Misleading the public with The Science (TM)

Is the existence of models to estimate lives saved by Covid-19 vaccines ever proof that lives were actually saved?

Again today I interrupt my regular work to ask a question that should be simple to answer…

Is the existence of models to estimate lives saved by Covid-19 vaccines ever proof that lives were actually saved?

On December 13, 2022 the Commonwealth Fund published yet another in the long line of articles that directly claim vaccines for Covid-19' saved millions of lives, mitigated tens of millions of hospitalisations, and saved (at least for the United States of America’s economy) more than a trillion dollars…

Statistical Modelling

Statistical modelling is common and can be seen everywhere - from the weather predictions forecasters make on the television each evening to the way aeronautical engineers simulate stresses on new fuselage designs. However, ever since the world became aware of Covid-19 there have been people seeking their 15 minutes of fame by modelling their version of what they think will happen next. Models told us how many people would become infected, how many would die and, more incredibly, the beneficial impact of different interventions… including claiming that lives were saved from social distancing, masks and more recently, vaccines.

Mathematical models have become ubiquitous as predictive tools for health policy governance - predicting transmission, epidemic outcomes and the effects of preventative measures and controls (Bauer, 2013; Ferguson et al, 2020; Medley et al, 2001; Mideo, Day & Reid, 2008; Pike & Saini, 2020). The general public are now more willing and have become conditioned to accept the media-promoted health-related outputs from our quietly studious number-crunching colleagues (Dorfman & Krasnow, 2014). But are these modelled estimates ever really evidence of what has, or could, happen?

Modelling Covid-19 Deaths - a shot in the dark

It was shocking to see the unfailingly doomsday predictions of one mathematician, Neil Ferguson of Imperial College London, used yet again to direct government health policy response (Adam, 2020; Cowley, 2020; Rhodes & Lancaster, 2020). At the time Ferguson presented his now infamous COVID-19 report (Ferguson et al, 2020) that almost everyone now agrees was flawed and grossly exaggerated, he had a contentious history - having already delivered numerous wildly extravagant predictive modelling results for disease transmission and death that had resulted in controversial and expensive UK policymaking (Charleston et al, 2011; Ferguson et al, 2001; Ferguson et al, 2002; Fund, 2020; Ghosh, 2011; Rushton & Foggo, 2020).

Ferguson’s now infamous 13+ year old software program that predicted 510,000 deaths in eight weeks was programmed with inputs that included an arbitrarily assumed R0 referenced from two prior works: (1) a paper by two Swiss researchers that estimated the R0 in China using a simulation model, ‘estimations of uncertainty’ and a process of elimination using sparse available data, a ‘wide range of parameter combinations’ and ‘comparison to past emergencies’ (Riou & Althus, 2020); and (2) a paper from a large group of Chinese researchers who directly acknowledged their lack of key epidemiological data meant they had to estimate the most important variables which resulted in a weakly estimated R0 value and the only honest and definitive conclusion of their paper - that human-to-human transmission had occurred (Li et al, 2020). It is interesting to note that Ferguson’s model assumed values for R0 that were respectively 10% and 20% higher than those of either paper he specifically cited for the R0 value.

Many public health decisions were made on the basis of the Ferguson model - from lockdown mandates that caused some to suicide to hospital clinic closures that meant people with treatable disease and cancers were sent home to die. And while I could deservedly continue to rub salt into Ferguson’s half a million deaths wound, there are more recent examples of his incredible and wildly exaggerative modelling of Covid-19 data to poke fun at…

Double down or go home

Among others, Neil Ferguson and the team at Imperial College London have also been involved in the manufacture of data (and headlines) that claim vaccines have or are going to save lives - anywhere from 20 million to 50 million, depending on the period being modelled.

In the first paper (Watson et al, 2022), a team that included authors from Ferguson’s half a million deaths paper above began with complicated model fitting that was based on two other models consisting almost entirely of fitted estimates and assumptions, and guided by a framework for inferring Covid-19 mortality based on assumptions about community indicators - thus making their new model an estimate of estimates stretched over a framework of inference and assumption. The second element of their approach was the application of mortality data that they admit was heterogeneous (having the quality or state of being diverse in character or content - which in this case surely means diverse in its degree of accuracy?) and that prominent mathematicians and health informaticians like Professors Norman Fenton, Martin Neil and Scott McLachlan have already shown are loaded with baked-in discrepancies and inconsistencies that compromise the accuracy of mortality rate comparisons between the vaccinated and unvaccinated (http://dx.doi.org/10.13140/RG.2.2.32817.10086 and https://www.significancemagazine.com/701 and http://dx.doi.org/10.13140/RG.2.2.14176.20483). Watson et al (2022) used this approach to first predict the overall number of deaths they believe should have occured - but which were, they said, averted by the vaccines. They arrive at an eventual tally just shy of 20 million lives that they factitiously claim were undoubtably ‘saved by the covid-19 vaccines’.

In the second paper (Toor et al, 2021), that includes Ferguson and several of his Imperial College London colleagues, the authors use 21 different mathematical models kludged together to create a single estimate for disease burden and arbitrarily attribute: (1) any negative impact on mortality to the disease (that is, the vaccines are artificially perfect and never cause adverse events); and (2) any beneficial impact to the vaccine (well… the year of vaccination as a proxy for the vaccine). This means their work ignores any of the vast evidence base that demonstrates a medication, even the exaulted and almighty vaccine, can kill or seriously maime even some percentage of the population it is administered to (can anyone say AstraZeneca VaxZeviria… or Johnson & Johnson/Janssen… or GSK Pandemrix… or Merck’s 1976 Swine Flu Vaccine… or Sanofi SKYCellflu… or… I could honestly go on for a week but you get the idea). The results of Toor et al (2021) are a meaningless and irredeemable estimation that between 80 and 120 million lives will be saved before the year 2030 by vaccinations administered now, and that 50 million lives were saved by vaccinations administered between 2000 and 2019 - truly fortune-telling through the most tenebrous and feculent of crystal balls!

But covid vaccines saved lives… right?

A July 2021 article in the newspaper The Guardian by a prominent Cambridge University math professor was one of the first to conflate that the existence of several academic models for estimating the number of lives saved by COVID-19 vaccines…

… was indisputable proof that lives were actually saved.

Even as they hedged their bets by quoting George Box’s ‘all models are wrong’, Speigelhalter and Masters closed their article by deluding the reader with the statement ‘vaccines saved thousands of lives’ (Spiegelhalter & Masters, 2021). As if, by all appearances, that must surely be the final word on the matter.

However, there is a key mistake in approaches used to model the numbers used in these ‘Covid-19 vaccines saved lives’ approaches that mathematicians like Ferguson and Speigelhalter fail, perhaps deliberately, to tell you about - and it is a mistake made all the more obvious by the Commonwealth Fund model I opened this post with.

Assumptions

In every one of the works discussed here that claims vaccines saved lives, the authors began with the untested and unproven assumption that vaccines do, in fact, save lives. Then, and in every case, they create two models - one in which we have vaccines that do save lives, and the other where we do not have vaccines that save lives. To get their fantastic results they run both models simply to see whether, in the one that has vaccines that do save lives, the vaccines indeed have (unsurprisingly) saved lives. Essentially, their research question amounts to: Are more lives saved in a model where vaccines that do save lives exist?

In essence, they bias their work by starting with the conclusion and crafting the entire modelling process to produce results to order. Then, to get their wildly exaggerated estimation of lives saved, and as demonstrated by Professor Norman Fenton in this video, they subtract the estimation of deaths from the model that has vaccines that do save lives, from the much higher estimation of deaths from the model that does not have vaccines that save lives.

Voila!

But pay no mind to the man behind the curtain…

The final word

While the Commonwealth Fund article is framed (or as legal experts might say, couched) and promoted as an academic study, it is nothing of the sort. Even though we are told the authors are academics in the employ of several USA universities (but mostly the Yale School of Public Health) with qualifications in global health and epidemiology - it is, in fact, a blog post. While the authors tell us that the values used to populate their models were drawn from published estimates (see excerpt below), the blog post contains no citations, no list of references and, absent some super-sleuthing, no obvious way of divining exactly how their model was designed or operated. It is entirely opaque - and that is not how science is supposed to operate.

Bret Swanson posted on Substack on December 14 about the Commonwealth Fund model for Covid-19 mortality, and their history for exageration. He demonstrates that the Commonwealth Fund model requires an estimate for covid mortality that consistently exceeds the maximum number that ever died - by a factor of almost 700%.

The results of these models that claim Covid-19 vaccines saved lives are not just bad science - they are, as Heather Heying asserted yesterday, clearly fraud. Fraud that is used to promote the vaccines and the vaccine agenda of their funders - which in each case comes back to two organisations both controlled and funded by the single person who admits to making the most profit from vaccines - Bill Gates.

Could it be that rather than proving that lives were saved, these ‘lives saved’ models are actually advertising and promotion - puffery dressed up as ‘The Science (TM)’ and intended by Gates, their funder, to increase positive sentiment towards vaccines that he invested in, and from which he admits to making a 20-to-1 return?¹

*For reference, staff of both the Commonwealth Fund and Yale School of Public Health regularly receive named grants (E.g.: Dr Elizabeth Bradley, Leslie Curry, and Dr Rafael Pérez-Escamilla) and large financial grants from the Bill Gates’ research funding organisations (e.g.: $14.5million here).

References

Adam, D. (2020). Special report: The simulations driving the world’s response to COVID-19. Nature, News Feature: 03 April 2020. https://www.nature.com/articles/d41586-020-01003-6

Bauer, S. (2013). Modeling population health. Medical Anthropology Quarterly, 27, 510–530.

Charleston, B., Bankowski, B. M., Gubbins, S., Chase-Topping, M. E., Schley, D., Howey, R., ... & Woolhouse, M. E. (2011). Relationship between clinical signs and transmission of an infectious disease and the implications for control. Science, 332(6030), 726-729.

Cowley, J. (2020). Neil Ferguson: The COVID modeller. The New Statesman, 31 July 2020. https://www.newstatesman.com/uncategorized/2020/07/neil-ferguson-covid-modeller

Dorfman, L., & Krasnow, I. D. (2014). Public health and media advocacy. Annual review of public health, 35, 293-306. https://www.annualreviews.org/doi/full/10.1146/annurev-publhealth-032013-182503

Ferguson, N. M., Donnelly, C. A., & Anderson, R. M. (2001). The foot-and-mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science, 292(5519), 1155-1160.

Ferguson, N. M., Ghani, A. C., Donnelly, C. A., Hagenaars, T. J., & Anderson, R. M. (2002). Estimating the human health risk from possible BSE infection of the British sheep flock. Nature, 415(6870), 420-424.

Ferguson, N., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., .. Ghani, A. (2020). Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare Demand. Imperial College London, 1-20. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf

Fund, J. (2020). ‘Professor Lockdown’ Modeler resigns in Disgrace. National Review, (6 May 2020). Sourced from: https://www.nationalreview.com/corner/professor-lockdown-modeler-resigns-in-disgrace/

Ghosh, P. (2011). Mass culling for foot-and-mouth ‘may be unnecessary’. BBC News (6 May 2011). Sourced from: https://www.bbc.co.uk/news/science-environment-13299666

Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Feng, Z. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England journal of medicine.

Medley, G. F., Lindop, N. A., Edmunds, W. J., & Nokes, D. J. (2001). Hepatitis-B virus endemicity: heterogeneity, catastrophic dynamics and control. Nature medicine, 7(5), 619-624.

Mideo, N., Day, T., & Read, A. F. (2008). Modelling malaria pathogenesis. Cellular Microbiology, 10(10), 1947-1955.

Pike, W. T., & Saini, V. (2020). An international comparison of the second derivative of COVID-19 deaths after implementation of social distancing measures. MedRxiv. https://www.medrxiv.org/content/medrxiv/early/2020/03/25/2020.03.25.20041475.full.pdf

Riou, J., & Althaus, C. L. (2020). Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance, 25(4), 2000058.

Spiegelhalter, D. & Masters, A. (2021). Covid vaccines saved lives in England, but why do estimates differ?. The Guardian, (4 July 2021). Sourced from: https://www.theguardian.com/theobserver/commentisfree/2021/jul/04/covid-vaccines-saved-lives-england-but-why-do-estimates-differ

Toor, J., Echeverria-Londono, S., Li, X., Abbas, K., Carter, E. D., Clapham, H. E., ... & Gaythorpe, K. A. (2021). Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world. Elife, 10.

Watson, O. J., Barnsley, G., Toor, J., Hogan, A. B., Winskill, P., & Ghani, A. C. (2022). Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. The Lancet Infectious Diseases, 22(9), 1293-1302.

1. While the Fact Checkers love to point out that they believe Bill was talking about social and economic benefits to everyone, not profit to himself, they do so by casually (deliberately) avoiding all recognition that Bill and his foundations have large investments and own significant chunks of a lot of pharma companies - including large stakes in both Pfizer and BioNTech (to the collective tune of around $100mil), CureVac ($52mil), and Vir Biotechnology (source: https://www.fool.com/investing/2020/09/24/4-coronavirus-vaccine-stocks-the-bill-melinda-gate/). While he started buying Pfizer and pharma stock as early as 2002, Gates is said to have been investing in mRNA technology since at least 2015 - and investment commentators say he has earned 10x on his BioNTech invesstment in just two years - making the original $55mil he put into BioNTech worth over $550mil now (source: https://www.trialsitenews.com/a/gates-earns-10x-on-biontech-in-just-two-years-55m-investment-now-over-550m). That’s not only some serious return on investment (ROI) but also demonstrates that where it comes to telling us we need to be jabbed, he has some serious conflict of interest issues (COI).

And when his foundation bought up supply of what they tout as potentially the next big thing (killer?) in covid treatment, that in the USA alone they can sell at $700 per treatment…

They offered their Molnupiravir to the South African Govt for R10,000 (around USD $530) per treatment - which the South African Govt ended up rejecting in favour of a generic version from a Singapore company (Arrow?) because Gates’ supply was ‘too spendy’. For Gates, it definitively isn’t about helping humanity… it’s all about making more and more money and feeding his saviour complex through his desperate need to give the appearance that he is saving the little underpriviledged coloured peoples…

While he’s helping others like Klaus Schwab take over the world.