Trust Real World Data, Not Computer Models

by | Apr 14, 2020 |

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The entire economy of the western world is being risked on the basis of the output from theoretical mathematical models of forecast expansion of the latest Chinese coronavirus. To help us understand if this makes sense, let’s consider two important cases where theoretical models were proven very wrong.
On the morning of August 6, 1945, the Japanese city of Hiroshima exploded into the first and largest high-level radiation test laboratory in the world. Three days later, Nagasaki became the second. Most victims died from the intense heat or blast effect. Hundreds were to succumb later to effects of radiation as thousands of survivors were hit instantaneously with trillions of neutrons and gamma rays we know as nuclear radiation.
In the early 1950s studies of the effects of radiation were needed by the U.S. military and civilian defense authorities because of the threat of nuclear war with the Soviet Union. A joint U.S.-Japan program was initiated to analyze radiation effects on the Japanese population. Doses to survivors were estimated by their locations at the time of the blasts, with a health handbook being kept by each exposed citizen in Japan. Their medical histories were meticulously recorded. 
Of great importance was the potential mutagenic effects, permanent changes in a victim’s genes. People feared “nuclear monsters.” While it was well known that the genes of some insects were altered by radiation, such never occurred in humans. In fact, not only were the offspring of survivors not negatively affected, but there were benefits that we now attribute to the “hormetic” effect of radiation. Hormesis is the concept that, while a lot of something may be bad for you, a little of that same thing may very well be good for you.
The primary concern was cancer however, and indeed excess cancers in the population, years later, were attributable to high levels of radiation among the Japanese citizens not affected by the blast but close enough to receive a high dose of radiation. 
However, due to the now disproven belief that a single unit of nuclear radiation could cause cancer, called the Linear-No-Threshold, or LNT, theory, a much greater number of cancers were expected to appear among the population whose exposure was minimal. Typically, this amount was equivalent to a lifetime of background radiation exposure experienced in but a few seconds. Researchers expected that excess cases of leukemia would be seen in 3 to 10 years and other cancers in 20 or even 30 years.
After 40 years of intense study and data collection from this large Japanese population exposed to minimal radiation, those things did not happen. In fact, as reported in the 1993 book, “Health Effects of Low-Level Radiation,” by Dr. Sohei Kondo of the Atomic Energy Research Institute, Kinki University, Japan, these bomb survivors were outliving their unexposed peers by significant rates.
Yet, even with countless additional studies of the benefits of low-level radiation in medicine, the LNT theory has not yet been placed in the dust bin of history.
What we learned about radiation, we learned from the analysis of data collected in the real world, not in unsubstantiated theoretical models. Models do have their uses, of course. We build aircraft based on mathematical models entered into computer simulations of as many as 3,000 parts that must work together seamlessly. We run thousands of Computer Aided Design (CAD) simulations that result in Computer Aided Manufacturing (CAM) to keep our airplanes aloft or we would not get on them with a chance of failure as small as one percent. 
Still many people are misguided to put their faith in mathematic models that mislead us into believing they simulate reality. In virtually all of such cases, the mathematical equations are filled with dozens of ‘SWAGs’, or ‘Scientific Wild Ass Guesses.’ 
The absurd predictions from climate models are an obvious second case in point. In 30 years and the expenditure of billions of dollars of government funding, with one exception, not a single climate model prediction of the Earth’s temperature has been close to being correct. The exception was one Russian model which was fully ‘tuned’ and accidentally matched observational data. 
One of the reasons the models work so poorly is that we do not have a theory of climate which would give us truly meaningful equations to program into computer models. And it is not likely we will have such a theory any time soon. University of Western Ontario applied mathematician Dr. Chris Essex, an expert in the mathematical models that underlie climate change concerns, explains, “Climate is one of the most challenging open problems in modern science. Some knowledgeable scientists believe that the climate problem can never be solved.”
In his February 2, 2016 testimony before the U.S. House of Representatives Committee on Science, Space & Technology, Dr. John Christy of The University of Alabama in Huntsville compared the results of atmospheric temperatures as depicted by the average of 102 climate models with observations from satellites and balloon measurements. Christy concluded, “These models failed at the simple test of telling us ‘what’ has already happened, and thus would not be in a position to give us a confident answer to ‘what’ may happen in the future and ‘why.’ As such, they would be of highly questionable value in determining policy that should depend on a very confident understanding of how the climate system works.” 

Some readers might think no harm no foul, but there has been immense harm in the climate arena by the waste of vast sums of money all over the world, well over $1 billion a day, that could have benefited the poor and public health.

Now our economy is being risked on the basis of the output of mathematical models of the expansion of the latest Chinese coronavirus. When the models say each case could lead to a certain range of new cases, sensationalist media only broadcast the worst-case scenarios for infections and the worst-case of potential mortality, leading to huge and probably unnecessary costs to our nations. Nancy Thorner and Ed Ingold write in the Illinois Review (“IF YOU’RE QUIET, YOU CAN HEAR THE GOAL POSTS MOVING”, April 13):

Much of the problem with models is there are not the facts to support the conclusions. In lieu of data, we must rely on assumptions and guesswork. The models tell us that the number of persons infected by a single source increases exponentially, therefore they are the best candidate for control, but only in a qualitative manner. Unknown is what that rate is, nor the timeline it takes. Based on assumptions, the number of persons infected by a single case range from 2.5 to 5.7 over a time from 3 days to two weeks. Nor do we know how many are infectious but asymptomatic.

Also unknown is how many individuals are cured and immune (not just those discharged from ICU). This information derives from tests for antibodies, which are yet to be administered in any quantity.

Governments must urgently address these issues if we are to rescue our economy in time to prevent disastrous long-term consequences. 
As optimists, we are confident that we will eventually win the war against the virus through effective treatments and eventual vaccines, and the economy will recover in a few years, and likely be stronger still. Nobel Laureate in Economics Vernon Smith wrote in the April 6th edition of the Wall Street Journal that he expects that we will see the survival of the fittest businesses and the expansion of new ideas the quarantine has produced and that the travel and hotel industry, which is not in a long-term decline, will bounce back quickly. And it has been revealed that we have the very best folks working in the trenches of medicine to care for the infected throughout our nation, putting their lives at risk as we have done in all past wars. We will survive and be better for it.
But let’s learn from the scare and hope that, in the next pandemic-type health emergency, we will better recognize media-driven political opportunism propelled by poorly formulated mathematical models. 
Note: The author thanks Ed Hiserodt for his assistance in reporting on the radiation impacts of the atomic bombs of World War II in his book UNDEREXPOSED. 

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Dr. Jay Lehr is a Senior Policy Analyst with the International Climate Science Coalition and former Science Director of The Heartland Institute. He is an internationally renowned scientist, author, and speaker who has testified before Congress on dozens of occasions on environmental issues and consulted with nearly every agency of the national government and many foreign countries. After graduating from Princeton University at the age of 20 with a degree in Geological Engineering, he received the nation’s first Ph.D. in Groundwater Hydrology from the University of Arizona. He later became executive director of the National Association of Groundwater Scientists and Engineers.

Tom Harris is Executive Director of the Ottawa, Canada-based International Climate Science Coalition, and a policy advisor to The Heartland Institute. He has 40 years experience as a mechanical engineer/project manager, science and technology communications professional, technical trainer, and S&T advisor to a former Opposition Senior Environment Critic in Canada’s Parliament.

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Dave James
Dave James
2 years ago

President Donald Trump has dropped his “China virus” dog-whistle language after the nation’s leading civil rights and racial justice organizations issued a urgent call to action against racism and discrimination targeting Asian Americans related to COVID-19. But Dr. Jay Lehr and Mr. Tom Harris continue to refer to global COVID-19 pandemic as the “Chinese coronavirus.”
Mr. Tom Harris and Dr. Jay Lehr assert, “Still many people are misguided to put their faith in mathematic models that mislead us into believing they simulate reality. In virtually all of such cases, the mathematical equations are filled with dozens of ‘SWAGs’, or ‘Scientific Wild Ass Guesses.’ The absurd predictions from climate models are an obvious second case in point.” However their assertion is not unsupported by the scientific evidence.
“..(C)limate models published over the past five decades were generally quite accurate in predicting global warming in the years after publication, particularly when accounting for differences between modeled and actual changes in atmospheric CO2 and other climate drivers.” (Source “Evaluating the Performance of Past Climate Model Projections” Zeke Hausfather Henri F. Drake Tristan Abbott Gavin A. Schmidt, Dec 4, 2019, Geophysical Research Letters)
Rather than blame the current global economic slow down on COVID-19, Dr. Lehr and Mr. Harris assert “Now our economy is being risked on the basis of the output of mathematical models of the expansion of the latest Chinese coronavirus.” However models of the spread of COVID-19 have enabled us to “flattening the curve” saving thousands of lives with different non-pharmaceutical interventions, such as social distancing and quarantine. (Source “Minnesota officials unveil modeling behind state’s COVID-19 strategy” By Jeremy Olson and Christopher Snowbeck, Apr 11, 2020, Star Tribune)
Dr. Lehr’s and Mr. Harris’ source Dr. Chris Essex is most famous for his 2007 claim, “There is no such thing as global temperature. And if there is no global temperature, how can there be global warming?” (Source ”There is no global ‘temperature’; Why are so many people obsessed with a single number?,” by Chris Essex, Jun 23, 2007, National Post) An argument recently echoed last month by Mr. Harris and Dr. Lehr “…it is difficult or meaningless to ascribe a single temperature to the globe…” (Source “No Meaningful Consensus Among Climate Scientists” By Dr. Jay Lehr & Tom Harris, Mar 24, 2020, America Out Loud)
Dr. Essex, Dr. Lehr’s and Mr. Harris’ assertions are not supported by the evidence. “The three most highly cited combined land temperature and SST data sets are NOAA’s MLOST, NASA’s GISTEMP, and the UK’s HadCRUT. A new merged land-ocean temperature data set is available from the Berkeley Earth group.” (Source “GLOBAL TEMPERATURE DATA SETS: OVERVIEW & COMPARISON TABLE” NCAR UCAR Climate Date Guide)
Mr. Harris’ and Dr. Lehr’s source Dr. John Christy is a climate science who work also relies on models. Dr. Christy’s satellite measurements are based on his computer models. Dr. Christy has repeated revised his model due to errors each time showing more warming. (Source “More errors identified in contrarian climate scientists’ temperature estimates” John Abraham, May 11, 2017, The Guardian) Relying on Christy data set and ignoring the other global data sets is science and not trusting the real world data.
Dr. Lehr’s and Mr. Harris’ assertions that low level radiation is good for you and that LNT model has been “dis-proven” is not shared by many of those who study and research low level radiation.
In 2004 the United States National Research Council (part of the National Academy of Sciences) supported the linear no threshold model and stated regarding Radiation hormesis: “The assumption that any stimulatory hormetic effects from low doses of ionizing radiation will have a significant health benefit to humans that exceeds potential detrimental effects from the radiation exposure is unwarranted at this time.”
In 2005 the United States National Academies’ National Research Council published its comprehensive meta-analysis of low-dose radiation research BEIR VII, Phase 2. In its press release the Academies stated: “The scientific research base shows that there is no threshold of exposure below which low levels of ionizing radiation can be demonstrated to be harmless or beneficial.”
The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) wrote in its 2000 report: “Until the […] uncertainties on low-dose response are resolved, the Committee believes that an increase in the risk of tumour induction proportionate to the radiation dose is consistent with developing knowledge and that it remains, accordingly, the most scientifically defensible approximation of low-dose response. However, a strictly linear dose response should not be expected in all circumstances.”
The United States Environmental Protection Agency also endorses the LNT model in its 2011 report on radiogenic cancer risk: “Underlying the risk models is a large body of epidemiological and radiobiological data. In general, results from both lines of research are consistent with a linear, no-threshold dose (LNT) response model in which the risk of inducing a cancer in an irradiated tissue by low doses of radiation is proportional to the dose to that tissue.” https://en.wikipedia.org/wiki/Linear_no-threshold_model

Dave James
Dave James
2 years ago

I made a mistake. My post should read, “Relying on Christy’s data set and ignoring the other global data sets is NOT science nor trusting the real world data.”

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