principiascientificinternational--2020-06-05--Top Italian Doctor: COVID19 ‘is Losing Potency’

Dr Alberto Zangrillo , the head of intensive care at the San Raffaele hospital in Milan in Lombardy ( the epicenter of Italy ’ s coronavirus outbreak ) , caused a stir on Sunday by telling Italian media that a study by his colleague had shown that the virus was losing its potency .
Zangrillo , who is well-known for being the personal doctor of Italy ’ s former President Silvio Berlusconi .
Meanwhile , the discredit UN World Health Organization claims the coronavirus is still a “ killer virus ” A very public dispute has broken out between some @ @ @ @ @ @ @ world ’ s , most high-profile doctors after one expert claimed the coronavirus “ no longer exists clinically.
” Dr. Alberto Zangrillo , the head of intensive care at the San Raffaele hospital in Milan in Lombardy ( the epicenter of Italy ’ s coronavirus outbreak ) , caused a stir on Sunday by telling Italian media that a study by his colleague had shown that the virus was losing its potency .
Zangrillo , who is well-known for being the personal doctor of Italy ’ s former Prime Minister Silvio Berlusconi , @ @ @ @ @ @ @ weakening and that , “ in reality , from the clinical point of view , the virus no longer exists.
” “ The swabs that were performed over the last 10 days showed a viral load in quantitative terms that was absolutely infinitesimal compared to the ones carried out on patients a month or two ago , ” he told RAI television Sunday , citing a study from Massimo Clementi , director of the Microbiology and Virology Laboratory at the San Raffaele hospital , that is reportedly due to be published shortly .
@ @ @ @ @ @ @ and absolute bewilderment for the statements made by Professor Zangrillo .
Just look at the number of new cases confirmed every day to have evidence of the persistent circulation of the virus in Italy , ” Italian news agency ANSA reported Monday .
The controversy over the comments comes at a delicate time for Italy , where Europe ’ s coronavirus outbreak was first detected back in February .
Lockdown measures are being lifted throughout the country with much of public life reopened .
On Wednesday , inter-regional travel will be allowed to @ @ @ @ @ @ @ reported 233,197 confirmed cases of the virus and 33,475 deaths .
Globally , the virus has infected more than 6.2 million people and at least 375,000 people have died , according to Johns Hopkins University .
PRINCIPIA SCIENTIFIC INTERNATIONAL , legally registered in the UK as a company incorporated for charitable purposes .
Head Office : 27 Old Gloucester Street , London WC1N 3AX .
Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method .

Top Italian Doctor: COVID19 ‘is Losing Potency’
Written by Holly Ellyatt
Dr Alberto Zangrillo, the head of intensive care at the San Raffaele hospital in Milan in Lombardy (the epicenter of Italy’s coronavirus outbreak), caused a stir on Sunday by telling Italian media that a study by his colleague had shown that the virus was losing its potency.
Zangrillo, who is well-known for being the personal doctor of Italy’s former President Silvio Berlusconi. Meanwhile, the discredit UN World Health Organization claims the coronavirus is still a “killer virus”
A very public dispute has broken out between some of Italy’s, and the world’s, most high-profile doctors after one expert claimed the coronavirus “no longer exists clinically.”
Dr. Alberto Zangrillo, the head of intensive care at the San Raffaele hospital in Milan in Lombardy (the epicenter of Italy’s coronavirus outbreak), caused a stir on Sunday by telling Italian media that a study by his colleague had shown that the virus was losing its potency. Zangrillo, who is well-known for being the personal doctor of Italy’s former Prime Minister Silvio Berlusconi, said the study showed the virus was weakening and that, “in reality, from the clinical point of view, the virus no longer exists.” “The swabs that were performed over the last 10 days showed a viral load in quantitative terms that was absolutely infinitesimal compared to the ones carried out on patients a month or two ago,” he told RAI television Sunday, citing a study from Massimo Clementi, director of the Microbiology and Virology Laboratory at the San Raffaele hospital, that is reportedly due to be published shortly.
“I can only express great surprise and absolute bewilderment for the statements made by Professor Zangrillo. Just look at the number of new cases confirmed every day to have evidence of the persistent circulation of the virus in Italy,” Italian news agency ANSA reported Monday. The controversy over the comments comes at a delicate time for Italy, where Europe’s coronavirus outbreak was first detected back in February. Lockdown measures are being lifted throughout the country with much of public life reopened. On Wednesday, inter-regional travel will be allowed to resume. To date, Italy has reported 233,197 confirmed cases of the virus and 33,475 deaths. Globally, the virus has infected more than 6.2 million people and at least 375,000 people have died, according to Johns Hopkins University. Read the full story at www.cnbc.com ***** PRINCIPIA SCIENTIFIC INTERNATIONAL, legally registered in the UK as a company incorporated for charitable purposes. Head Office: 27 Old Gloucester Street, London WC1N 3AX. Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method.
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principiascientificinternational--2020-06-05--The False World of Climate & COVID-19 Computer Models

COVID-19 has proved to be a crisis not only for public health but for public policy .
As credentialed experts , media commentators , and elected officials have insisted that ordinary men and women heed “ the science , ” the statistical models cited by scientists to predict the spread of contagion and justify the lockdown of the national economy have proven to be far off-base .
Gov .
Andrew Cuomo of New York complained this week about the “ guessing business ” experts had presented to him dressed up as scientific fact : “ All the early national experts @ @ @ @ @ @ @ my projection model .
Here ’ s my projection model , ” Cuomo said .
“ They were all wrong .
They were all wrong.
” A computer model produced by statisticians at Imperial College London had an outsized effect on government policy , predicting up to 2.2 million American deaths from the new coronavirus and as many as 9.6 million people requiring hospitalization .
Instead , emergency rooms and hospital beds in all but the few hardest-hit cities remained empty ; rather than being overwhelmed by cases , many doctors and nurses @ @ @ @ @ @ @ the staggering social and economic costs of the shutdown have become painfully clear , the failure of the models to accurately anticipate what would happen is raising questions about their use to justify life-altering public policies .
If computer models projecting the near-term future of an epidemic were so wrong , what does that mean for the far more complicated computer models predicting the far-off future of the entire planet ?
As Texas Sen. John Cornyn tweeted : “ After # COVID-19 crisis passes , could we have a good-faith discussion about the @ @ @ @ @ @ @ to predict the future ?
Everything from public health , to economic to climate predictions .
It isn ’ t the scientific method , folks.
” Scientific American sought to dismiss such concerns in an April 15 article headlined “ Climate Science Deniers Turn to Attacking Coronavirus Models.
” While not exactly defending the methodology used in the models , the article said they were wrong “ because millions of Americans responded to pleas for social distancing.
” It then invoked newer models that would also prove to be wrong – forecasting only @ @ @ @ @ @ @ more than 107,000 – before defending the original alarmist numbers with what almost sounds like an argument for the politicization of science from the coronavirus to climate change : Building complex models is both a science and an art .
It requires vast amounts of data representing a range of factors that might influence a particular question .
To predict the spread of COVID-19 , for example , researchers need reliable data on a wide range of factors including how infectious the virus is , how it is transmitted , how much of @ @ @ @ @ @ @ outcomes .
They have to assign a weight to each factor in the model , and then crunch the numbers with powerful computers to produce probabilities of possible outcomes .
Models may be helpful in thinking about the results of various policies .
But they are easily oversold as providing answers with mathematical certainty .
Writing in the BMJ ( formerly the British Medical Journal ) , Devi Sridhar , a professor of public health at Edinburgh University , and Maimuna Majumder , a computational epidemiologist affiliated with Harvard Medical School , chide @ @ @ @ @ @ @ to make the limitations of models clear .
Sridhar and Majumder call for transparency about the assumptions modelers make and clarity about how much the predictions shift when even small changes are made to the assumptions .
Most of all , they urge humility about just how uncertain such models are .
In an article in the Annals of Internal Medicine – “ Caution Warranted : Using the Institute for Health Metrics and Evaluation Model for Predicting the Course of the COVID-19 Pandemic ” – three prominent British and American researchers warned against @ @ @ @ @ @ @ and independent judgment .
“ This appearance of certainty is seductive , ” they wrote .
That false sense of certainty is particularly seductive “ when the world is desperate to know what lies ahead.
” Their critique was withering .
The flaws they found in the model from the Institute for Health Metrics and Evaluation at the University of Washington included several dubious assumptions : that social distancing would play out the same way everywhere , for one , and that curves could be expected to follow the same general patterns from @ @ @ @ @ @ @ the disease had spread – the essential data – was sketchy , plagued by “ inconsistent and poor reporting.
” When the projections were revised , the magnitude of the changes revealed “ substantial volatility.
” Volatile predictions are inherently uncertain .
But model-makers have presented their work with the impression of specificity .
On March 27 , for example , IHME predicted the number of COVID-19 deaths in New York would very likely be between 5,167 and 26,444 .
A rounded number – say , 10,000 – would have conveyed the ballpark @ @ @ @ @ @ @ the number the University of Washington group published was the very exact 10,243 .
As one statistician told RealClearInvestigations , the IHME projections suffer from the “ fallacy of misplaced concreteness.
” The Imperial College London model also suffered from uncertainty over what factors cause the disease to spread .
Consider musical concerts .
As states , counties , and cities in the U.S. attempt to reopen gradually , the last on the list to be liberated is likely to be live performances that entail “ mass gatherings.
” And yet , go @ @ @ @ @ @ @ – from the “ response team ” that did so much to stampede the U.K. into lockdown – and one finds this assessment of the danger of crowds : Respected scientists questioned not only the epidemiologists ’ efforts but the very value of such models .
“ I ’ ve spent a lot of time on the models , ” Dr. Anthony Fauci reportedly told his colleagues on the White House ’ s pandemic task force .
“ They don ’ t tell you anything .
You can ’ t really rely upon @ @ @ @ @ @ @ We are impressed with models in part because of their intellectual provenance : They are “ created by some of the cleverest people and often rely on some of the most advanced monitoring or simulation technologies available to us , ” according to Mike Hulme , a professor at the University of Cambridge and editor of last year ’ s “ Contemporary Climate Change Debates : A Student Primer.
” If one is in need of an oracle , models “ appear to offer authoritative and quantified predictions of the future , ” @ @ @ @ @ @ @ true for climate change as it is for a pandemic.
” Climate modeling and virus transmission modeling have certain similarities , says Hulme .
“ In both cases , models are alluring , claiming to offer a glimpse of the future denied to mere mortals , ” he told RealClearInvestigations .
“ Politicians easily get dazzled by them .
People easily confuse precision — models are good at that !
— with accuracy — models are rarely accurate.
” Which is why Hulme praises as wise the decision-maker who isn ’ t “ @ @ @ @ @ @ @ models.
” There are also differences : Climate models have a leg up on the COVID models if only because they ’ ve been tested for 20 to 30 years , and revised and adjusted accordingly , says Hulme .
The COVID modelers have been working with inconsistent , “ gappy ” data and untried assumptions .
And yet even with the decades of effort that has been put into climate , modelers struggle to predict phenomena such as regional rainfall .
The key message , ” Hulme tells RCI , is not @ @ @ @ @ @ @ world .
They are two separate places.
” All models are wrong , he says , but some are useful .
“ Models are far better as tools to help us think with than they are as truth oracles .
We must not think that models have some privileged access to ‘ the future.
’ That would likely lead to some very poor decisions.
” A core challenge for models is the sheer number of variables that must be taken into account .
With climate , that includes the amounts of greenhouse gases @ @ @ @ @ @ @ , and methane , but also soot and sulfur aerosols and the activity of the sun .
And then there are interactions to be accounted for .
Not only do aerosols themselves affect how much sunlight reaches Earth , but they also affect the formation of clouds , which in turn reflect sunlight out of the atmosphere .
If it is hard to model a single phenomenon , it is exponentially more difficult when a given model contains submodels , each with its own uncertainties .
“ Each time you add a new @ @ @ @ @ @ @ of freedom to the system with new feedbacks , ” says Judith Curry , former chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology .
“ Then when you couple the new submodel to the larger model , you add additional degrees of freedom to each variable that the new submodel connects with.
” In other words , with every submodel added the possibility of error compounds , multiplying the chance that the main model veers off target .
“ This issue , ” Curry says , @ @ @ @ @ @ @ of the problems and uncertainties in global climate models.
” The accuracy of climate model predictions also depends on assumptions made about future human behavior .
If a model is alarming , it ’ s worth checking whether it has built-in an unlikely eventuality , such as that by the end of the century industry will be burning five times the coal it is now .
But predictions of human behavior are inherently uncertain .
Whether disease or climate , a modeler has to anticipate the social issues that come into play .
@ @ @ @ @ @ @ isolation help create tinderbox conditions leading to nationwide rioting , looting , and arson ?
Crowds in the streets don ’ t maintain social distancing , which means that mass protests could affect the number of people who become infected by COVID-19 .
There is no one climate model .
The range of models used by the United Nations Intergovernmental Panel on Climate Change rely on different assumptions but have been in the ballpark of observed warming .
But there is regional variation in warming and many models have predicted rising temperatures in @ @ @ @ @ @ @ there .
Some climate scientists deny that COVID-19 models and climate models are anything alike .
They stand by the rigor of climate simulations while agreeing the disease projections are flawed : “ There are fundamental differences between epidemiological models and climate models , ” George Mason University climatology professor Jagadish Shukla told RealClearInvestigations .
“ The former are empirical models driven by incomplete data ; climate models are based on fundamental laws of physics and thermodynamics.
” But many researchers vigorously defend the coronavirus models .
“ Right answers are not what @ @ @ @ @ @ @ Tufekci wrote in The Atlantic .
“ When an epidemiological model is believed and acted on , it can look like it was false.
” What matters in this view is that a model spur action to change the outcome , not that it does anything so mundane as describe the real world .
Neither epidemic nor climate models attempt merely to predict what will happen .
Instead , they set out to project what will happen if people do or don ’ t change their behavior in response to the models .
@ @ @ @ @ @ @ be modest about the worst-case scenarios .
As one accomplished academic statistician told RealClearInvestigations , “ Part of the process is to scare people to get them to take things seriously.
” Physicist Lenny Smith , professor of statistics at the London School of Economics , says that many climate models operate on time frames beyond the lifespan of the modelers .
Often , he says , “ we can ’ t see the outcome being modeled for 150 years.
” By contrast , some of the COVID-19 models are being used to @ @ @ @ @ @ @ .
A group of Australian and American data scientists has been comparing the real coronavirus data of a given day against the IHME predictions made the day before .
Put to the test , the model has proved to have little predictive value .
“ COVID models are more easily evaluated since they are making short-term predictions , ” says Judith Curry .
“ Climate models are making predictions for decades into the future , ” she says .
“ By the time the climate change is actually realized , there will have @ @ @ @ @ @ @ ” In other words , the models get adjusted along the way , creating an appearance of accuracy .
It ’ s not done chaotically , as the epidemic model revisions have been , but rather in a systematic way that has been kept somewhat undercover .
The climate model revisions are called “ tuning , ” and were discussed in “ The Art and Science of Climate Model Tuning ” by Frédéric Hourdin and a dozen other climatologists in the Bulletin of the American Meteorological Society in 2017 .
“ [ T @ @ @ @ @ @ @ unavoidable but dirty part of climate modeling , ” they write , “ an act of tinkering that does not merit recording in the scientific literature.
” The tinkering consists of “ adjusting the values ” of the submodels after the fact , bringing “ the solution as a whole into line with aspects of the observed climate.
” The tinkering is not advertised , the scientists admit , because of “ concern that explaining that models are tuned , may strengthen the arguments of those claiming to question the validity of climate @ @ @ @ @ @ @ , climate modelers follow theories ; some use observations ; some just make a “ back-of-the-envelope estimate.
” But it isn ’ t done randomly : Hourdin et al .
write , “ [ S ] ome models are explicitly , or implicitly , tuned to better match the 20th-century warming.
” Whether it ’ s epidemiology , climate , or economics , says Sally Cripps of the University of Sydney , modelers need to “ acknowledge and explain the uncertainty ” in their enterprise .
“ The data science community in particular @ @ @ @ @ @ @ she says .
“ It needs to hose down claims about Big Data being a crystal ball , and instead use the data to understand what we don ’ t know .
That is the way forward.
” PRINCIPIA SCIENTIFIC INTERNATIONAL , legally registered in the UK as a company incorporated for charitable purposes .
Head Office : 27 Old Gloucester Street , London WC1N 3AX .
Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method .

The False World of Climate & COVID-19 Computer Models
Written by Eric Felten
COVID-19 has proved to be a crisis not only for public health but for public policy.
As credentialed experts, media commentators, and elected officials have insisted that ordinary men and women heed “the science,” the statistical models cited by scientists to predict the spread of contagion and justify the lockdown of the national economy have proven to be far off-base.
Gov. Andrew Cuomo of New York complained this week about the “guessing business” experts had presented to him dressed up as scientific fact: “All the early national experts [said]: Here’s my projection model. Here’s my projection model,” Cuomo said. “They were all wrong. They were all wrong.”
A computer model produced by statisticians at Imperial College London had an outsized effect on government policy, predicting up to 2.2 million American deaths from the new coronavirus and as many as 9.6 million people requiring hospitalization.
Instead, emergency rooms and hospital beds in all but the few hardest-hit cities remained empty; rather than being overwhelmed by cases, many doctors and nurses found themselves out of work.
As the staggering social and economic costs of the shutdown have become painfully clear, the failure of the models to accurately anticipate what would happen is raising questions about their use to justify life-altering public policies.
If computer models projecting the near-term future of an epidemic were so wrong, what does that mean for the far more complicated computer models predicting the far-off future of the entire planet?
As Texas Sen. John Cornyn tweeted: “After #COVID-19 crisis passes, could we have a good-faith discussion about the uses and abuses of ‘modeling’ to predict the future? Everything from public health, to economic to climate predictions. It isn’t the scientific method, folks.”
Scientific American sought to dismiss such concerns in an April 15 article headlined “Climate Science Deniers Turn to Attacking Coronavirus Models.”
While not exactly defending the methodology used in the models, the article said they were wrong “because millions of Americans responded to pleas for social distancing.”
It then invoked newer models that would also prove to be wrong – forecasting only 60,000 U.S. deaths; there are now more than 107,000 – before defending the original alarmist numbers with what almost sounds like an argument for the politicization of science from the coronavirus to climate change:
“Health experts say the models worked the way they were supposed to — by providing a glimpse into a dire future that was partially averted because of collective action.”
Building complex models is both a science and an art. It requires vast amounts of data representing a range of factors that might influence a particular question.
To predict the spread of COVID-19, for example, researchers need reliable data on a wide range of factors including how infectious the virus is, how it is transmitted, how much of the population is susceptible to the worst outcomes.
They have to assign a weight to each factor in the model, and then crunch the numbers with powerful computers to produce probabilities of possible outcomes.
Models may be helpful in thinking about the results of various policies. But they are easily oversold as providing answers with mathematical certainty.
Writing in the BMJ (formerly the British Medical Journal), Devi Sridhar, a professor of public health at Edinburgh University, and Maimuna Majumder, a computational epidemiologist affiliated with Harvard Medical School, chide the “modeling community” for failing to make the limitations of models clear.
Sridhar and Majumder call for transparency about the assumptions modelers make and clarity about how much the predictions shift when even small changes are made to the assumptions. Most of all, they urge humility about just how uncertain such models are.
In an article in the Annals of Internal Medicine – “Caution Warranted: Using the Institute for Health Metrics and Evaluation Model for Predicting the Course of the COVID-19 Pandemic” – three prominent British and American researchers warned against thinking computer calculations could replace sound data and independent judgment.
“This appearance of certainty is seductive,” they wrote. That false sense of certainty is particularly seductive “when the world is desperate to know what lies ahead.”
Their critique was withering. The flaws they found in the model from the Institute for Health Metrics and Evaluation at the University of Washington included several dubious assumptions: that social distancing would play out the same way everywhere, for one, and that curves could be expected to follow the same general patterns from country to country.
Evidence of how the disease had spread – the essential data – was sketchy, plagued by “inconsistent and poor reporting.” When the projections were revised, the magnitude of the changes revealed “substantial volatility.”
Volatile predictions are inherently uncertain. But model-makers have presented their work with the impression of specificity.
On March 27, for example, IHME predicted the number of COVID-19 deaths in New York would very likely be between 5,167 and 26,444. A rounded number – say, 10,000 – would have conveyed the ballpark nature of their guesstimate.
Instead, the number the University of Washington group published was the very exact 10,243. As one statistician told RealClearInvestigations, the IHME projections suffer from the “fallacy of misplaced concreteness.”
The Imperial College London model also suffered from uncertainty over what factors cause the disease to spread. Consider musical concerts. As states, counties, and cities in the U.S. attempt to reopen gradually, the last on the list to be liberated is likely to be live performances that entail “mass gatherings.”
And yet, go back to the Imperial College London study – from the “response team” that did so much to stampede the U.K. into lockdown – and one finds this assessment of the danger of crowds:
“Stopping mass gatherings is predicted to have relatively little impact because the contact-time at such events is relatively small compared to the time spent at home, in schools or workplaces and in other community locations such as bars and restaurants.”
Respected scientists questioned not only the epidemiologists’ efforts but the very value of such models. “I’ve spent a lot of time on the models,” Dr. Anthony Fauci reportedly told his colleagues on the White House’s pandemic task force. “They don’t tell you anything. You can’t really rely upon models.”
And yet we do.
We are impressed with models in part because of their intellectual provenance: They are “created by some of the cleverest people and often rely on some of the most advanced monitoring or simulation technologies available to us,” according to Mike Hulme, a professor at the University of Cambridge and editor of last year’s “Contemporary Climate Change Debates: A Student Primer.”
If one is in need of an oracle, models “appear to offer authoritative and quantified predictions of the future,” he says. “This is as true for climate change as it is for a pandemic.”
Climate modeling and virus transmission modeling have certain similarities, says Hulme. “In both cases, models are alluring, claiming to offer a glimpse of the future denied to mere mortals,” he told RealClearInvestigations. “Politicians easily get dazzled by them. People easily confuse precision — models are good at that! — with accuracy — models are rarely accurate.”
Which is why Hulme praises as wise the decision-maker who isn’t “sucked into the gravitational force fields of models.”
There are also differences: Climate models have a leg up on the COVID models if only because they’ve been tested for 20 to 30 years, and revised and adjusted accordingly, says Hulme.
The COVID modelers have been working with inconsistent, “gappy” data and untried assumptions. And yet even with the decades of effort that has been put into climate, modelers struggle to predict phenomena such as regional rainfall.
The key message,” Hulme tells RCI, is not to “mistake model-land for the real world. They are two separate places.” All models are wrong, he says, but some are useful.
“Models are far better as tools to help us think with than they are as truth oracles. We must not think that models have some privileged access to ‘the future.’ That would likely lead to some very poor decisions.”
A core challenge for models is the sheer number of variables that must be taken into account. With climate, that includes the amounts of greenhouse gases such as carbon dioxide, nitrous oxide, and methane, but also soot and sulfur aerosols and the activity of the sun.
And then there are interactions to be accounted for. Not only do aerosols themselves affect how much sunlight reaches Earth, but they also affect the formation of clouds, which in turn reflect sunlight out of the atmosphere.
If it is hard to model a single phenomenon, it is exponentially more difficult when a given model contains submodels, each with its own uncertainties.
“Each time you add a new submodel, you are adding new degrees of freedom to the system with new feedbacks,” says Judith Curry, former chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology. “Then when you couple the new submodel to the larger model, you add additional degrees of freedom to each variable that the new submodel connects with.”
In other words, with every submodel added the possibility of error compounds, multiplying the chance that the main model veers off target. “This issue,” Curry says, “remains at the heart of many of the problems and uncertainties in global climate models.”
The accuracy of climate model predictions also depends on assumptions made about future human behavior. If a model is alarming, it’s worth checking whether it has built-in an unlikely eventuality, such as that by the end of the century industry will be burning five times the coal it is now.
But predictions of human behavior are inherently uncertain. Whether disease or climate, a modeler has to anticipate the social issues that come into play.
Did sudden mass joblessness and government-enforced social isolation help create tinderbox conditions leading to nationwide rioting, looting, and arson?
Crowds in the streets don’t maintain social distancing, which means that mass protests could affect the number of people who become infected by COVID-19.
There is no one climate model. The range of models used by the United Nations Intergovernmental Panel on Climate Change rely on different assumptions but have been in the ballpark of observed warming.
But there is regional variation in warming and many models have predicted rising temperatures in Antarctic waters and loss of sea ice there.
Some climate scientists deny that COVID-19 models and climate models are anything alike. They stand by the rigor of climate simulations while agreeing the disease projections are flawed:
“There are fundamental differences between epidemiological models and climate models,” George Mason University climatology professor Jagadish Shukla told RealClearInvestigations. “The former are empirical models driven by incomplete data; climate models are based on fundamental laws of physics and thermodynamics.”
But many researchers vigorously defend the coronavirus models. “Right answers are not what epidemiological models are for,” Zeynep Tufekci wrote in The Atlantic. “When an epidemiological model is believed and acted on, it can look like it was false.”
What matters in this view is that a model spur action to change the outcome, not that it does anything so mundane as describe the real world.
Neither epidemic nor climate models attempt merely to predict what will happen. Instead, they set out to project what will happen if people do or don’t change their behavior in response to the models.
Modelers aren’t exactly incentivized to be modest about the worst-case scenarios. As one accomplished academic statistician told RealClearInvestigations, “Part of the process is to scare people to get them to take things seriously.”
Physicist Lenny Smith, professor of statistics at the London School of Economics, says that many climate models operate on time frames beyond the lifespan of the modelers.
Often, he says, “we can’t see the outcome being modeled for 150 years.” By contrast, some of the COVID-19 models are being used to predict what will happen the next day.
A group of Australian and American data scientists has been comparing the real coronavirus data of a given day against the IHME predictions made the day before. Put to the test, the model has proved to have little predictive value.
“COVID models are more easily evaluated since they are making short-term predictions,” says Judith Curry. “Climate models are making predictions for decades into the future,” she says. “By the time the climate change is actually realized, there will have been several generations of new climate models.”
In other words, the models get adjusted along the way, creating an appearance of accuracy. It’s not done chaotically, as the epidemic model revisions have been, but rather in a systematic way that has been kept somewhat undercover.
The climate model revisions are called “tuning,” and were discussed in “The Art and Science of Climate Model Tuning” by Frédéric Hourdin and a dozen other climatologists in the Bulletin of the American Meteorological Society in 2017.
“[T]uning is often seen as an unavoidable but dirty part of climate modeling,” they write, “an act of tinkering that does not merit recording in the scientific literature.”
The tinkering consists of “adjusting the values” of the submodels after the fact, bringing “the solution as a whole into line with aspects of the observed climate.”
The tinkering is not advertised, the scientists admit, because of “concern that explaining that models are tuned, may strengthen the arguments of those claiming to question the validity of climate change projections.”
To make those adjustments, climate modelers follow theories; some use observations; some just make a “back-of-the-envelope estimate.” But it isn’t done randomly: Hourdin et al. write, “[S]ome models are explicitly, or implicitly, tuned to better match the 20th-century warming.”
Whether it’s epidemiology, climate, or economics, says Sally Cripps of the University of Sydney, modelers need to “acknowledge and explain the uncertainty” in their enterprise.
“The data science community in particular needs a little more humility,” she says. “It needs to hose down claims about Big Data being a crystal ball, and instead use the data to understand what we don’t know. That is the way forward.”
Read more at RealClearInvestigations
PRINCIPIA SCIENTIFIC INTERNATIONAL, legally registered in the UK as a company incorporated for charitable purposes. Head Office: 27 Old Gloucester Street, London WC1N 3AX.
Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method.
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principiascientificinternational--2020-06-08--Biased BBC Invents ‘Hottest May On Record’

AFTER the BBC ’ s ‘ environment analyst ’ Roger Harrabin went into hysterics about last month ’ s sunny weather in his piece ‘ Climate change : May was sunniest calendar month on record in the UK ’ , it is appropriate to point out to him that last month was far from being the hottest on record in England : In fact , there have been 47 hotter Mays since 1659 .
The hottest was 1833 , and the five hottest were all pre-1850 .
We know , of course , that Harrabin has little interest in facts @ @ @ @ @ @ @ he really should consult the data since he might learn something .
He claims that May 2020 was the sunniest on record in the UK , but sunshine records did not start until 1929 .
I strongly suspect those scorching Mays in the 19th century were also unusually sunny .
What about spring as a whole ?
Again , nowhere near a record , tied at 9th warmest with 1952 .
Spring was also warmer back in 1893 : The spring of 1893 was the driest on record in England .
A look @ @ @ @ @ @ @ shows just how similar that spring was to this year ’ s , other than the fact that there was some rain towards the end of May : Far from the recent weather in the UK being unprecedented and astounding , or it being a part of a pattern of increasingly extreme weather , as Harrabin ’ s so-called fellow scientist travelers want us to believe , the spring of 1893 shows just how little England ’ s climate has changed in the last 127 years .
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Biased BBC Invents ‘Hottest May On Record’
Written by Paul Homewood
AFTER the BBC’s ‘environment analyst’ Roger Harrabin went into hysterics about last month’s sunny weather in his piece ‘Climate change: May was sunniest calendar month on record in the UK’, it is appropriate to point out to him that last month was far from being the hottest on record in England:
https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html
In fact, there have been 47 hotter Mays since 1659. The hottest was 1833, and the five hottest were all pre-1850.
We know, of course, that Harrabin has little interest in facts unless they suit his agenda. But he really should consult the data since he might learn something.
He claims that May 2020 was the sunniest on record in the UK, but sunshine records did not start until 1929. I strongly suspect those scorching Mays in the 19th century were also unusually sunny.
What about spring as a whole? Again, nowhere near a record, tied at 9th warmest with 1952. Spring was also warmer back in 1893:
The spring of 1893 was the driest on record in England. A look at the Met Office monthly weather summaries shows just how similar that spring was to this year’s, other than the fact that there was some rain towards the end of May:
https://digital.nmla.metoffice.gov.uk/SO_0ae033c1-0ae6-4bde-a493-5851547972e6/
Far from the recent weather in the UK being unprecedented and astounding, or it being a part of a pattern of increasingly extreme weather, as Harrabin’s so-called fellow scientist travelers want us to believe, the spring of 1893 shows just how little England’s climate has changed in the last 127 years.
But you won’t be told that by the BBC.
Read more at Conservative Woman
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principiascientificinternational--2020-06-08--DailyKos Admits It Favors Unscientific & Subjective Climate Alarm

The Daily Kos published an article claiming climate alarmists are more scientifically credible when they make things up out of thin air – while presenting no supporting scientific facts or evidence – than when climate realists cite numerous datasets and peer-reviewed studies that support their claims .
Making stuff up out of thin air is acceptable , honorable , and compelling , according to DailyKos , if you do it in the name of climate alarmism .
Earlier this week , we published an article here at Climate Realism calling attention to dubious claims made by prominent climate alarmist Katharine @ @ @ @ @ @ @ groups .
We specifically noted that Hayhoe uses an apparently self-made chart in her presentations that shows global temperatures steadily and monotonously declining , with almost no variations , for nearly 6,000 years .
Hayhoe ’ s chart reproduced below , then shows , at the end of the 6,000 years of steady cooling , an enormous temperature spike in the 20th century that completely overwhelms the previous 6,000 years of steady cooling .
Our Climate Realism article then showed what the United Nations Intergovernmental Panel on Climate Change ( IPCC ) itself @ @ @ @ @ @ @ – published in 1990 and confirmed by multiple studies since ( see , for example , pg .
14 here ) – in which scientists , the IPCC , and multiple datasets and peer-reviewed studies agreed that scientific evidence shows temperatures have varied frequently and considerably throughout the past 6,000 years .
The IPCC chart reproduced below , also showed temperatures currently remain cooler than was the case during most of the past 6,000 years .
As we pointed out in the Climate Realism article , Hayhoe provides no sources or supporting data @ @ @ @ @ @ @ , established science , and multiple supporting studies are all wrong .
She merely shows her own chart in public presentations and then uses her highly suspicious chart to support her claim that humans are creating an unprecedented and existential climate crisis .
DailyKos published an article childishly attacking the Climate Realism article .
The DailyKos article never cited any scientific evidence , data , or studies to support Hayhoe ’ s chart .
Of course , the only presented “ thirty years of research ” is Katharine Hayhoe ’ s self-serving , @ @ @ @ @ @ @ documentary evidence .
So , the alarmist argument is that if a climate alarmist simply makes up a chart or an argument out of thin air , that chart or argument is more credible than factually documented , evidentiary-based science that has been well established and that has remained in effect for many , many years .
• Climate alarmists can – and likely will – claim without any supporting evidence that the Sun revolves around the Earth because Copernicus ’ theory is “ old science ” demonstrated more than 400 years ago @ @ @ @ @ @ @ likely will – claim without any supporting evidence that the Earth is flat because it has been 500 years since Magellan circumnavigated the Earth .
• Climate alarmists can – and likely will – claim without any supporting evidence that the Moon landing never happened , because 50 years have passed since scientists and astronauts accomplished and documented the feat .
Sorry , Katharine Hayhoe and DailyKos , making stuff up out of thin air is not more scientifically compelling than long-established scientific knowledge that is well-sourced , well-cited , and confirmed multiple @ @ @ @ @ @ @ studies .
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DailyKos Admits It Favors Unscientific & Subjective Climate Alarm
Written by James Taylor
The Daily Kos published an article claiming climate alarmists are more scientifically credible when they make things up out of thin air – while presenting no supporting scientific facts or evidence – than when climate realists cite numerous datasets and peer-reviewed studies that support their claims.
Making stuff up out of thin air is acceptable, honorable, and compelling, according to DailyKos, if you do it in the name of climate alarmism.
Earlier this week, we published an article here at Climate Realism calling attention to dubious claims made by prominent climate alarmist Katharine Hayhoe in her public presentations to Christian groups.
We specifically noted that Hayhoe uses an apparently self-made chart in her presentations that shows global temperatures steadily and monotonously declining, with almost no variations, for nearly 6,000 years.
Hayhoe’s chart reproduced below, then shows, at the end of the 6,000 years of steady cooling, an enormous temperature spike in the 20th century that completely overwhelms the previous 6,000 years of steady cooling.
Our Climate Realism article then showed what the United Nations Intergovernmental Panel on Climate Change (IPCC) itself presented in its very first climate report – published in 1990 and confirmed by multiple studies since (see, for example, pg. 14 here) – in which scientists, the IPCC, and multiple datasets and peer-reviewed studies agreed that scientific evidence shows temperatures have varied frequently and considerably throughout the past 6,000 years.
The IPCC chart reproduced below, also showed temperatures currently remain cooler than was the case during most of the past 6,000 years.
As we pointed out in the Climate Realism article, Hayhoe provides no sources or supporting data to support her claim that the IPCC, established science, and multiple supporting studies are all wrong.
She merely shows her own chart in public presentations and then uses her highly suspicious chart to support her claim that humans are creating an unprecedented and existential climate crisis.
DailyKos published an article childishly attacking the Climate Realism article. The DailyKos article never cited any scientific evidence, data, or studies to support Hayhoe’s chart.
Instead, the article claimed,
“Not be outdumb’d, Heartland’s James Taylor thought it would be clever to contrast a chart Dr. Hayhoe uses with one from the very first IPCC report, as though a graph from 1990 is somehow more accurate than something produced with the benefit of an additional thirty years of research.”
Of course, the only presented “thirty years of research” is Katharine Hayhoe’s self-serving, self-presented chart for which she provides no documentary evidence.
So, the alarmist argument is that if a climate alarmist simply makes up a chart or an argument out of thin air, that chart or argument is more credible than factually documented, evidentiary-based science that has been well established and that has remained in effect for many, many years.
By that logic:
Climate alarmists can – and likely will – claim without any supporting evidence that the Sun revolves around the Earth because Copernicus’ theory is “old science” demonstrated more than 400 years ago.
Climate alarmists can – and likely will – claim without any supporting evidence that the Earth is flat because it has been 500 years since Magellan circumnavigated the Earth.
Climate alarmists can – and likely will – claim without any supporting evidence that the Moon landing never happened, because 50 years have passed since scientists and astronauts accomplished and documented the feat.
Sorry, Katharine Hayhoe and DailyKos, making stuff up out of thin air is not more scientifically compelling than long-established scientific knowledge that is well-sourced, well-cited, and confirmed multiple times since with additional scientific data and studies.
Read more at Climate Realism
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principiascientificinternational--2020-06-09--Coronavirus UK – while Johnson fiddles, the UK burns

The UK response to the coronavirus is fundamentally wrong .
Why fundamentally ?
Because from the beginning , the situation has been treated homogeneously , whereas the public health risk is entirely heterogenous .
As a result , focus has been on the wrong metric – cases .
The term “ cases ” has changed implication , so it is important to explain how .
Initially the term was used to refer to those infections that were serious enough to merit hospitalisation .
However , as more testing was done , the term includes everyone who tests positive , regardless @ @ @ @ @ @ @ cases and make headline figures of them is misleading and is the reason why the UK and most other governments continue to fail in dealing with the situation .
While the debate rages about what policies are effective about stemming the inevitable spread of the virus , the focus where it matters – on hospitalisation and ultimately death – has been lost .
Boris Johnson ’ s pithy mantra “ Stay at home , protect the NHS , save lives ” , delivered on 23rd March and the Department of Health and Social @ @ @ @ @ @ @ a Channel 4 Dispatches documentary on 3rd June [ i ] , “ our response has ensured that the NHS has capacity for everyone who needs it and that it can provide the best possible care for people who become ill ” , highlight the important objective of the public health policy – to prevent hospitalisations and death .
Hospitalisations and death are significantly more important than the number of cases but only in the detail .
The detail in the data does not support the current policy responses – indiscriminate lockdowns , @ @ @ @ @ @ @ And no-one in government , their advisors or select committees [ ii ] is asking the right questions of the data .
According to NHS statistics [ iii ] as of 26th May , of all the hospital deaths related to COVID-19 in England , more than 95 % had a pre-existing condition such as asthma , chronic kidney disease , chronic neurological disorder , chronic pulmonary disease , dementia , diabetes , rheumatological disorder or ischaemic heart disease .
Taking into consideration the age distribution of the population [ iv ] , @ @ @ @ @ @ @ of the population , those aged over 80 account for 53 % of the deaths , of which 97 % had pre-existing condition .
Accounting for less than 19 % of the population , those aged between 60 and 79 account for more than 38 % of the deaths , of which 95 % had pre-existing condition .
Despite accounting for almost half the population , those aged under 40 account for just 0.8 % of the deaths , and only 0.1 % with no known pre-existing condition .
Representing almost a quarter @ @ @ @ @ @ @ pre-existing condition account for 0.05 % of the deaths and 0.01 % without .
The pattern is clear – the risk of death is very much skewed towards the elderly and those with pre-existing conditions .
Since 63 % of all deaths occurred in hospitals , analysis of the deaths data is also a useful proxy for the risk of burden on the NHS .
Another way to understand the situation is by looking at the infection fatality rate ( IFR ) , i.e .
the proportion of people who have died relative @ @ @ @ @ @ @ is important to examine the heterogenous distribution of this rate to get a proper insight that should govern public policy and allow each individual to act in accordance with their own risk , as they do in every other aspect of their life .
In the ONS Coronavirus ( COVID-19 ) Infection Survey pilot : 28 May 2020 [ v ] , 6.78 % of those individuals providing blood samples tested positive for antibodies to COVID-19 .
Applying this ratio to the population , it is estimated that just under 4 million people @ @ @ @ @ @ @ This produces an IFR of about 0.66 % .
There is evidence to suggest a significant number of people have innate immunity [ vi ] which could possibly mean the overall IFR is half this number .
Assuming an equal distribution of infection , the IFR ranges from 6.8 % for those aged 80 and above with pre-existing conditions to 0.0003 % for those under 19 without .
In other words , that ’ s roughly 1 in every 15 to 1 in every 300,000 .
By now , it should be completely @ @ @ @ @ @ @ , public health policy should be almost entirely directed towards protecting the elderly and those with pre-existing conditions from infection .
It is inconceivable that this can be achieved by basing policy on any hypothetical , epidemiological model that does not explicitly account for the heterogeneity of the risk and targeted strategies towards this section of the population .
Instead , the entire UK policy still appears to be reliant on the suppression of the general reproduction rate of the virus , i.e .
containing the spread and minimising the cases .
What @ @ @ @ @ @ @ high street , schools , parks and beaches , and only re-open them with a plethora of conditions when the majority of the susceptible population are in care homes and hospitals ?
Over 16,000 care home residents in England have succumbed to the virus between 10th April and 29th May [ vii ] , representing well over 50 % of the total .
All targeted measures that would more logically and practically mitigate the risk of the virus to the health service and the population are ignored in favour of indiscriminate measures like @ @ @ @ @ @ @ schools , the wearing of non-surgical masks , and other such measures , which have little scientific evidence to support them and don ’ t correlate with the empirical outcome data , to permit the resumption of business and activity .
This conclusion is shared by others across the world , including for example Scott W. Atlas , MD , a Senior Fellow at Stanford University ’ s Hoover Institution and the former chief of neuroradiology at Stanford University Medical Center [ viii ] who states : “ The overwhelming evidence all over @ @ @ @ @ @ @ defined group — older people and others with underlying conditions — is more likely to have a serious illness requiring hospitalization and more likely to die from COVID-19 .
Knowing that , it is a common-sense , achievable goal to target isolation policy to that group , including strictly monitoring those who interact with them.
” By and large , it is now too late to make the biggest difference in the primary and secondary care facilities where most of the contagion in the susceptible is nosocomial but at least the logically ineffective @ @ @ @ @ @ @ measures should be dispelled .
About the author : Joel Smalley holds an MBA from the University of Toronto and works as a Blockchain architect and early stage , polymath data-driven technologist , specializing in fintech , healthtech and IoT .
He is currently CIO and CTO of Toucan Labs .
PRINCIPIA SCIENTIFIC INTERNATIONAL , legally registered in the UK as a company incorporated for charitable purposes .
Head Office : 27 Old Gloucester Street , London WC1N 3AX .
Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method @

Coronavirus UK – while Johnson fiddles, the UK burns
Written by Joel Smalley MBA
The UK response to the coronavirus is fundamentally wrong. Why fundamentally? Because from the beginning, the situation has been treated homogeneously, whereas the public health risk is entirely heterogenous.
As a result, focus has been on the wrong metric – cases. The term “cases” has changed implication, so it is important to explain how. Initially the term was used to refer to those infections that were serious enough to merit hospitalisation. However, as more testing was done, the term includes everyone who tests positive, regardless of severity.
To continue to monitor cases and make headline figures of them is misleading and is the reason why the UK and most other governments continue to fail in dealing with the situation. While the debate rages about what policies are effective about stemming the inevitable spread of the virus, the focus where it matters – on hospitalisation and ultimately death – has been lost.
Boris Johnson’s pithy mantra “Stay at home, protect the NHS, save lives”, delivered on 23rd March and the Department of Health and Social Care Media Centre’s response to a Channel 4 Dispatches documentary on 3rd June[i], “our response has ensured that the NHS has capacity for everyone who needs it and that it can provide the best possible care for people who become ill”, highlight the important objective of the public health policy – to prevent hospitalisations and death.
Hospitalisations and death are significantly more important than the number of cases but only in the detail. The detail in the data does not support the current policy responses – indiscriminate lockdowns, social distancing, and mask wearing. And no-one in government, their advisors or select committees[ii] is asking the right questions of the data.
According to NHS statistics[iii] as of 26th May, of all the hospital deaths related to COVID-19 in England, more than 95{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} had a pre-existing condition such as asthma, chronic kidney disease, chronic neurological disorder, chronic pulmonary disease, dementia, diabetes, rheumatological disorder or ischaemic heart disease.
Taking into consideration the age distribution of the population[iv], despite accounting for less than 5{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the population, those aged over 80 account for 53{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the deaths, of which 97{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} had pre-existing condition.
Accounting for less than 19{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the population, those aged between 60 and 79 account for more than 38{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the deaths, of which 95{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} had pre-existing condition.
Despite accounting for almost half the population, those aged under 40 account for just 0.8{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the deaths, and only 0.1{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} with no known pre-existing condition.
Representing almost a quarter of the population, under 20s with pre-existing condition account for 0.05{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the deaths and 0.01{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} without.
The pattern is clear – the risk of death is very much skewed towards the elderly and those with pre-existing conditions. Since 63{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of all deaths occurred in hospitals, analysis of the deaths data is also a useful proxy for the risk of burden on the NHS.
Another way to understand the situation is by looking at the infection fatality rate (IFR), i.e. the proportion of people who have died relative to being infected. Again, it is important to examine the heterogenous distribution of this rate to get a proper insight that should govern public policy and allow each individual to act in accordance with their own risk, as they do in every other aspect of their life.
In the ONS Coronavirus (COVID-19) Infection Survey pilot: 28 May 2020[v], 6.78{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of those individuals providing blood samples tested positive for antibodies to COVID-19. Applying this ratio to the population, it is estimated that just under 4 million people in England have contracted the virus. This produces an IFR of about 0.66{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117}. There is evidence to suggest a significant number of people have innate immunity[vi] which could possibly mean the overall IFR is half this number.
Assuming an equal distribution of infection, the IFR ranges from 6.8{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} for those aged 80 and above with pre-existing conditions to 0.0003{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} for those under 19 without. In other words, that’s roughly 1 in every 15 to 1 in every 300,000.
By now, it should be completely apparent that to deal with this coronavirus, public health policy should be almost entirely directed towards protecting the elderly and those with pre-existing conditions from infection. It is inconceivable that this can be achieved by basing policy on any hypothetical, epidemiological model that does not explicitly account for the heterogeneity of the risk and targeted strategies towards this section of the population.
Instead, the entire UK policy still appears to be reliant on the suppression of the general reproduction rate of the virus, i.e. containing the spread and minimising the cases. What sense does it make to close the high street, schools, parks and beaches, and only re-open them with a plethora of conditions when the majority of the susceptible population are in care homes and hospitals? Over 16,000 care home residents in England have succumbed to the virus between 10th April and 29th May[vii], representing well over 50{154653b9ea5f83bbbf00f55de12e21cba2da5b4b158a426ee0e27ae0c1b44117} of the total.
All targeted measures that would more logically and practically mitigate the risk of the virus to the health service and the population are ignored in favour of indiscriminate measures like social distancing, reduced class sizes in schools, the wearing of non-surgical masks, and other such measures, which have little scientific evidence to support them and don’t correlate with the empirical outcome data, to permit the resumption of business and activity.
This conclusion is shared by others across the world, including for example Scott W. Atlas, MD, a Senior Fellow at Stanford University’s Hoover Institution and the former chief of neuroradiology at Stanford University Medical Center[viii] who states:
“The overwhelming evidence all over the world consistently shows that a clearly defined group — older people and others with underlying conditions — is more likely to have a serious illness requiring hospitalization and more likely to die from COVID-19. Knowing that, it is a common-sense, achievable goal to target isolation policy to that group, including strictly monitoring those who interact with them.”
By and large, it is now too late to make the biggest difference in the primary and secondary care facilities where most of the contagion in the susceptible is nosocomial but at least the logically ineffective myriad of other often confusing and contradictory measures should be dispelled.
[i] https://healthmedia.blog.gov.uk/2020/06/03/response-to-channel-4-dispatches-britains-coronavirus-catastrophe-did-the-government-get-it-wrong/
[ii] https://parliamentlive.tv/Event/Index/c36d74b3-2fe2-4309-8554-f50fe966f7a3
[iii] https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-daily-deaths/
[iv]https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/overviewoftheukpopulation/august2019
[v]https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronaviruscovid19infectionsurveypilot/28may2020
[vi] https://www.cell.com/cell/fulltext/S0092-8674(20)30610-3
[vii]https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/numberofdeathsincarehomesnotifiedtothecarequalitycommissionengland
[viii] https://thehill.com/opinion/healthcare/494034-the-data-are-in-stop-the-panic-and-end-the-total-isolation
About the author: Joel Smalley holds an MBA from the University of Toronto and works as a Blockchain architect and early stage, polymath data-driven technologist, specializing in fintech, healthtech and IoT. He is currently CIO and CTO of Toucan Labs.
PRINCIPIA SCIENTIFIC INTERNATIONAL, legally registered in the UK as a company incorporated for charitable purposes. Head Office: 27 Old Gloucester Street, London WC1N 3AX.
Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method.
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principiascientificinternational--2020-06-09--A Possible Aether Explanation for Two Light Phenomena

A Possible Aether Explanation for Two Light Phenomena Independent researcher , Dr Raymond Gallucci , submits his latest paper , ‘ A Possible Aether Explanation for Two Light Phenomena : Varying Speed with Medium and Cosmological Redshift ‘ for reader feedback .
We have pleasure in publishing his summary with a link to the full PDF , below .
A Possible Aether Explanation for Two Light Phenomena : Varying Speed with Medium and Cosmological Redshift It is extremely difficult , if not impossible , to imagine any kind of particle , even an allegedly massless one such as a photon @ @ @ @ @ @ @ 3.00 x 105 km/s .
Nonetheless , that is what mainstream physics would have us accept as gospel , with some wave-like properties thrown-in for good measure .
It is almost as difficult to envision the same speed of a pure wave propagating through some sort of medium , such as an aether for light .
Nonetheless , this would seem the only possible means of such speedy travel , and perhaps alternative concepts such as an aether consisting of “ ropes ” ( Gaede ’ s intertwined electromagnetic “ threads ” ) @ @ @ @ @ @ @ less difficult to imagine .
( Gallucci , “ Does Gaede ’ s ‘ Rope Hypothesis ’ of Light Align with a Standing-Wave Aether ?
” http : //vixra.org , item 1801.0420 ; https : //principia-scientific.org/does-gaedes-rope-hypothesis-of-light-align-with-a-standing-wave-aether/ [ April 23 , 2018 ] ) With this possibility of an aether as light ’ s propagating medium in mind , two phenomena of light are examined from an aether perspective – its speed varying with medium and the cosmological redshift .
When light travels from one medium to another of different density , its speed @ @ @ @ @ @ @ , as shown in Table 1 ( see full article ) .
However , the “ color ” ( frequency ) of light does not change with medium , implying that the speed change arises solely from a change in wavelength , since “ color ” is a function solely of frequency , such that any change in speed must arise solely from a corresponding change in wavelength .
Cosmological redshift refers to the observed , roughly linear decrease in frequency of light emanating from galaxies as a function of distance ( i.e.
@ @ @ @ @ @ @ ” in terms of “ color ” ) , alleged by mainstream physics to result from the ever-expanding nature of space itself .
In three dimensions , this is often likened to raisin bread rising as baked .
In two dimensions , this is likened to a polka-dotted balloon as it is inflated .
“ Tired light ” advocates contend that this “ reddening ” results not from cosmic expansion but due to energy losses by the light itself as it traverses the vast distances of space , often due to interactions with @ @ @ @ @ @ @ space .
( For a comprehensive review of many “ tired light ” theories , see Marmet , “ On the Interpretation of Red-Shifts : A Quantitative Comparison of Red-Shift Mechanisms II , ” Feb. 29 , 2016 , https : //pdfs.semanticscholar.org/58e7/cf3f681962ccef6cb4dd283a6b81c1d0f5a0.pdf . )
Figure 1 ( see full article ) illustrates the concept of an essentially incompressible aether , consisting of a “ string ” of adjacent “ aetherons ” ( spherical , for simplicity ) arrayed linearly to represent a light ray spanning a distance “ d ” in a vacuum @ @ @ @ @ @ @ “ c ” over distance “ d ” “ unhindered ” in a vacuum requires a travel time of t = d/c .
However , if the same light ray now travels through a non-vacuum medium composed of atoms of diameter 2r ( nuclear diameter ) , the light pulse , still traveling at “ c , ” must now cover a greater non-linear distance , d – 2r + ( pi ) r = d + ( pi – 2 ) r , increasing the travel time to t = [ d @ @ @ @ @ @ @ ] /c > t , per atom .
Therefore , the light pulse , now traveling through a non-vacuum medium , is perceived as traveling at a slower speed over the linear distance “ d ” of v = d/t < c = d/t .
Now , in Figure 2 ( see full article ) , this same light pulse is viewed as a series of waves , propagated via the adjacent aetherons .
This illustration shows the pulse through the vacuum being comprised of a total of seven wavelengths , spanning the @ @ @ @ @ @ @ of nuclear diameter “ 2r ” now present in the non-vacuum medium , the light pulse must travel the slightly greater non-linear distance d + ( pi – 2 ) r , with a corresponding addition of approximately one-half wavelength ( when passing “ around ” the nucleus ) to cover the linear distance ” d. ” Over this linear distance , the equivalent wavelength of the light pulse through the non-vacuum medium has decreased by the ratio 7/7.5 , and the light speed , which is the product of frequency and wavelength @ @ @ @ @ @ @ ratio , since the frequency ( “ color ” ) of light does not change when passing from one medium through another .
This decreased wavelength is also consistent with the phenomenon of light refraction when passing to/from a less dense from/to a denser medium .
Cosmological redshift refers to the “ reddening ” of light from other galaxies , roughly as a linear function of distance .
It is not applied to light emanating from within our own galaxy .
This brings into question the many “ tired light ” theories that @ @ @ @ @ @ @ ( i.e.
, a reduction in frequency [ “ color ” ] since light energy is the product of Planck ’ s constant and the frequency ) via interactions with particles in a non-pure vacuum .
Given the higher density ( non- “ pure ” -vacuum nature ) of galactic vs. galactic space , such “ reddening ” would be expected from light emanating within our galaxy as well , not just from other galaxies .
If we dismiss the mainstream explanation of “ expanding space , ” as well as the “ @ @ @ @ @ @ @ through interactions with particles , what might an aether perspective suggest ?
Figure 3 ( see full article ) illustrates a possible difference between galactic and galactic aether that might explain cosmological redshift without either expanding space or tired light .
In an galactic vacuum , the aether is represented as a string of adjacent “ aetherons ” ( 20 shown , no “ attenuation ” ) .
Next consider an “ attenuated ” string of nearly adjacent “ aetherons ” in an galactic vacuum ( 16 shown , with 15 interstitial spaces @ @ @ @ @ @ @ , yielding a total space among them = 15 x delta ) .
Finally , envision an “ attenuated ” string of adjacent “ aetherons , ” now compressible , in an galactic vacuum ( 20 shown as ellipses slightly compressed in direction of motion , with displacement = 15 x delta ) .
In an galactic vacuum ( no aether “ attenuation ” ) , a light pulse is transmitted across the distance “ d ” with essentially no force required and no interstitial distance needing to be traversed ; thus , @ @ @ @ @ @ @ essentially zero .
In an galactic vacuum ( “ attenuated ” aether ) , some force is required for the pulse to cover the interstitial distance , which totals a displacement of ( 15 x delta ) in this illustration , corresponding to some energy lost since energy ( work ) is the product of force and displacement .
Since light energy is the product of Planck ’ s constant and frequency , any decrease in energy translates into a decrease in frequency , thereby yielding a redshift due to the “ attenuated @ @ @ @ @ @ @ in an galactic vs. an galactic vacuum .
Why might the nature of the aether differ between galactic ( adjacent , incompressible aetherons ) and galactic space ( either non-adjacent incompressible aetherons or adjacent but compressible aetherons , either referred to as “ attenuated ” ) ?
One can only speculate .
Perhaps the gravitational or electromagnetic fields present with a galaxy vs. essentially absent ( at least in terms of vanishing magnitude ) between galaxies may play a part .
Envisioning light to be capable of traveling ballistically at 3.00 x 105 @ @ @ @ @ @ @ ( photon ) , even one that is “ massless , ” would seem to stretch all credulity .
And while envisioning this speed to be attainable even by a pure wave phenomenon is almost equally incredible , there is at least some plausibility to this being possible via a propagating medium such as an aether .
Two light phenomena have been examined here from an aether perspective , speed varying with medium and the cosmological redshift , with explanations offered as to how both could be consistent with light propagating as a @ @ @ @ @ @ @ INTERNATIONAL , legally registered in the UK as a company incorporated for charitable purposes .
Head Office : 27 Old Gloucester Street , London WC1N 3AX .
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A Possible Aether Explanation for Two Light Phenomena
Written by Dr. Raymond HV Gallucci, PE
Independent researcher, Dr Raymond Gallucci, submits his latest paper, ‘A Possible Aether Explanation for Two Light Phenomena:Varying Speed with Medium and Cosmological Redshift‘ for reader feedback. We have pleasure in publishing his summary with a link to the full PDF, below.
A Possible Aether Explanation for Two Light Phenomena:Varying Speed with Medium and Cosmological Redshift
Dr. Raymond HV Gallucci, PE
gallucci@localnet.com, r_gallucci@verizon.net
(This is a summary article – for the complete article, including tables and figures, see [via cut and paste into browser] https://vixra.org/pdf/2006.0039v1.pdf.)
It is extremely difficult, if not impossible, to imagine any kind of particle, even an allegedly massless one such as a photon to be capable of ballistically speeding at 3.00 x 105 km/s. Nonetheless, that is what mainstream physics would have us accept as gospel, with some wave-like properties thrown-in for good measure. It is almost as difficult to envision the same speed of a pure wave propagating through some sort of medium, such as an aether for light. Nonetheless, this would seem the only possible means of such speedy travel, and perhaps alternative concepts such as an aether consisting of “ropes” (Gaede’s intertwined electromagnetic “threads”) or standing waves could render the implausibility less difficult to imagine. (Gallucci, “Does Gaede’s ‘Rope Hypothesis’ of Light Align with a Standing-Wave Aether?” http://vixra.org, item 1801.0420; https://principia-scientific.com/does-gaedes-rope-hypothesis-of-light-align-with-a-standing-wave-aether/ [April 23, 2018]) With this possibility of an aether as light’s propagating medium in mind, two phenomena of light are examined from an aether perspective – its speed varying with medium and the cosmological redshift.
Two Light Phenomena
When light travels from one medium to another of different density, its speed changes, decreasing with increased medium density, as shown in Table 1 (see full article). However, the “color” (frequency) of light does not change with medium, implying that the speed change arises solely from a change in wavelength, since “color” is a function solely of frequency, such that any change in speed must arise solely from a corresponding change in wavelength. Cosmological redshift refers to the observed, roughly linear decrease in frequency of light emanating from galaxies as a function of distance (i.e., a shifting toward the “red” in terms of “color”), alleged by mainstream physics to result from the ever-expanding nature of space itself. In three dimensions, this is often likened to raisin bread rising as baked. In two dimensions, this is likened to a polka-dotted balloon as it is inflated. “Tired light” advocates contend that this “reddening” results not from cosmic expansion but due to energy losses by the light itself as it traverses the vast distances of space, often due to interactions with particles in the non-pure vacuum of intergalactic space. (For a comprehensive review of many “tired light” theories, see Marmet, “On the Interpretation of Red-Shifts: A Quantitative Comparison of Red-Shift Mechanisms II,” Feb. 29, 2016, https://pdfs.semanticscholar.org/58e7/cf3f681962ccef6cb4dd283a6b81c1d0f5a0.pdf.)
Light Speed vs. Medium
Figure 1 (see full article) illustrates the concept of an essentially incompressible aether, consisting of a “string” of adjacent “aetherons” (spherical, for simplicity) arrayed linearly to represent a light ray spanning a distance “d” in a vacuum. A light pulse traveling at speed “c” over distance “d” “unhindered” in a vacuum requires a travel time of t 0 = d/c. However, if the same light ray now travels through a non-vacuum medium composed of atoms of diameter 2r (nuclear diameter), the light pulse, still traveling at “c,” must now cover a greater non-linear distance, d – 2r + (pi)r = d + (pi – 2)r, increasing the travel time to t 1 = [d + (pi – 2)r]/c > t 0 , per atom. Therefore, the light pulse, now traveling through a non-vacuum medium, is perceived as traveling at a slower speed over the linear distance “d” of v = d/t 1 < c = d/t 0 .
Now, in Figure 2 (see full article), this same light pulse is viewed as a series of waves, propagated via the adjacent aetherons. This illustration shows the pulse through the vacuum being comprised of a total of seven wavelengths, spanning the distance “d.” With the atom of nuclear diameter “2r” now present in the non-vacuum medium, the light pulse must travel the slightly greater non-linear distance d + (pi – 2)r, with a corresponding addition of approximately one-half wavelength (when passing “around” the nucleus) to cover the linear distance ”d.” Over this linear distance, the equivalent wavelength of the light pulse through the non-vacuum medium has decreased by the ratio 7/7.5, and the light speed, which is the product of frequency and wavelength, has also decreased by the same ratio, since the frequency (“color”) of light does not change when passing from one medium through another. This decreased wavelength is also consistent with the phenomenon of light refraction when passing to/from a less dense from/to a denser medium.
Cosmological Redshift
Cosmological redshift refers to the “reddening” of light from other galaxies, roughly as a linear function of distance. It is not applied to light emanating from within our own galaxy. This brings into question the many “tired light” theories that explain the reddening as light energy lost (i.e., a reduction in frequency [“color”] since light energy is the product of Planck’s constant and the frequency) via interactions with particles in a non-pure vacuum. Given the higher density (non-“pure”-vacuum nature) of intra galactic vs. inter galactic space, such “reddening” would be expected from light emanating within our galaxy as well, not just from other galaxies. If we dismiss the mainstream explanation of “expanding space,” as well as the “tired light” theories requiring energy loss through interactions with particles, what might an aether perspective suggest?
Figure 3 (see full article) illustrates a possible difference between intra galactic and inter galactic aether that might explain cosmological redshift without either expanding space or tired light. In an intra galactic vacuum, the aether is represented as a string of adjacent “aetherons” (20 shown, no “attenuation”). Next consider an “attenuated” string of nearly adjacent “aetherons” in an inter galactic vacuum (16 shown, with 15 interstitial spaces of distance “delta” between each, yielding a total space among them = 15 x delta). Finally, envision an “attenuated” string of adjacent “aetherons,” now compressible, in an inter galactic vacuum (20 shown as ellipses slightly compressed in direction of motion, with displacement = 15 x delta).
In an intra galactic vacuum (no aether “attenuation”), a light pulse is transmitted across the distance “d” with essentially no force required and no interstitial distance needing to be traversed; thus, the effective energy “lost” is essentially zero. In an inter galactic vacuum (“attenuated” aether), some force is required for the pulse to cover the interstitial distance, which totals a displacement of (15 x delta) in this illustration, corresponding to some energy lost since energy (work) is the product of force and displacement. Since light energy is the product of Planck’s constant and frequency, any decrease in energy translates into a decrease in frequency, thereby yielding a redshift due to the “attenuated” nature of the “aether” in an inter galactic vs. an intra galactic vacuum.
Why might the nature of the aether differ between intra galactic (adjacent, incompressible aetherons) and inter galactic space (either non-adjacent incompressible aetherons or adjacent but compressible aetherons, either referred to as “attenuated”)? One can only speculate. Perhaps the gravitational or electromagnetic fields present with a galaxy vs. essentially absent (at least in terms of vanishing magnitude) between galaxies may play a part.
Summary
Envisioning light to be capable of traveling ballistically at 3.00 x 105 km/s in the form of a particle (photon), even one that is “massless,” would seem to stretch all credulity. And while envisioning this speed to be attainable even by a pure wave phenomenon is almost equally incredible, there is at least some plausibility to this being possible via a propagating medium such as an aether. Two light phenomena have been examined here from an aether perspective, speed varying with medium and the cosmological redshift, with explanations offered as to how both could be consistent with light propagating as a wave through an aether.
Read/download the full paper at: https://vixra.org/pdf/2006.0039v1.pdf
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Please DONATE TODAY To Help Our Non-Profit Mission To Defend The Scientific Method.
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