Radical Leftists Claim Oil Companies Are Committing Climate Murder

They’re gaslighting voters and consumers – when the real mass killers are environmentalists

By Paul Driessen

We’re constantly told fossil fuel use is causing an existential climate crisis, extreme weather, worsening wildfires, and more frequent and intense hurricanes, tornadoes, floods and droughts. 

Actual temperature, storm and other records provide no support for these claims. They certainly don’t back up ludicrous assertions that burning coal, oil and natural gas – and even human breathing and baking pizzas in wood-fired ovens – are causing countless alleged calamities: such as slowing Earth’s rotation, thereby affecting scientific time clocks (by one second). 

The claims are based primarily on computer models that erroneously assume carbon dioxide and a few other “greenhouse gases” (0.05% of Earth’s atmosphere, in total) control the climate, while the sun plays virtually no role, urban heat islands are inconsequential, and incompetent forest management is irrelevant. 

It’s gaslighting: perversions of truth designed to make us guilt-ridden, willing to slash our living standards, and happy to keep poor countries energy-deprived and impoverished. 

In the USA and worldwide, fossil fuels still provide 80% of total energy. They’re also the foundation for our economy, living standards, health and longevity – and over 6,000 vital products, including plastics, paints, pharmaceuticals, cosmetics, cell phones, eyeglasses, fertilizers and wind turbine blades. 

“Earth-friendly” wind and solar installations would blanket millions of acres of farmland, scenic areas and wildlife habitats; require billions of tons of ores; generate billions of tons of toxic water and air pollution from mining and manufacturing; and send electricity prices skyrocketing to pay for expensive battery or gas backup systems, and extensive new transmission lines and grid upgrades. 

Climate Defiance, Extinction Rebellion and other radical groups nevertheless block pipelines, rant and rampage through our streets, deface priceless artwork, and glue themselves to roads and statues, to intimidate legislators and regulators. 

Others file endless lawsuits to bankrupt fossil fuel projects and promote their twisted views about “climate justice.” Their latest scheme could be viewed as the culmination of their self-indoctrination. 

A recent Harvard Environmental Law Review article proposes prosecuting major oil companies for “climate homicide” and “mass murder” – for supposedly killing people, by raising global temperatures and sea levels, and causing deadlier hurricanes, tornadoes, floods, droughts, heatwaves, blizzards and wildfires. 

Media outlets eagerly promoted the claims – and Soros-funded prosecutors will undoubtedly be thrilled to indict ExxonMobil and other companies, once they’ve put more flash-mob thieves back on our streets. 

But not even these prosecutors – or any judge, jury or “expert witness” – can separate natural from human causes of modern climate change. Nor can they calculate fractions of manmade-climate-change-induced disasters attributable to coal, oil, gas, dung, or “renewable” energy mining and manufacturing; to China and India versus Europe and the United States; or certainly to specific energy companies. 

What they really don’t want addressed in this “climate homicide” discussion, however, is who is actually committing mass murder, especially of women and children, people with disabilities, people of color and other “particularly threatened” groups so supposedly beloved by climate justice warriors. 

My book, Eco-Imperialism: Green power · Black death, forcefully demonstrates that it is these self-righteous climate and environmental activists, and those who fund them, that are callously causing the eco-murder deaths of millions every year – and setting the stage for dramatically more in the future. 

These environmentalist death tolls have worsened, as greens became wealthier, more powerful, more fanatical, and more influential with and within US, EU and UN government agencies. 

More than 750 million people still have no access to electricity; nearly 2 billion have only sporadic access to barely enough electricity to charge cell phones and power a lightbulb or 1-cubic-foot refrigerator – and no juice for modern homes, schools and hospitals, water purification, or factories and other job-creating businesses. 

These people are forced to heat and cook with wood, charcoal or animal dung, inhaling noxious fumes that cause millions of deaths annually from respiratory diseases. Millions more die annually from intestinal diseases due to contaminated water and spoiled food, due to energy deprivation. 

Green fanatics perpetuate the death tolls, by battling anything except grossly insufficient, weather-dependent wind and solar power. (In European and other modern countries, people die of heatstroke when they cannot get or afford air conditioning; nine times more die from cold – from hypothermia and illnesses they’d normally survive if they could afford to heat their homes properly.) 

The fanatics also wage campaigns to deny Third World people access to insecticides and spatial insect repellants that would control disease-carrying flies and mosquitoes and even modern farming practices and technologies. Millions more thus die every year from diseases that are readily preventable or could be cured in modern hospitals (that don’t exist). 

No wonder developing nations increasingly reject Western carbon colonialism.

Radical food groups despise genetically engineered crops that multiply crop yields, survive droughts and slash pesticide spraying by 75% or more. They vilify Golden Rice, which enables malnourished children to avoid Vitamin A Deficiency, blindness and death. 

They demand “AgroEcology,” which rejects virtually everything that helps modern farmers feed billions more people with less acreage and water, and could largely eliminate hunger and malnutrition worldwide. The perverse movement rabidly opposes biotechnology, chemical insecticides, nitrogen fertilizers, and even hybrid seeds, monoculture farming and mechanized equipment like tractors. 

It demands “food sovereignty” for impoverished, malnourished Africans, Asians and Latin Americans – the right to “define their own food and agriculture systems” and have “healthy and culturally appropriate” food produced through “ecologically sound and sustainable methods.” 

Even more bizarre and frightening, major philanthropies like the Rockefeller Foundation and Walton Family Foundation support this craziness! So do the World Bank and many UN agencies. 

They say the world’s food production systems should be “aligned” with the purely arbitrary goal of preventing more than 1.5ºC of global warming since 1850, by “phasing out fossil fuel use, especially fossil-fuel-based chemicals in industrial agriculture.” 

Farmers who want to “define their own food and agriculture systems” by choosing modern technologies and practices get ostracized instead of supported. 

It’s increasingly obvious that climate fear-mongering and GIGO computer models have replaced evidence-based science, history, human nutrition needs and traditional ethical principles. 

But on a more positive note, climate cultists chomping at the bit to see oil companies prosecuted for climate murder should be careful what they wish for. Such a precedent could put eco-imperialists and their financiers on trial for manslaughter on a truly horrific scale. 

Paul Driessen is senior policy analyst for the Committee For A Constructive Tomorrow (www.CFACT.org), and author of articles and books on environmental, climate and human rights issues.

Let’s talk about REAL climate cataclysms

All-natural, not so long ago, hugely destructive, and a repeat would devastate people and planet

Paul Driessen

The climate crisis! Save our planet! Stop using fossil fuels! The ranting never ends.

Present data showing that hurricane frequency and intensity are not increasing, but tornado activity has declined significantly since 1975; offer clear evidence of past, recurrent, major climate changes, including a widespread Little Ice Age from 1300 to 1850, a 2400-year-old forest emerging from beneath a melting Alaskan glacier, a 5200-year-old mummified traveler frozen on a once-nearly-ice-free Italian Alps trail –

and they respond, Shut up! The climate crisis! The science is settled! Stop using fossil fuels!

Fine. But take a few minutes to ponder REAL climate disasters: the Pleistocene Ice Ages. And then ask Michael Mann, Al Gore, John Kerry and the rest of the climate cataclysm cabal to explain exactly how carbon dioxide and a few other greenhouse gases caused those massive ice sheets to grow and melt, multiple times. Just CO2 and GHG, all by themselves. They can’t do it. The CO2-driven crisis is a fable.

Widespread glaciation began some 1,800,000 years ago in North America and Europe, and about 800,000 years earlier on other continents. There were at least four, possibly five, major glacial periods, interspersed with warm intervals (like the one we’re in now) during which the glaciers mostly melted.

That last glacial epoch is named the Wisconsinan, because some of the most exemplary features are in my home state. It marks the furthest extent of glaciers southward into the United States and east and west across Canada. A piece of limestone on my desk shows striations (scratches) made by rocks dragged along beneath the monstrous glaciers that once covered Green Bay Packer country.

During those times of extreme glaciation, ice buried more than 17,000,000 square miles of North America, Europe, Asia and South America: some 30% of Earth’s land area; nearly six times the entire Continental USA.

The glaciers – a mile or more thick in places – pulled so much water out of the oceans that sea levels fell 300-400 feet. The world’s continental shelves were high and dry, and a thousand-mile-wide “land bridge” between Asia/Siberia and Alaska/Canada enabled animals and humans to migrate between the continents. Plant and animal species arose, disappeared and were replaced with each warming and freezing.

The towering walls of slowly moving ice obliterated everything in their paths. They bulldozed enormous quantities of soil, rocks and boulders off vast land areas, then deposited them elsewhere as the glaciers retreated. The ice was so heavy that Earth’s crust beneath it was compacted tens of feet at the margins and nearly 1,000 feet in the eastern Hudson Bay area, where the glaciers were thickest. The land subsidence caused areas far beyond the glaciers to be pushed upward.

Even today, the compacted areas are still rebounding upward, while areas pushed upward are subsiding, making sea levels appear to be rising in some places, and falling in others.

Temperatures plummeted during each glaciation. Average annual air temperatures were in the 54-68 degree F range (12-20 C) during each ice age. Closer to the icy ramparts, they ranged from freezing to well below freezing. They rose significantly during the warm interglacial periods.

The massive ice packs across and just above what are now the world’s northern corn and wheat belts, plus frigid air and low atmospheric CO2 levels, would have made agriculture impossible, had humans been farmers instead of nomadic hunter-gatherers. Another ice age would do likewise.

The last glacial period began about 129,000 years ago and ended 11,700 years ago – though not all at once everywhere. In North America, US glaciers disappeared by about 10,000 years ago; in the Hudson Bay area and northeast of that, they lingered several thousand more years. 

As the ice sheets melted, sea levels rose some 400 feet, turning high land areas into islands. In warmer waters, coral reefs grew with every foot of sea level rise, creating Caribbean, Red Sea, Australian and other coral wonderlands that didn’t exist just a few thousand years earlier. Life was good again.

The Ice Ages were Real Climate Cataclysms – dwarfing anything Earth might be experiencing today. No wonder alarmists refuse to debate climate change, its natural causes and minor human influences, and whether destroying our energy, economy and living standards would bring any climate benefits.

The fact is, their rants and demands aren’t about climate change. They’re about control.

Control of our energy and economic future. Our jobs and living standards. The kinds of homes we can have, and how much we can heat and cool them. What kinds of cars we can have, and how far we can drive them. What we can hear, see, read, learn, think and say. Under full-throttle Green Fascism.

Amid record crime, the Ukraine war, skyrocketing energy prices, Covid, plummeting student math and reading scores, economic recession, a looming cold winter of discontent – and soaring increases in fossil fuel use and emissions in China, India and a hundred developing countries – Biden-Democrat policy-makers are obsessed with climate change, because they think it’s their ticket to controlling our lives. 

For them, it doesn’t matter that their entire case depends on ignoring Earth history, censoring skeptical voices – and focusing on computer models that predict temperatures and weather events that bear zero resemblance to what is actually happening right outside our windows, in the Real World. It doesn’t matter that their climate obsession is driving people to depression, drugs and suicide.

It doesn’t matter that eliminating fossil fuels that provide 80% of our energy (and 100% of the chemical feed stocks for fertilizers and thousands of other vital products) – and forcing us to rely on wind and solar power – would de-industrialize most of the United States, just as it’s doing now in Europe. It doesn’t matter that millions of blue collar jobs would disappear, or that living standards and life spans would nosedive.

The Biden-Green-Democrat Ruling Classes couldn’t care less. It’s not their jobs and living standards they’re decimating. It’s yours and ours. They’d happily destroy the best of the present and past that we want to preserve – and replace it with what? Utopian dreams in a parallel universe? Energy and economic systems that exist only in their fevered imaginations and proclamations?

They don’t care. They assume they’ll end up on top of the heap, the consequences for the rest of us be damned. Maybe most amazing of all, they actually believe their nonsense. Or at least they expect us to believe it – at least long enough to get their candidates safely past the upcoming elections. 

So our job is simple, and we’re not likely to get a second chance.

Kick as many of them out of office as we can on November 8. End the tyranny and power grabs. Preserve and restore American pride, prestige, industry, prosperity, freedom and sacred honor.

Paul Driessen has degrees in geology, ecology, and environment and natural resources law. He is senior policy advisor for the Committee For A Constructive Tomorrow (www.CFACT.org) and author of books and articles on energy, climate change, environmental policy and human rights.

 

Scientific Computer Modeling Method 101

By Roy Tucker

Editor's Note - This another of those timeless pieces I come back to over and over again.  With permission, I published this in May of 2009, and in 2012, but with all the false information that has come out over the years regarding global warming, climate models and pandemic predictions, I think this is worth another look.  I've also added some verbiage  RK  

I'm a bit dismayed about how computer models have come to be more important than actual observations and so I offer a formal statement of the Scientific Computer Modeling Method.

The Scientific Method.  Data in Search of Conclusions

1. Observe a phenomenon carefully.
2. Develop a hypothesis that possibly explains the phenomenon.
3. Perform a test in an attempt to disprove or invalidate the hypothesis. If the hypothesis is disproven, return to steps 1 and 2.
4. A hypothesis that stubbornly refuses to be invalidated may be correct. Continue testing.

The Scientific Computer Modeling Method. Conclusions In Search of Data.

 

1. Observe a phenomenon carefully.
2. Develop a computer model that mimics the behavior of the phenomenon.
3. Select observations that conform to the model predictions and dismiss observations as of inadequate quality that conflict with the computer model.
4. In instances where all of the observations conflict with the model, "refine" the model with fudge factors to give a better match with pesky facts. Assert that these factors reveal fundamental processes previously unknown in association with the phenomenon. Under no circumstances willingly reveal your complete data sets, methods, or computer codes.
5. Upon achieving a model of incomprehensible complexity that still somewhat resembles the phenomenon, begin to issue to the popular media dire predictions of catastrophe that will occur as far in the future as possible, at least beyond your professional lifetime.
6. Continue to "refine" the model in order to maximize funding and the awarding of Nobel Prizes.
7. Dismiss as unqualified, ignorant, and conspiracy theorists all who offer criticisms of the model.

Repeat steps 3 through 7 indefinitely. 

 

Nobel Prize Awarded for the Worst Climate Model

By Patrick J. Michaels October 26, 2021

Given the persistent headlines about climate change over the years, it’s surprising how long it took the Nobel Committee to award the Physics prize to a climate modeler, which finally occurred earlier this month.

Indeed, Syukuro Manabe has been a pioneer in the development of so-called general circulation climate models (GCMs) and more comprehensive Earth System Models (ESMs). According to the Committee, Manabe was awarded the prize “For the physical modelling of the earth’s climate, quantifying variability, and reliably predicting global warming.”.........To Read More....

The Failure of Imperial College Modeling Is Far Worse than We Knew

Phillip W. Magness  – April 22, 2021 @ American Institute for Economic Research

A fascinating exchange played out in the UK’s House of Lords on June 2, 2020. Neil Ferguson, the physicist at Imperial College London who created the main epidemiology model behind the lockdowns, faced his first serious questioning about the predictive performance of his work.

Ferguson predicted catastrophic death tolls back on March 16, 2020 unless governments around the world adopted his preferred suite of nonpharmaceutical interventions (NPIs) to ward off the pandemic. Most countries followed his advice, particularly after the United Kingdom and United States governments explicitly invoked his report as a justification for lockdowns. 

Ferguson’s team at Imperial would soon claim credit for saving millions of lives through these policies – a figure they arrived at through a ludicrously unscientific exercise where they purported to validate their model by using its own hypothetical projections as a counterfactual of what would happen without lockdowns. But the June hearing in Parliament drew attention to another real-world test of the Imperial team’s modeling, this one based on actual evidence.

As Europe descended into the first round of its now year-long experiment with shelter-in-place restrictions, Sweden famously shirked the strategy recommended by Ferguson. In doing so, they also created the conditions of a natural experiment to see how their coronavirus numbers performed against the epidemiology models. Although Ferguson originally limited his scope to the US and UK, a team of researchers at Uppsala University in Sweden borrowed his model and adapted it to their country with similarly catastrophic projections. If Sweden did not lock down by mid-April, the Uppsala team projected, the country would soon experience 96,000 coronavirus deaths.

I was one of the first people to call attention to the Uppsala adaptation of Ferguson’s model back on April 30, 2020. Even at that early date, the model showed clear signs of faltering. Although Sweden was hit hard by the virus, its death toll stood at only a few thousand at a point where the adaptation from Ferguson’s model already expected tens of thousands. At the one year mark, Sweden had a little over 13,000 fatalities from Covid-19 – a serious toll, but smaller on a per-capita basis than many European lockdown states and a far cry from the 96,000 deaths projected by the Uppsala adaptation.

The implication for Ferguson’s work remains clear: the primary model used to justify lockdowns failed its first real-world test.

In the House of Lords hearing from last year, Conservative member Viscount Ridley grilled Ferguson over the Swedish adaptation of his model: “Uppsala University took the Imperial College model – or one of them – and adapted it to Sweden and forecasted deaths in Sweden of over 90,000 by the end of May if there was no lockdown and 40,000 if a full lockdown was inforced.” With such extreme disparities between the projections and reality, how could the Imperial team continue to guide policy through their modeling?

Ferguson snapped back, disavowing any connection to the Swedish results: “First of all, they did not use our model. They developed a model of their own. We had no role in parameterising it. Generally, the key aspect of modelling is how well you parameterise it against the available data. But to be absolutely clear they did not use our model, they didn’t adapt our model.”

The Imperial College modeler offered no evidence that the Uppsala team had erred in their application of his approach. The since-published version from the Uppsala team makes it absolutely clear that they constructed the Swedish adaptation directly from Imperial’s UK model. “We used an individual agent-based model based on the framework published by Ferguson and coworkers that we have reimplemented” for Sweden, the authors explain. They also acknowledged that their modeled projections far exceeded observed outcomes, although they attribute the differences somewhat questionably to voluntary behavioral changes rather than a fault in the model design.

Ferguson’s team has nonetheless aggressively attempted to dissociate itself from the Uppsala adaptation of their work. After the UK Spectator called attention to the Swedish results last spring, Imperial College tweeted out that “Professor Ferguson and the Imperial COVID-19 response team never estimated 40,000 or 100,000 Swedish deaths. Imperial’s work is being conflated with that of an entirely separate group of researchers.” It’s a deflection that Ferguson and his defenders have repeated many times since.

As it turns out though, Ferguson and the Imperial College team were being less than truthful in their attempts to dissociate themselves from Sweden’s observed outcomes. In the weeks following the release of their well-known US and UK projections, Ferguson and his team did in fact produce a trimmed-down version of their own modeling exercise for the rest of the world, including Sweden. They did not widely publicize the country-level projections, but the full list may be found buried in a Microsoft Excel appendix file to Imperial College’s Report #12, released on March 26, 2020.

Imperial’s own projected results for Sweden are nearly identical to the Uppsala adaptation of their UK model. Ferguson’s team forecast up to 90,157 deaths under “unmitigated” spread (compared to Uppsala’s 96,000). Under the “population-level social distancing” scenario meant to approximate NPI mitigation measures such as lockdowns, the Imperial modelers predicted Sweden would incur up to 42,473 deaths (compared to 40,000 from the Uppsala adaptation).

The Imperial team did not specify the exact timing of when they expected Sweden to reach the peak of its outbreak. We may reasonably infer it though from their earlier US and UK model, which anticipated the “peak in mortality (daily deaths) to occur after approximately 3 months” following the initial outbreak. That would place Sweden’s peak daily death toll around mid-June, or almost the exact same time period as the Uppsala team’s adaptation.

Figure I: Imperial College Model for Sweden, March 26, 2020

It turns out that Viscount Ridley’s line of questioning was correct all along. The Uppsala adaptation of Ferguson’s model not only projected exaggerated death tolls in Sweden. Ferguson’s own projections for Sweden advanced similar numbers, all wildly off the mark from what happened.

Imperial College’s multi-country model used its earlier and more famous projections for the US and UK to claim validity for its more expansive set of international extrapolations. As Ferguson’s team wrote on March 26, 2020: “Our estimated impact of an unmitigated scenario in the UK and the USA for a reproduction number, R0 , of 2.4 (490,000 deaths and 2,180,000 deaths respectively) closely matches the equivalent scenarios using more sophisticated microsimulations (510,000 and 2,200,000 deaths respectively)” that they released a few weeks prior. If Imperial’s US and UK projections matched, a similar validity could be inferred for the other countries they modeled in the multi-country report.

The Imperial College team fully intended for its multi-country model to guide policy. They called on other countries to adopt lockdowns and related NPIs to reduce the projected death toll from the “unmitigated” scenario to “social distancing.” As Ferguson and his colleagues wrote at the time, “[t]o help inform country strategies in the coming weeks, we provide here summary statistics of the potential impact of mitigation and suppression strategies in all countries across the world. These illustrate the need to act early, and the impact that failure to do so is likely to have on local health systems.”

Failure to act, they continued, would lead to near-certain catastrophe. As Ferguson and his team wrote, “[t]he only approaches that can avert health system failure in the coming months are likely to be the intensive social distancing measures currently being implemented in many of the most affected countries, preferably combined with high levels of testing.” In short, the world needed to go into immediate lockdown in order to avert the catastrophes predicted by their multi-country model.

(Note: Imperial College also included a third possible mitigation scenario for stricter measures on top of general population NPIs, aimed at further isolating elderly and vulnerable people, projecting it could reduce Sweden’s numbers to between 16,192 and 33,878. They further modeled a fourth possible “suppression” scenario consisting of a severe lockdown that would reduce human contacts by 75% for the duration of the pandemic and maintain them for a year or more until population-wide vaccination was achieved. It predicted 14,518 deaths. Sweden clearly did not adopt either of these approaches).

One year later we may now look back to see how Imperial College’s international projections performed, paying closer attention to the small number of countries that bucked his lockdown recommendations. The results are not pretty for Ferguson, and point to a clear pattern of modeling that systematically exaggerated the projected death tolls of Covid-19 in the absence of lockdowns and related NPIs.

Figure II compares the Imperial College model’s projections for its “social distancing” scenario and “unmitigated” scenario against the actual outcomes at the one-year mark after its release. These projections reflect an assumed replication rate (R0) of 2.4 – the most conservative scenario they considered, meaning Imperial’s upper range of projections anticipated substantially higher death tolls. The countries examined here – Sweden, Taiwan, Japan, and South Korea – are distinctive for either eschewing lockdowns and similar aggressive NPI restrictions entirely or for relying on them in a much more limited scope than Imperial College advised. The United States, where 43 of 50 states adopted lockdowns of some form, is also included for comparison.

Figure II: Performance of Imperial College Modeling in 4 Non-Lockdown Countries & the United States,

Country (assumed R0 = 2.4)	Imperial Model projected deaths – social distancing (lockdowns)	Imperial Model projected deaths -unmitigated spread	1 year actual deaths (3/26/21)	Overestimate, Lockdown scenario	Overestimate, Unmitigated scenario	Overestimate Percentage – Lockdowns	Overestimate Percentage – Unmitigated
Sweden	30,434	66,393	13,496	16,938	52,897	126%	392%
Taiwan	93,712	179,828	10	93,702	179,818	937020%	1798180%
South Korea	141,198	301,352	1,716	139,482	299,636	8128%	17461%
Japan	469,064	1,055,426	8,967	460,097	1,046,459	5131%	11670%
United States	1,099,095	2,186,315	563,285	535,810	1,623,030	95%	288%

(Editor's Note: I couldn't reproduce this chart as presented, so I reformed it from an Excel file.  Go to the original for a better view.  RK)

As can be seen, Imperial College wildly overstated the projected deaths in each country under both its “unmitigated” scenario and its NPI-reliant “social distancing” scenario – including by orders of magnitude in several cases.

Similar exaggerations may be found in almost every other country where Imperial released projections, even as most of them opted to lock down. The Imperial team’s most conservative model predicted 332,000 deaths in France under lockdown-based “social distancing” and 621,000 with “unmitigated” spread. At the one year mark, France had incurred 94,000 deaths. Belgium was expected to incur a minimum of 46,000 fatalities under NPI mitigation, and 91,000 with uncontrolled spread. At the one year anniversary of the model, it reached 23,000 deaths – among the highest tolls in the world on a per capita basis and an example of extreme political mismanagement of the pandemic under heavy lockdown to be sure, but still only half of Imperial College’s most conservative projection for NPI mitigation.

Just over one year ago, the epidemiology modeling of Neil Ferguson and Imperial College played a preeminent role in shutting down most of the world. The exaggerated forecasts of this modeling team are now impossible to downplay or deny, and extend to almost every country on earth. Indeed, they may well constitute one of the greatest scientific failures in modern human history.

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Phillip W. Magness

Phil Magness is a Senior Research Fellow at the American Institute for Economic Research.

He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.

Books by Phillip W. Magness

 

The Disease Models Were Tested and Failed, Massively

Phillip W. Magness  – March 19, 2021 @ American Institute for Economic Research

One year ago this week the world embraced a lockdown strategy premised on the epidemiology modeling of Imperial College-London (ICL). In a March 16, 2020 report by physicist and computer modeler Neil Ferguson, the ICL team predicted catastrophic death tolls in the United Kingdom and United States unless both countries adopted an aggressive policy response of mandating social distancing, school and business closures, and ultimately sheltering in place.

Ferguson’s model presented a range of scenarios under increasingly restrictive nonpharmaceutical interventions (NPIs). Under its “worst case” or “do nothing” model 2.2 million Americans would die, as would 510,000 people in Great Britain, with the peak daily death rate hitting somewhere around late May or June. At the same time, the ICL team promised salvation from the coronavirus if only governments would listen to and adopt its technocratic recommendations. Time was of the essence to act, so President Donald Trump and UK Prime Minister Boris Johnson both listened. And so began the first year of “two weeks to flatten the curve.”

It took a little over a month before we saw conclusive evidence that something was greatly amiss with the ICL model’s underlying assumptions. A team of researchers from Uppsala University in Sweden adapted Ferguson’s work to their country and ran the projections, getting similarly catastrophic results. Over 90,000 people would die by summer from Covid-19 if Sweden did not enter immediate lockdown. Sweden never locked down though. By May it was clear that the Uppsala adaptation of ICL’s model was off by an order of magnitude. A year later, Sweden has fared no worse than the average European lockdown country, and significantly better than the UK, which acted on Ferguson’s advice.

Pressed on this unexpected result, ICL tried to distance itself from the Swedish adaptation of its model in May. The records from the March 21st supercomputer run of the Uppsala team’s projections belie that assertion, linking directly to Ferguson’s March 16^th report as the framework for its modeling design. But no matter – the ICL team’s own publications would soon succumb to a real-time testing against actual data.

A second ICL report, attempting to model the reopening of the United States from lockdowns, wildly exaggerated the death tolls that were expected to follow. By July, this model too had failed to even minimally correspond to observed reality. ICL attempted to save face by publishing an absurd exercise in circular reasoning in the journal Nature where they invoked the unrealized projections of their own model to supposedly “prove” multiple millions of lives had been saved by the lockdowns. That study soon failed basic robustness checks when the ICL team’s suite of models were applied to different geographies. 

Another team of Swedish researchers then noticed oddities in the ICL team’s coding, suggesting they had modified a key line to bring data from their own comparative analysis of Sweden into sync with other European countries under lockdown after the models did not align. A published derivative of this discovery showed that ICL’s own attempts to validate the effectiveness of its lockdown strategies does not withstand empirical scrutiny. 

Finally, in November, another team of researchers from the United States compared a related ICL team model for a broader swath of countries against five other international models of the pandemic, examining the performance of each against observed deaths. Their results contain a stunning indictment: “The Imperial model had larger errors, about 5-fold higher than other models by six weeks. This appears to be largely driven by the aforementioned tendency to overestimate mortality.”

The verdict is in. Imperial College’s Covid-19 modeling has an abysmal track record – a characteristic it unfortunately shares with Ferguson’s prior attempts to model mad cow disease, swine flu, avian flu, and countless other pathogens.

After a year of model-driven lockdowns, we may also look back to the original March 16, 2020 report to see yet another failure of its predictive ability. Recall that this is the model that fueled the alarmist rush to shut everything down last March, all to avert a 2.2 million death toll that would presumably peak around June.

 (Source: Imperial College Report #9, March 16, 2020)

As noted above, the 2.2 million figure for the US (and corresponding 510,000 figure in Britain) were “worst case” scenarios in which the pandemic ran its course. According to the underlying theory of the ICL model, these catastrophic totals could be reduced by the adoption of NPIs – the escalating suite of social distancing measures, business and school closures, and ultimately full lockdowns that we observed in practice over the last year.

Aside from its 2.2 million worst case scenario, ICL offered no specific projections for how its proposed mitigation measures would work in the United States. Ferguson did however tell the New York Times on March 20, 2020 that a “best case” American scenario would still yield “about 1.1 million deaths,” giving us a glimpse of what he believed to be possible under NPI mitigation. The March 16th report similarly “predict[ed] there would still be in the order of…1.1-1.2 million in the US” under the most optimistic mitigation strategy, barring a large increase in hospital ICU bed capacity.

By contrast, ICL did publish an extensive table showing the results of its model run for Britain over a group of four increasingly stringent NPI scenarios. These range from the “worst case” projection with half a million deaths (the figures vary depending on assumptions about the virus’s reproduction rate) to a more stringent model where four NPIs (public school closures, case isolation, home quarantine, and social distancing) are simultaneously enacted. The results are depicted below.

(Source: Imperial College Report #9, March 16, 2020)

Note that the UK enacted policies based on all four measures recommended by the March 16^th report, as well as an even more stringently enforced general lockdown on three separate occasions. After one year of following and expanding upon the Imperial College strategy, an unusual result appears in the data: not only have the UK’s numbers come up far short of Ferguson’s most alarmist scenario (depicted in the first column), but the UK has actually done much worse than the other NPI mitigation models in the ICL report.

As of the 1-year anniversary, the UK had a little over 125,000 confirmed Covid-19 deaths. By implication, the UK death toll has exceeded the mildest of the other three NPI scenarios from the ICL model (column 2) and blown past its heavier NPI recommendations (columns 3 and 4), even while operating under a more stringent set of lockdowns than ICL originally contemplated.

The implications are clear. While Ferguson wildly exaggerated the “worst case” scenario for the UK, he also severely overestimated the effectiveness of NPIs at controlling the pandemic.

By building its policy response around the Imperial College model, the UK government delivered the worst of both worlds. It imposed some of the most severe and long-lasting lockdowns in the world based on the premise that NPIs would work as Ferguson’s team predicted, and that such actions were needed to avert a catastrophe. Except the lockdowns did not work as intended, and the UK also ended up with an abnormally high death count compared to other countries – including locales that did not lock down, or that reopened earlier and for longer periods than the UK.

Why were the Ferguson/ICL predictions so far off base on both ends? The answer likely derives from two central flaws in their model design.

First, Ferguson adapted the model directly from a 2006 influenza pandemic model that he published in the journal Nature. As with the March 16^th Covid report, this study aimed to predict the spread of a virus across the general population, subject to a suite of increasingly stringent NPI countermeasures. As the second-to-last paragraph of the study reveals though, it only modeled general population spread. In doing so, the authors acknowledged that “Lack of data prevent us from reliably modelling transmission in the important contexts of residential institutions (for example, care homes, prisons) and health care settings.”

With Covid-19 however, nursing homes have emerged as one of the greatest vulnerabilities in the pandemic. In many locales, nursing home deaths alone account for almost half of all Covid-19 fatalities despite housing only a tiny fraction of the population. While the latest nursing home figures for the UK are as of yet hard to come by, reports from last year suggest they are not only a large share of the country’s Covid-19 deaths but also severely undercounted in official records. Using a preliminary count from last year, the UK had one of the worst nursing home shielding ratios in Europe – a measure that compares a country’s death toll in its care facilities to the general population. The ICL projections likely missed this problem entirely due to a defect in the 2006 model it was built upon.

Second, Ferguson’s model severely overstated the effectiveness of NPIs at mitigating general population spread. Part of the appeal of the ICL report from last March came from its succinct portrayal of the available policy options and their claimed effects. The modelers presented political leaders with a menu of escalating measures to adopt with mathematical precision, each linked to an associated projection of its effectiveness at staving off the pandemic. All the politicians had to do was select from the menu and implement the prescribed course.

Except it wasn’t that simple in practice. ICL’s recommended NPI measures baked assumptions about their own effectiveness into the model. In reality, most of these assumptions had never been tested or even minimally quantified. As a key chart from the March 16^th report illustrates, the supposed effect of each NPI was little more than a guesstimate – a set of nice, round numbers that purported to show the change in social interactions after its adoption. 

A 2019 report by the World Health Organization (WHO) warned of the flimsy empirical basis for epidemiology models such as the one developed by ICL. “Simulation models provide a weak level of evidence,” the report noted, and lacked randomized controlled trials to test their assumptions. The same report designated mass quarantine measures – what we now know of as lockdowns – as “Not Recommended” due to lack of evidence for their effectiveness. Summarizing this literature, which included the same 2006 influenza model that Ferguson adapted to Covid-19, the WHO concluded: “Most of the currently available evidence on the effectiveness of quarantine on influenza control was drawn from simulation studies, which have a low strength of evidence.”

The UK’s experience under the ICL model therefore demonstrates not only Ferguson’s propensity toward wildly alarmist disease forecasting – it also illustrates the abject failure of lockdowns and related NPI measures to mitigate the pandemic. As a revealing point of comparison, the UK’s population-adjusted daily death toll under lockdowns has been consistently higher than no-lockdown Sweden for most of the pandemic, despite both countries following a nearly identical pattern of timing in both the first and second waves. 

The relevant question, then, is not whether the UK failed to lock down stringently enough, but whether lockdowns offer any meaningful benefit whatsoever in mitigating the pandemic. A growing body of empirical data strongly suggests they do not.

The repeated failures of the Ferguson/ICL model point to a scientific error at the heart of the theory behind lockdowns and similar NPIs. They assume, without evidence, that their prescriptive approach is correct, and that it may be implemented by sheer will as one might achieve by clicking a check-box in a Sim City-style video game. After a year of real-time testing, it is now abundantly clear that this video game approach to pandemic management ranks among the most catastrophic public health policy failures in the last century.

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Phillip W. Magness

Phil Magness is a Senior Research Fellow at the American Institute for Economic Research.

He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.

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Anatomy of the Nursing Home Death Warrants

Jordan Schachtel – February 16, 2021 @ American Institute for Economic Research 

 

 

New York Governor Andrew Cuomo is finally facing the heat for his botched and criminally negligent coronavirus response policies, yet no one seems to be asking why Cuomo and select governors made the fateful decisions that led to the excess deaths — and the coverup campaigns — of tens of thousands of senior citizens in New York and elsewhere across the United States.

After being awarded an Emmy and writing a book on his supposedly heroic response to the pandemic, Cuomo is finally receiving the very necessary inquiries into his handling of the crisis. Cuomo is perhaps the most egregious example of abuse and neglect (given his refusal to use the Javits Center or a Navy hospital ship), he is far from the only governor who executed the “nursing home death warrants.” Governor Cuomo was accompanied by the governors of California, New Jersey, Pennsylvania, Michigan, and elsewhere.

The common thread seen in the United States is the delegation of state policy to prediction modeling forecasts from the Institute for Health Metrics and Evaluation (IHME), a Washington State-based institution that is wholly controlled and funded (to the tune of hundreds of millions of dollars) by The Bill and Melinda Gates Foundation.

In March and early April, politicians were informed by the modeling “experts” at Gates-funded IHME that their hospitals were about to be completely overrun by coronavirus patients. Modelers from IHME claimed this massive surge would cause hospitals to run out of lifesaving equipment in a matter of days, not weeks or months. Time was of the essence, and now was the time for rapid decision making, the modelers claimed. 

On two separate April 1 and April 2 press conferences, Cuomo made clear that his policy decisions were based off of the IHME model.

“There is a group that is funded by the Gates Foundation. Thank you very much Bill Gates,” Cuomo said on April 1 in discussing ICU needs and how he was using Gates models to make other healthcare policy decisions.

“There’s only one model that we look at that has the number of projected deaths which is the IHME model which is funded by the Gates Foundation,” Cuomo said on April 2, adding, “and we thank the Gates Foundation for the national service that they’ve done.”

In an April 9 briefing, Michigan Governor Gretchen Whitmer referred to the IHME model in order to project deaths and the PPE resources needed for the supposed surge.

It was the same story with the government of Pennsylvania. The PA Health Department exclusively uses IHME models to forecast coronavirus outcomes.

Governor Phil Murphy, another nursing home death warrant participant, used IHME models to navigate the state’s policy response.

It wasn’t just state governors relying on this data, federal bureaucrats Dr. Anthony Fauci and Dr. Deborah Birx, both of whom have substantial ties to the Gates network, used the IHME COVID-19 forecasting models (which Birx endorsed specifically as the best prediction modeling outfit) to make policy recommendations to states. In her White House briefings, Birx, who simultaneously had a seat on the board of a Gates-funded institution, almost exclusively relied on IHME models to project outcomes.

These models, and the policy decisions that were made by relying on them, set off a chain of events that led to indefinite lockdowns, complete business closures, statewide curfews, and most infamously, the nursing home death warrants.

States across the nation went to extremes, resorting to full bunker mode while waiting for bodies to start dropping in the streets, but the IHME modeling never panned out. Hospital capacity was never threatened. Most states that had created “surge capacity” pop-up health care centers never even used these facilities. IHME, for its part, regularly “adjusts” its models, and has never acknowledged their routine failures to forecast outcomes.

Bill Gates has never discussed the catastrophic failures of his prized “health metrics” forecasting organization, and how it has contributed to the suffering of millions of Americans. Instead, he has seamlessly washed his hands of COVID mania, and has moved on to demanding that the western world sacrifice itself in the name of the latest “crisis” that is climate change.

In December, however, Melinda Gates acknowledged that “we hadn’t really thought through the economic impacts “ of demanding that people stay locked in their houses indefinitely, among other policy requests demanded by Gates Inc.

The IHME models that demanded lockdowns and other insane restrictions relied entirely on sketchy COVID-19 data coming from the city of Wuhan, China. The early statistics concerning deaths, hospitalizations, and overall age stratification have not come close to matching the actual data on the virus. For example, IHME used a 3+% death rate when the real number *from* COVID-19 is only around 0.1%. IHME’s risk projections, which they presented as sound science, were all incredibly overinflated.

The buck does indeed stop with the elected leaders who made the fateful decisions to send sick COVID patients into nursing homes, lock down their states, and mask up their citizens in perpetuity, but that’s only half of the story. The bad data they used almost exclusively came from the Gates network, which has trafficked in pseudoscience and has demonstrated complete incompetence and reckless forecasting since the beginning of last year. 

Reprinted from the author’s blog

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Jordan Schachtel

Jordan Schachtel is an investigative journalist and foreign policy analyst based in Washington, D.C.

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It's possible that 2020's election fraud is way bigger than we thought

November 9, 2020 By Andrea Widburg 

Two massive Twitter threads contend that serious computer fraud two years ago in Texas, which turned strong red districts blue, was essentially a trial run for what's been playing out across America in this election.  This post summarizes the core claims in those threads, along with interlineations of information I found. One Twitter thread comes from Roscoe B. Davis, the other from Bad Kitty.  Scytl is a Barcelona-based company that provides electronic voting systems worldwide, many of which have proven vulnerable to electronic manipulation.  Scytl has (or had) Soros and Democrat party connections.  Microsoft co-founder Paul Allen's Vulcan Capital has invested $40 million in Scytl.............To Read More...

 

CLONING THE VOTE Soviet Style: Did DEMs Use HAMMER / SCORECARD To Repeatedly CLONE Blocks of Biden Votes?

By Mary Fanning, 12 November, 2020

 By WVW Broadcast Network National Security Authors Mary Fanning and Alan Jones | November 12, 2020 - Multiple injections into state election systems in 10 states of cloned, identically-sized blocks of Biden votes were used to steal the 2020 election from President Trump, according to Dennis Montgomery.  Montgomery is the CIA whistleblower who was granted immunity by the DOJ AND FBI for exposing illegal U.S. domestic surveillance.

The votes were created by SCORECARD and other technology and then replicated multiple times.  

Sidney Powell, the super attorney working with President Trump’s legal team to investigate the 2020 election results, told Maria Bartiromo of Fox News this morning that “we have evidence of the same number of ballots or same number of votes being injected into the Wisconsin system and into the Michigan system three different times. I think there were two different injections of exactly the same numbers in Michigan and then in Milwaukee it happened three times.”...........To Read More....

1.  Did American Patriots get HAMMERED by the globalist deep-staters?
2.  The HAMMER is the Key to the Coup With General McInerney and Mary Fanning
3. Worldview Radio Exclusive: Retired Three-Star General and National Security Expert Warn of the HAMMER (Software) That Flipped Votes to Carry Out a Coup D'etat on President Trump 
4. Whistleblower Tapes: Trump Wiretapped “A Zillion Times” By ‘The Hammer,’ Brennan’s and Clapper’s Secret Computer System
5.  DHS Cyber Director Chris Krebs And Deputy Director Matt Travis Tied To Clapper Who Commandeered HAMMER / SCORECARD
6.  A Constitutional Crisis
7.  DHS Cyber Director Chris Krebs And Deputy Director Matt Travis Tied To Clapper Who Commandeered HAMMER / SCORECARD
8. America's Election System: Gen. McInerney
9.  Digital Forensic Analysis Shows Dominion’s Server Connected to Iran and China: Affidavit

Too Much Faith in Models

John Stossel Jul 15, 2020

Between 2 million and 3 million Americans will die!

That was the prediction from "experts" at London's Imperial College when COVID-19 began. They did also say if there was "social distancing of the whole population," the death toll could be cut in half, but 1.1 million to 1.46 million Americans would still die by this summer. Our actual death toll has been about one-tenth of that.

Nevertheless, Imperial College's model was extremely influential. Politicians issued stay-at-home orders. They said we must trust the "experts." "Follow the science. Listen to the experts. Do what they tell you," said Joe Biden, laughing at what he considered an obvious truth. But "there is no such thing as "the science!" replies science reporter Matt Ridley in my new video about "expert" predictions. "Science consists of people disagreeing with each other!".....................

If............ politicians had just done just a little research, then they would have known that Imperial College researchers repeatedly predict great disasters that don't happen. Their model predicted 65,000 deaths from swine flu, 136,000 from mad cow disease, and 200 million from bird flu.  The real numbers were in the hundreds.....................To Read More....

Oxford epidemiologist on COVID-19 lockdowns: 'We might have done better by doing nothing at all'

"The epidemic has largely come and is on its way out" 

By Aaron Colen

A University of Oxford professor who produced a competing model to the apocalyptic Imperial College model said there's a chance that doing nothing would've been a more effective COVID-19 response than the various lockdowns implemented around the world. Sunetra Gupta, professor of theoretical epidemiology at Oxford, said the virus is on its way out in the United Kingdom, and the true infection fatality rate is likely extremely low.

"I think the epidemic has largely come and is on its way out in this country so I think it would definitely be less than 1 in 1,000 (0.1%) and probably closer to 1 in 10,000 (0.01%)," Gupta said.
Gupta is opposed to the lockdowns, pointing out that the coronavirus outbreak has followed similar patterns in different countries with different lockdown policies.............

She blamed government overreaction from the worst-case scenario projections from the Imperial College model, which showed the potential for 500,000 deaths in the U.K. and more than 2 million in the United States.... .To Read More....

Wrecking the economy with discredited computer models

 May 13, 2020 By Ellen Sauerbrey

Neil Ferguson, a mathematical epidemiologist at Imperial College London, panicked policy makers around the world when he released his computer model projecting that the coronavirus pandemic would result in 500,000 deaths in the U.K. and 2.2 million in the U.S. It is hard to understand why his pronouncement was accepted so uncritically when he has a history of being wrong. Very wrong.

• According to The Spectator, in 2005, Ferguson said that up to 200 million people could be killed from bird flu. The actual number was several hundred. 
•  In 2009, he gave an estimate of 65,000 deaths from the Swine Flu in the U.K. U.K. deaths actually numbered fewer than 500. 

Ferguson's dangerously inflated computer models have led to policies that are wrecking the American economy............... To Read More

Media-produced coronavirus fear is killing our country

April 25, 2020 By Leslie Taha

The media's incredibly irresponsible "shock and awe" reporting on the coronavirus has created a monumental stampede of fear and panic, which is a million times more damaging than the virus itself.

Here are two facts that say it all:

• Fact #1. Deaths in the U.S. from influenza from 1950 to 2017 ranged from 13.5 to 53.7 per 100,000.
• Fact #2. Deaths in the U.S. (as of this writing) from the coronavirus are 14.9 per 100,000. Bear in mind that this number includes "probable" deaths as well as confirmed deaths from the coronavirus.

To sum this up, the per capita deaths in the U.S. from the coronavirus are lower than the per capita deaths from the flu in almost every year from 1950 to 2017. The highest number of deaths from the flu per capita was in 1960. Deaths that year were 53.7 per 100,000, well over three times as high as the coronavirus. These are the facts, not crazy media hype. Please check them out for yourself.

Incidentally, the per capita death rate from what the hysterical media love to call a "global pandemic" is 2.4 per 100,000.............The White House Coronavirus Task Force's newest model predicts that coronavirus deaths could reach 60,000. It currently stands at 49,000. Even if that number were to double to 98,000, it would still be much lower than all the per capita flu deaths from 1950 to 1998. ..........To Read More....

The coronavirus modellers are saying even North Dakota needs to stay locked down until July?

April 25, 2020 By Jack Hellner

The University of Washington's all-important Institute for Health Metrics and Evaluation (IMHE), which is those people who are bringing us all of these garbage-in-garbage-out coronavirus "models" that are killing our economy, is now saying the new safe opening date for North Dakota is July.

According to the latest statistics I can find, North Dakota has had 15 deaths attributed to COVID 19 in the last two months, based on state data.

North Dakota is a remote, sparsely populated, spread out state, well beyond the heavy urban centers where the coronavirus has exacted a heavy toll.

I found that around 6,000 people die in North Dakota every year or 500 per month.

So this group of supposed experts that wants to destroy the lives, wealth and income of tens of million of Americans, wants to keep North Dakota closed because of fifteen deaths (1.5% of total deaths) and most of those deaths are older people who have multiple morbidity risks......To Read More.....


My Take -In 2017 the population of North Dakota was 755,393.  Fifteen people died of coronavirus, supposing they've determined that accurately, which has become an issue in other areas.  So, if it's true, and for convenience sake, we will suppose it is, what's the real death rate for coronavirus in that North Dakota?   0.00199%!  So, North Dakota should destroy the state's economy for what reason?  We know this has nothing to do with public health.  It has everything to do with destroying the American economy, the American identity and overthrowing the U.S. Constitution, leading to the ultimate destruction of America.  Why is that so hard to grasp? 

These "expert modelers" need to be investigated, fired, and possibly prosecuted for fraud. 

 

'The Pretense of Knowledge' has Cost America Dearly

April 25, 2020 By Nicholas J. Kaster

Recently, Brit Hume, the sober and understated Fox News commentator, voiced the thoughts of millions when he said,

“I think its time to consider the possibility… that this lockdown, as opposed to the more moderate mitigation efforts… is a colossal public policy calamity.”

The financial extent of the calamity was quantified by economist Scott Grannis when he observed that “almost overnight, we have wiped out all the net job gains of the past 14 years.” He made that comment on April 12 and the losses aren’t over yet. Grannis bluntly concluded that, “The shutdown of the U.S. economy will prove to be the most expensive self-inflicted injury in the history of mankind.”

The loss of liberty incurred as a result of shutdown is not as easily quantifiable, but is no less significant.

Epidemiological “models” have provided the scientific basis for this large-scale abrogation of personal and economic liberty. Now that the models have been shown to be grossly inaccurate, some are demanding accountability..........To Read More....