Analysis of deaths in England 2017 to 2021
Empirical proof that all government COVID measures have been ineffective at best.
Deaths in England follow a distinct seasonal pattern with excess mortality typically occurring between October and June. Since 2017 this seasonal excess is approximately above 8,600 deaths per week. The deaths can be easily characterised by a simple model.
Each season consists of two to three predictable distributions, a gradual hump that spans the whole mortality season, punctuated by at least one spike attributed to the dominant, circulating pathogen.
Before COVID-19, 2017-18 was a particularly bad year due to the impact of influenza B(H3N2), accounting for around 31,000 deaths. 2018-19 was a particularly soft year, amounting to only 27,000 seasonal excess deaths in total compared to 52,000 in total the previous season.
It is not uncommon for a particularly bad year to be followed by a soft one and vice versa.
It is apparent that the model which consists only of two parameters, an initial growth rate and an exponential decay on that growth rate, captures the entire three distributions in each season. This indicates that there were no exogenous factors that interrupted the distributions since their inception.
We observe the exact same seasonal patterns in 2019-20, the year that COVID-19 first emerges and 2020-21 when it should have behaved with endemicity like all the other various respiratory pathogens.
In addition to the ubiquitous season-long hump, the first part of 2019-20 was characterised by the return of influenza A(H3N2), accounting for 11,000 deaths on top of the 19,000 expected deaths that are not usually attributable to a specific pathogen. If 2019-20 were comparable with 2017-18, another 22,000 deaths would not have been extraordinary.
In fact, COVID-19 is attributed to around 43,000, making it about 38% more deadly than influenza B(H3N2) a few years before.
However, there were around 16,000 additional deaths that could not be attributed to COVID-19 or any other pathogen. Upon presentation of the same fact by the ONS, the government and its advisers accepted these deaths as collateral for the hundreds of thousands of deaths they claim were avoided by their intervention with respect to the modelling of Prof. Ferguson of Imperial College, London1.
And then, by the end of May, COVID-19 deaths have all but gone, just like respiratory pathogen deaths do every single year at this time.
When COVID inevitably returned in autumn 2020, as we would expect the dominant respiratory pathogen to do, as, according to the predictive model, it always does, it would have accounted for 41,000 deaths over the entire season.
Due to the “success” of Operation Moonshot, COVID was detected in every single excess death, including the ubiquitous hump so that ought to have been it, ranking it slightly closer to the soft season of 2018-19 than the harsh one of 2017-18.
However, exactly concomitant with the rollout of mass COVID vaccination, an unexpected surge of a variant produced a new distribution which eventually accounted for a further 44,000 seasonal excess deaths, taking the season tally to an extraordinary 85,000, 63% higher than the entire 2017-18 season.
The season-long hump expected to account for around 17,000 deaths, meaning the true excess due to COVID, being the dominant circulating pathogen, was 69,000.
Both distributions in 2020-21 are perfectly characterised by the continuous model, demonstrating that all other non-pharmaceutical interventions together (including but not limited to universal masking, restriction on group sizes, school and business closures, tier systems, international travel screening, and testing/isolation of asymptomatic people) had NO benefit whatsoever on COVID and all-cause mortality. None.
Due to the reliability of the simple, parametric model, it is possible to identify the impact of exogenous factors on mortality.
It is clear where a new distribution emerges even before a previous one has receded, representing a new event that causes excess deaths.
If the government interventions to mitigate the COVID-19 death toll were effective, we should expect to see actual deaths rise at a slower rate and or fall at a higher rate than predicted by the model.
Given the justification for the collateral deaths in spring 2020, it is imperative to do this empirical analysis.
The original model was calibrated to the entire distribution. If we want to measure the impact of lockdowns in England that were introduced on 23rd March 2020, we should only calibrate the model up to 3rd April. This 11-day period is well within the average period from infection to death of 3 to 4 weeks measured by numerous studies.
It is apparent that the model is exceptional at fitting to the empirical deaths data up to the point of calibration. However, it also appears that there were actually more deaths than we might have expected if the deaths had continued along the same path upon which they had moved for the first 5 weeks of the epidemic.
This disputes the validity of any claim that lockdowns were successful in mitigating deaths.
We can also use the model to determine the only feasible distribution that would accommodate the exaggerated claims made by Prof. Ferguson of 510k unmitigated deaths in the UK by using the initial growth of the best-fit model above (which cannot be changed since it occurred well before the effect of the intervention) and solving for the decay factor.
Using the conservative estimate of 400k for England alone would produce a distribution that deviates by more than 50% from the empirical data in the first full week after the intervention. This is not supported by any plausible evidential or scientific theory.
In conclusion, this empirical analysis demonstrates conclusively that, at best, the government COVID policy interventions in spring 2020 were INEFFECTIVE in mitigating COVID mortality.
As such, there is no justification for the 16k unexpected and avoidable excess non-COVID deaths during that period.
It also demonstrates conclusively that the 3rd wave of COVID is exactly coincidental with the mass vaccination program, although the model cannot prove causation.
It also shows that when it first emerged, in the absence of vaccination, COVID was less than 40% worse than a bad flu but in the season after vaccinations, it had increased to more than 60% worse than a bad flu season, suggesting again that, at best, the intervention was INEFFECTIVE in reducing COVID mortality and all-cause mortality.
The scientific reasons for the abject failure of all these interventions can be debated but the empirical proof that they have failed cannot be refuted.
All data publicly sourced from the Office for National Statistics and the former Public Health England bulletins.
https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf
The modellers confessed to their sins, saying they were not asked to model reality, rather the "worst possible case...". Ergo, no use to policy making, but consistent with years of fatuous climate models, which now even the likes of Gavin Schmidt admit "run much too hot".
Well you dweebs, we've been telling you that for years and been stigmatised for doing so.
And we won't mention Ferguson's models. Ferguson, the very epitome of the now standard UK public sector reward for failure. Why we still employ this fanatic is beyond me.
It is clear that CV-19 should always have been modelled in the same way as the flu, meaning that waves are self-limiting due to the fact that the vulnerable segments of the population at any point in time are defined. Hence, there has been success in modelling CV-19 using straightforward distributions (notably Gompertz).
However, the statement that, "conconcomitant with the rollout of mass COVID vaccination, an unexpected surge of a variant produced a new distribution which eventually accounted for a further 44,000 seasonal excess deaths", appears to have no genuine empirical basis. Instead, how about treating vaccines as explanatory variables in causing these excess deaths. This would at least appear to help explain the unseasonal pattern observed from June 2021, and also happens to neatly coincide with FDA/Pfizer documents showing higher all-cause mortality in the vaccinated groups versus the placebo and studies of high numbers of vaccine-induced deaths by extrapolating VAERS and so on.
While seasonal respiratory viruses will not go away any time soon, my bet is that you will need to find some other explanations for unseasonal patterns of excess deaths throughout 2022. A continuing refusal to treat the elephant in the room as an explanatory variable is likely to lead to a lot of head scratching, I suspect.