.007%, which might then be compared to the 1% of the population who might be estimated to die, on average, in a given population, i.e. less than 1% of 1%. Of course, this analysis might rightly be challenged as not being authoritative, such that other sources of supportive evidence possibly needs to be tabled at this point.

Note: The first source of information to be cited is a video by Ivor Cummins entitled ‘Viral Issue Crucial Update 8-Sep-2020’. Ivor Cummins has published many informative videos on his YouTube channel, which only cites official data, although possibly subject to more analytical rigour than normally found on mainstream media.

While the reader should first review the video for themselves, this discussion will also make some additional comments starting with the graph displayed at the beginning of the video and reproduced below, but now revised to 16-Sep-2020.


This graph shows the Covid-19 deaths estimated per million, which this discussion will translate into the UK population. Based on the chart above, the 14 deaths per million for the UK in Apr-2020 would translate to a peak of 938 deaths for its 67 million population, which had fallen below 1 death per million, or 67 deaths per day, by 13-Aug-2002. However, based on later NHS data, the number of total deaths on 12-Sep-2020 was 2, not 67, such that we possibly need to revise the risk assessment. On average, about 1% of the UK population die every year, where in 2018, this figure was 637,000 or 1745 per day. Therefore, the peak UK 938 daily deaths would correspond to about 53% of the 1745 daily average, but if now fallen to 2, might be better compared to influenza deaths in the UK. Currently, as of 13-Sep-2020, the website is reporting an accumulated total of 41,623 UK deaths, which is about 6.5% of the average yearly deaths in the UK, but where the Covid-19 virus might simply have been present and not necessarily the cause of death. Given the questioning of the accuracy of Covid-19 deaths directly attributable to the virus and not the effects of age, immune deficiencies and co-morbidities, the figure of 41,623 could reasonably be revised downwards, such that they are comparable to many influenza seasons in recent years. However, given some of the concerns expressed about UK statistics, we might make reference to the situation in Ireland and the data produced by the Health Protection Surveillance Centre, see next chart. Based on the numbers in the chart below, there have been a total of 1,777 Covid-19 deaths in Ireland. However, 1,677 of these deaths had 1 or more comorbidities, where the median age of those who died, i.e. 84, was 2 years older than the average age in Ireland, i.e. 82.

However, it might be suggested that only 100 (1777-1666) deaths might be directly attributed to Covid-19, i.e. 5.6%, which while appreciably lower than the Italian suggestion of 12% might still be a more accurate risk assessment than assuming all 1777 deaths were caused by Covid-19. It might also be highlighted that the total number of deaths in Ireland in 2014 was estimated to be 29,368, which is 0.59% of its 4,947,870 population. As such, even the figure of 1777 Covid-19 deaths would only amount to 6% of the yearly deaths, while the reduced figure of 100 covid-19 deaths would correspond to 0.0034%.

Note: When the Covid-19 pandemic was first perceived back in Feb-2020, there was a plausible justification for precautionary measures to be enforced, e.g. lockdowns. However, subsequent official statistics questions the efficacy of such policies to change the course of the ‘pandemic’. In fact, differentiating Covid-19 from the yearly statistics associated with influenza has become increasingly difficult, such that the economic and social impact of lockdown policies not only appear ineffective, but actually harmful to the majority of the population below the age of 60.

If we return to the details in the Ivor Cummins video, he proceeds to do a comparison of the impact of Covid-19 in the UK, with lockdown, versus Sweden, with basic social distancing, where the following graph left was produced by the Swiss Policy Research Group – see Deaths in Sweden chart for details.


The Deaths in Sweden link also provides some additional insight, see following quote, for what is defined as the Infection Fatality Rate (IFR), which although seemingly similar in scope to the deaths per infections percentages quoted in the earlier tables, requires an accurate assessment of infections to be meaningful, which appears absent in the website statistics.

Most antibody studies showed a population-based IFR between 0.1% and 0.3%, which is comparable to a severe influenza. In fact, for people under 50, the Covid19 IFR is rather lower than for influenza. A few hotspots did show locally higher IFRs of up to 0.7%, but these places usually were affected by a collapse in elderly care due to infections or panic.

We might see the distribution of the percentage Covid-19 deaths against age in the chart below, which was discussed under the heading of All-Cause Mortality. Basically, people under the age of 60 appear to have minimal risk from Covid-19 provided this risk is not aggravated by what is now often referred to as metabolic syndrome – see video Metabolic Syndrome Is caused by high insulin for a 5 minute introduction.

At this point, the official statistical evidence associated with Covid-19 across all countries appears to question the efficacy of any lockdown policy for the following reasons:

  • The Covid-19 virus is not as deadly as first feared, especially for younger age groups.

  • It is now recognised that the worst-case susceptibility is biased towards age and existing comorbidities.

  • UK/Lockdown vs Sweden/Social Distancing comparisons shown no appreciable difference in deaths per population.

Let us now consider two further issues, which tend to be often misreported or ignored in the mainstream media, i.e. the efficacy of face-masks and the Polymerase Chain Reaction (PCR) tests. The first link is evidence given to a special committee of the Irish Government by Professor Carl Heneghan on 13-Aug-2020, where he explains why face-masks are ineffective when used by the general public, which might be summarised in the quoted statistic below:

“200,000 people would have to wear a face mask for 1 week to prevent 1 infection.”

Again, returning to the Ivor Cummins video at time (24:12), he addresses what he calls a ‘casedemic’ that is created by increasing numbers of PCR tests being carried out in many countries. Within the framework of a casedemic, the number of infection cases increases without any appreciable increase in deaths, which the video explains in some detail by referencing the site linked in the question: Are you infectious if you have a positive PCR test result for COVID-19?, again authored by Professor Carl Heneghan. The findings of the ‘Centre for Evidence-Based Medicine’ is summarised below.

The PCR detection problem is ubiquitous for RNA viruses as viral RNA can be detected long after the disappearance of the infectious virus. The immune system works to neutralise the virus and prevent further infection. Whilst an infectious stage may last a week or so, because inactivated RNA degrades more slowly over time, it may still be detected many weeks after infectiousness has dissipated. Therefore, while PCR detection of viruses is helpful so long as its limitations are understood; while it detects RNA in minute quantities, caution needs to be applied to the results as it often does not detect infectious viruses.

Another issue covered in the Ivor Cummins video, at time (18:50), is seasonality, which is possibly important to understand before we discuss epidemiological models and the accuracy of their predictions. For these models have been used in the formation of public policy to address the Covid-19 pandemic, i.e. lockdown and social distancing, such that we need some understanding of the complexity of all the variables needed to be taken into consideration. First, it needs to be highlighted that coronaviruses are a group of related RNA viruses, which can all cause illness in humans, primarily flu-like respiratory infections that can range from mild to lethal. Mild illnesses are often associated with the common cold, which might be linked to other viruses, e.g. rhinoviruses, while other strains of viruses can cause SARS, MERS and now Covid-19. In this context, the ‘novel coronavirus’ or SARS-CoV-2 is not really that novel as it belongs to a family of viruses, which have been in circulation for over two decades. This point is highlighted because it might suggest that a certain percentage of any population might have already developed a degree of immunity – see video SARS2 reactive T-cells: 17-Jul-2020 for more details. However, it has long been recognised that the impact of many of these viruses is seasonal, as suggested in the diagram below.

Note: One suggestion being forwarded to explain this seasonality is based on the uptake of vitamin-D with UVB sunlight in the summer months in northern latitudes, which is reversed in southern latitudes. This idea also suggests why people of darker skin colour, who now live in higher latitudes, may be more susceptible to the Covid-19 virus - see Vitamin D Status and Viral Interactions: 27-Apr-2020.

 So far, this outline has tried to quantify the many factors that influence the spread and seriousness of the Covid-19 pandemic by making reference to official statistics and expert opinion, such that we might judge the effectiveness of ‘lockdown’ policies. Initially, one of the main arguments originally cited, especially in the UK, was an epidemiological model linked to the Imperial College COVID-19 Report: 16-Mar-2020. Today, actual statistics appear to discredit the projection of the Imperial model, which might be described as a form of SIR model first discussed on the opening page of the Covid-19 Pandemic analysis, where the SIR acronym stands for ‘ Susceptible, Infected and Recovered’ .

While this discussion will not repeat the mathematical details that can be reviewed via the link, this discussion might attempt to review what has now been learnt about the various factors that drives the SIR model to produce the outcome characterised in the chart above, where figures are normalised in percentages. Before we discuss the characteristics of the chart, the idea of the basic reproduction number [R0] needs to be clarified in terms of the effective reproduction number [RE]

Note: The value of [R0] of a specific virus is generally defined as the expected number of cases directly generated by one case in a population, where all individuals are assumed to be susceptible to infection. Despite the reference above suggesting a Covid-19 value of [R0] between [3.8–8.9], the University of Oxford’s Covid-19 Evidence Service Team estimated that SARS-CoV-2, responsible for the Covid-19 pandemic, has an estimated value [R0] of ~2.63, although other estimates vary between 0.4 and 4.6. However, estimates may vary due to demographic and geographic differences in the populations affected. 

The outcome of the SIR model can be highly dependent on the initial value of [R0] assumed, such that the predictive accuracy of these models can be questionable based on overly simplistic assumptions that are then proved wrong, as in the case of the Imperial model. There can also be a misunderstanding of the effective infection rate [RE] at any given point in time or the overall susceptibility within the collective population based on both demographic and geographic differences.

Note: Without detailing all the technical arguments, the effective rate [RE] is a function of time, i.e.


Where [β] is the infection rate of the virus, [γ] the recovery rate and [S] is the number of remaining susceptibles that changes as a function of time, such that [RE] reflects a negative growth rate, which is shown on the previous chart as a blue dotted line with the scale on the right starting at [R0=2.5] and falling to [RE=0.27]. However, what we might understand from this outline, and previous statistics, is that certain demographic groups within a given population are more susceptible than others, e.g. by age and comorbidities. Likewise, certain geographic regions within a population may have already been exposed and therefore become less susceptible, e.g. urban versus rural populations. Of course, if the model really has no accurate assessment of the number of remaining susceptibles [S] in all these different groups, then a SIR model will simply generate erroneous predictions, as per the Imperial model – see video Vastly Under-Counting Population Immunity: 17-Jul-2020 for further details.

If we look at the %-values suggested by the chart above, based on an initial value of [R0=2.5], the number of infections peaks below 25%, although if an initial value of [R0=5] is modelled, then the number of infections peaks above 50%. While this analysis is neither authoritative or rigorous, it suggests the degree of herd immunity may also depend on the initial value of [R0] assumed, which is dependent on demographic factors in a population, e.g. average age and Vitamin-D deficiency for example – see Covid 19 - Chart and Stats Essentials: 28-Jun-2020 for more in depth details and opinion.

Note: By way of a general sanity check of the SIR model above, we might consider the case of the Diamond Princess, which was a cruise ship that began its voyage on 20-Jan-2020. Subsequently, an increasing number of passengers tested positive for the Covid-19 virus and the ship was held in quarantine until 1-Mar-2020. Over this time, 567 out of 2666 passengers and 145 of the 1045 crew were infected and 14 died.

If we treat this incident as a simple population case-study, then 21.2% of the passengers and 13.8% of the crew were infected. While there were possibly many reasons for the difference in these infection rates, the age demographics of the passengers and crew might account for some of this difference. Overall, 19.1% of the 3711 people onboard were infected, where the 14 deaths represented 1.96% of those infected, but only 0.37% of the ship’s population. While this case study is hardly definitive, it does not seem to contradict the previous SIR model based on [R0=2.5]. Of course, one of the problems already highlighted with these SIR models is the variance in demographics and geography when extended to the population of an entire country. So, while based on only a limited assessment, it is still difficult not to conclude that SIR models may only be of limited use, if they cannot accurately quantify the complexity of an entire population in terms of its demographic distributions and geographic differences. If this is the case, we might also begin to understand why different regions, with demographic and geographic variations, may have vastly different susceptible populations, such that regional spikes in both infections and deaths may occur over different periods of time, such that they should not be immediately interpreted as evidence of a ‘second-wave’, especially when seasonality is taken into account.

Note: One of the reasons for highlighting the ‘seasonal’ effects associated with most viruses is that northern hemisphere countries are now starting to enter their winter months. As such, there will probably be a natural increase in viral infections of all types, which should not necessarily be described as a ‘second-wave’. However, it is suspected that this argument will be ignored by most mainstream sources.

While this addendum has made no claim to be authoritative on the issues being discussed, it has in conjunction with the earlier analysis of the Covid-19 pandemic, published in Jun-2020, tried to make reference to official sources of data and the opinion of recognised experts to justify some of its conclusions – see Data Models and Data Reality for more details.

Note: Many of the sources used are summarised in chronological order in an appendix at the end of this document, such that the reader might get a better idea as to the wider ‘evolution’ of understanding surrounding the Covid-19 pandemic along the timeline of the last 6 months.

However, despite the statistical arguments, it possibly needs to be highlighted that this addendum is not making an argument that the Covid-19 pandemic does not pose a threat to life, only that government lockdown policies should be reconsidered in light of a more open and honest debate about the actual risks associated with the Covid-19 virus – see All-Cause Mortality for more details. In this context, an earlier discussion entitled The Efficacy of Lockdown produced a similar table as shown next.

Risk Groups Pop % of Pop %-Working % Death
Low-Risk under 50 35,778,000 53.40% 82.00% 1.00%
High-Risk over 50 31,423,000 46.90% 18.00% 27.70%

Based on broad estimates, people under 50 represent 53.4% of the UK population, but over 80% of its working population, where their risk of death is 27 times lower, on average, than the groups over 50. Within this generalisation, an argument can be made that ‘lockdown’ restrictions should not have included people below 50, including children who have almost zero-risk, such that their social lives and jobs along with the wider economy might have been maintained. In reality, the main victims of the Covid-19 pandemic were often elderly people in care-homes, which the lockdown policy failed to protect in practice. So, having established a baseline of expert opinion surrounding the current status of the Covid-19 pandemic, albeit seemingly contradicting the position of government experts, this discussion will now consider the more controversial issues linked to the next question.

Why are so many governments still pursuing a lockdown strategy?

Let us first widen the scope of the discussion beyond just the technical issues surrounding a viral pandemic in order to consider the implications associated with lockdown policies, which not only affect the social freedom of most people, but in many cases has imposed a financial burden on them, as well as their national economy. We might start this process by considering the arguments in the video: The scientific community overreacted to the threat of Covid-19: 14-Sep-2020.

Note: Jay Bhattacharya, MD, PhD, is a professor of medicine at Stanford University’s Centre for Primary Care and Outcomes Research, director of Stanford’s Centre on the Demography and Economics of Health and Aging and a senior fellow at the Stanford Institute for Economic Policy Research. As such, he appears well qualified to open this section of the discussion.

We will now follow the previous wide ranging video with one that focuses on the suggestion that governments have tried to scare the population into accepting their lockdown policies, while making little attempt to debate or justify these policies – see Trying to scare people is not a solution: 5-Sep-2020.

Note: John Ioannidis is a physician, scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. He also studies scientific research published in the field of clinical medicine and the social sciences. Again, he appears well qualified to discuss these wider issues.

One of the possible unintended consequences of scaring people by not informing them of the real risks, especially by age demographics, is that society as a whole may make irrational decisions. In many countries, people appear unwilling to return to their normal lives, e.g. work and schools, because they believe that the threat of the Covid-19 virus still exists. Of course, this risk does exist in some cases, but where the statistical analysis now suggests that the risk, for most people, is now comparable to influenza, which we have always faced on a seasonal basis. Of course, if mainstream sources present this seasonal effect as evidence of a ‘second wave’, then the adverse effects on people’s lives, both socially and economically, might persist by their own choice. Whether some powerful groups might perceive an advantage in this situation to pursue their own self-interests is speculative, but not necessarily unwarranted – see video ‘The Great Reset Agenda’ for an appraisal of this idea.

Note: While this discussion will not pursue the potential implications associated with a ‘Great Reset’, it might reiterate some earlier concerns raised in the Propaganda and the Covid-19 Pandemic discussion. These concerns were linked to the Vaccine Impact Modelling Consortium and one of its partners, e.g. Imperial College London who produced the discredited SIR model and advised the UK government on the need for a lockdown policy.

However, the partnership implied in the note above also includes the Bill & Melinda Gates Foundation and the Gavi Vaccine Alliance as primary funders, where Professor Neil Ferguson was listed as the Acting Consortium Director. It is claimed that this partnership has received a total of $400 million from the Wellcome Trust and $185 million from the Gates Foundation since 2014. In addition, this strategic partnership supports and finances the Gavi Vaccine Alliance, as outlined in the following charts.

There is also a link between the Gavi Alliance and the ID-2020 alliance, where the mission statement, shown below, appears to forward the idea that everybody needs to have a digital ID.  

Identity is vital for political, economic, and social opportunity. But systems of identification are archaic, insecure, lack adequate privacy protection, and for over a billion people, inaccessible. Digital identity is being defined now and we need to get it right.

While there may be legitimate arguments in support of this goal, it is not surprising that some might seriously question this goal in terms of the right to privacy, such that we might wish to know a little bit more about all these organisations and their objectives. The ID-2020 Alliance was apparently started in 2017 with founder members Accenture, GAVI, Microsoft, Rockefeller Foundation and with the goal of tagging every global citizen by the year 2030. GAVI along with vaccine manufacturers also support the ID-2020 program, although it has its own reason with respect to global vaccination. We might highlight yet one more organisation, founded in 2015, called the Coalition for Epidemic Preparedness Innovations (CEPI) that seeks to finance independent research projects to develop vaccines against emerging infectious diseases, where its funding also links back to various governments and the Gates Foundation ($100 million) and the Wellcome Trust ($100 million). In March 2020, the UK government pledged £210 million to CEPI to focus on a vaccine for the Covid-19 virus, making it the largest individual donor to CEPI and GAVI.

Note: The idea that the pharmaceutical industry only want to develop a Covid-19 vaccine for the good of humanity is possibly somewhat naïve given their past track record – see Prevention versus Cure plus Financial Conflicts of Interests and the End of Evidence-Based Medicine. Another worry is that governments and global institutions might see the Covid-19 pandemic as an opportunity to pursue their own goals. While people might assume that western democracies will maintain their right to personal freedom, it is clear that countries, such as China, are already developing their idea of a Social Credit System - see Information Control for wider discussion. However, under the guise of lockdown policies, many personal freedoms have already been lost and some are now arguing that mandatory vaccines will be necessary and that proof of vaccination will be required in the form of a digital passport on everybody’s smart phones. While speculative, there is an implication that without this digital passport, people will not be allowed to use public transport or access public spaces or even go to work. If so, it is unclear how this idea would differ in scope to the Chinese Social Credit System.

So, having veered off into some speculative consequences, which might be linked back to Covid-19 lockdown policies, we might return to the more immediate implications on the economy - The Epidemiology - Economics Tradeoff: 20-Aug-2020.

Note: In a Wall Street Journal article, dated 24-Aug-2020, it wrote that in response to the coronavirus, many governments deployed draconian tactics never used in modern times: severe and broad restrictions on daily activity that helped send the world into its deepest peacetime slump since the Great Depression. The equivalent of 400 million jobs have been lost world-wide, 13 million in the U.S. alone. Global output is on track to fall 5% this year, far worse than during the financial crisis, according to the International Monetary Fund.

Clearly, there are potentially many factors that may have influenced governments in their strategy towards minimising the impact of the Covid-19 pandemic in their country. However, the statistical data shows no appreciable difference, as suggested in the table below. Where Brazil and Sweden in green had no lockdown, while the UK in orange had strict and prolonged lockdowns.

Rank Country Population Infections % Deaths %-Inf %-Pop
Global 7,700,000,000 28,944,152 0.38% 924,577 3.19% 0.012%
3 Brazil 212,347,956 4,315,858 2.03% 131,274 3.04% 0.062%
14 UK 67,772,000 365,174 0.54% 41,623 11.40% 0.061%
38 Sweden 10,343,403 86,505 0.84% 5,846 6.76% 0.057%

Based on the earlier SIR model with [R0=2.5], less than 25% of the susceptible population would be infected at the peak of the outbreak, such that it suggests that the majority of the population are either not exposed to the virus or have some form of immunity. As a general estimate, it is believed that 80% of those infected may only have mild symptoms, while another 15% may have more severe symptoms possibly requiring some medical treatment with only the final 5% experiencing life threatening symptoms. However, of the 80% infected, possibly in excess of 50% may have no symptoms at all, such that they are described as asymptomatic – see video SARS2 unexposed, normal healthy donors: 17-Jul-2020 for more detail on immunity due to earlier exposure to other corona viruses. We might also qualify the 5% with potentially life-threatening symptoms in terms of the deaths as a percentage of the population being in the region of 0.06%, where statistical evidence also shows that most can be correlated with older age groups, i.e. +60, and multiple comorbidities. Likewise, there is a strong suggestion that people with a general healthy immune system have little to fear from the virus, such that government advice about metabolic syndrome caused by high insulin and the benefits of vitamin-D supplements may have been more effective than lockdown, which it is known to have caused excess death by restricting the health services on offer to patients with other life-threatening conditions. Having attempted to present the statistical data and a range of expert opinions, we might return to the question tabled earlier.

So, why did so many governments adopt the lockdown strategy?

It is recognised that this discussion might only speculate on this question. However, we might reasonably assume that most governments, i.e. politicians, were not qualified to formulate a strategy without input from people with expertise in multiple fields, e.g. epidemiology, virology and many others. However, it is known that the body that advised the UK government, i.e. Scientific Advisory Group for Emergencies (SAGE) was criticised for its lack of transparency in general, especially in regard to the discredited Imperial College model. It also appears that some members of SAGE had interests linked to other institutions that were funding vaccine research, as outlined above. If we put such speculative concerns to one-side, it might be accepted that none of these experts could have really understood the true nature and scope of the Covid-19 pandemic back in Feb-2020, such that a lockdown strategy might have simply appeared to be the safest option at that time. Of course, having decided on a lockdown strategy, in line with many other western governments, the general public needed to be convinced that the lockdown strategy was in their best interest, but without necessarily highlighting all the potential side-effects, e.g. excess death, job losses, social isolation and longer-term economic fallout. In this respect, the mainstream media only appears to have supported the lockdown narrative with no obvious evidence that it wanted to investigate any other alternatives, e.g. Sweden’s social distancing approach.

But is such speculation simply drifting towards conspiracy theory?

It is recognised that some of the information cited appears to suggest that certain groups may be pursuing interests that extend beyond the scope of the Covid-19 pandemic. However, such an idea is hardly a conspiracy theory as it might be better described as simply a fact of life. Therefore, without actually knowing why so many western governments continue to pursue the lockdown approach, despite increasing doubts about its effectiveness, it is hardly surprising that certain powerful institutions might seek to use the situation to further their own interests. In terms of the earlier example of the ‘Great Reset’, see The Great Reset Explained: 12-Sep-2020, the director of the International Monetary Fund (IMF) has described the Covid-19 pandemic as a ‘great opportunity’. In a similar fashion, others may seek to use the Covid-19 pandemic to pursue their own agenda for climate change, which is being supported by two other powerful institutions, e.g. the United Nations (UN) and the World Health Organisation (WHO) – see videos UN chief on climate change and COVID-19: 28-Apr-2020 and WHO are trying to link the Coronavirus to Climate Change. While many may perceive the first video to be a reasonable argument for positive change and the second as being too politically biased to the FOX news agenda, there are legitimate concerns that many of these powerful institutions are not being totally honest about their motives to the public.

But, how might lockdown policies adversely affect our lives?

 While a full discussion of this question is beyond the scope of this addendum, some outline of the issues might still be attempted. First, this discussion has questioned the efficacy of the lockdown approach, where the previous link simply references an earlier discussion. However, irrespective of whether it was effective in minimising the impact of the virus, there have clearly been other implications stemming from the lockdown policy.

Note: The chart above is taken from the Data Models and Data Reality discussion and shows the excess deaths, in red, over and above the average base line, in black, where the statistical data used to construct the chart right is based on an Office of National Statistics (ONS) weekly report, dated 15-May-2020. It might also be useful for the reader to reference another video entitled The Latest Data and Evidence, dated 28-May-2020. Basically, the bottom section of the chart shows that there may have been a similar number of non-Covid deaths as actual Covid deaths, as medical services for the 637,000 people who died, on average, in the UK were restricted.

The lockdown policies have also adversely affected the lives of the living. For human-beings are ‘creatures’ that have evolved to live in social groups, such that being forced to isolate from family, friends and colleagues can be traumatic and stressful for many in society. It is known that the effects of isolation, both short and long-term, can cause psychological and physical health problems. Such problems can manifest themselves in terms of increase levels of anxiety, aggression and depression. Of course, economic and financial worries can also put increase pressure on families, which not only causes stress to the parents, but psychological stress on the children.

Note: Peter Nilsson, a professor of internal medicine and epidemiology at Lund University, warns that the economic devastation caused by lockdowns will cause more deaths than the coronavirus itself. Part of the reasoning why the effects of the economy might be so devastating might be explain in the following videos: Why You Should Be Very Afraid of A K-shaped Recovery: 13-Sep-2020 and Will Governments Save Us?: 28-Jul-2020.

One final comment might be made about personal freedoms, or civil liberties, which we normally assume democratic governments will protect. In part, lockdown policies have been imposed without sufficient debate as to whether they are really effective, which many believed have set a very worrying precedence for the future – see Liberty and The Rule of Law: 13-Apr-2020. Whether a more open and honest debate takes place may possibly be seen as a litmus test of the real health of our society.

Appendix: Video References

The following video links are weighted in chronological order, such that the reader might get a better idea as to the ‘evolution’ of understanding surrounding the Covid-19 pandemic.

  1. Influenza Viruses and Pandemics: 2-Nov-2009
  2. Nuts and Bolts of the Immune System: 21-Jul-2011
  3. Vitamin D and Human Health: 4-Dec-2018
  4. Financial Conflicts of Interests and the End of Evidence-Based Medicine: 24-Sep-2019
  5.  Prevention versus Cure: 27-Nov-2019
  6.  Estimating case fatality rates for COVID-19: 9-Mar-2020
  7.  Corona crisis: Open letter to the Chancellor from Prof. Sucharit Bhakdi: 29-Mar-2020
  8. Hydroxychloroquine Use in Coronavirus Patients: 30-Mar-2020 .
  9. How can the Coronavirus pandemic end?: 10-Apr-2020
  10. Dr. Paul Mason - 'How to Survive Coronavirus: 13-Apr-2020
  11. How COVID19 Kills Some People: 15-Apr-2020
  12. Why lockdowns are the wrong policy - Swedish expert Prof. Johan Giesecke: 17-Apr-2020
  13. A Data Centric Perspective: 24-Apr-2020
  14. Prof. Neil Ferguson defends UK Coronavirus lockdown strategy: 25-Apr-2020
  15. Vitamin D Status and Viral Interactions: 27-Apr-2020
  16. How coronavirus charts can mislead us: 28-Apr-2020
  17. UN chief on climate change and COVID-19: 28-Apr-2020
  18. The Failure of Expert Predictions and Models: 30-Apr-2020 ,
  19. COVID19 Never Grows ExponentiallyMichael Levitt 14-May-20
  20. Possible vaccine in 2021, but not without taking risks: 8-May-2020
  21. Professor Dolores Cahill: 12-May-2020
  22. Does Wearing N95 MASK Increase CO2 Levels and Reduce Oxygen: 14-May-2020 ,
  23. Vitamin D and Latitude Powerful Effects: 25-May-2020
  24. Covid 19 - Chart and Stats Essentials: 28-Jun-2020
  25. Swedish Covid-19 chief Anders Tegnell: 23-Jul-2020
  26. Prof Carl Heneghan: can we trust Covid-19 death numbers? 17-Jul-2020
  27. SARS2 reactive T cells in unexposed, normal healthy donors: 17-Jul-2020
  28. Oxford epidemiologists: suppression strategy is not viable18-Jul-2020
  29. The Great Reset Agenda: 1-Aug-2020
  30. Efficacy of Face Masks: 13 Aug 2020
  31. Odds of Dying from COVID-19 by Age Group: 18-Aug-2020
  32. The Epidemiology - Economics Tradeoff: 20-Aug-2020
  33. Prof Michael Levitt: Covid panic will shorten lives: 28-Aug2020
  34. Tyler Cowen on herd mentality and herd immunity: 3-Sep-2020
  35. John Ioannidis - Trying to scare people is not a solution: 5-Sep-2020
  36. Viral Issue Crucial Update Sept 8th: the Science, Logic and Data Explained: 8-Sep-2020
  37. The Great Reset Explained: 12-Sep-2020
  38. Two Studies Highlight Vitamin D as an Effective Treatment for COVID-19: 13-Sep-2020
  39. The scientific community overreacted to the threat of COVID-19: 14-Sep-2020