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Although it’s impossible to say for sure, Trofim Lysenko probably killed more human beings than any individual scientist in history. Other dubious scientific achievements have cut thousands upon thousands of lives short: dynamite, poison gas, atomic bombs. But Lysenko, a Soviet biologist, condemned perhaps millions of people to starvation through bogus agricultural research—and did so without hesitation. Only guns and gunpowder, the collective product of many researchers over several centuries, can match such carnage.
Lysenko forced farmers to plant seeds very close together since, according to his “law of the life of species”, plants from the same “class” never compete with one another. Lysenko played an active role in the famines that killed millions of Soviet people and his practices prolonged and exacerbated the food shortages. The People’s Republic of China under Mao Tse-Tung adopted his methods starting in 1958, with calamitous results, culminating in the Great Chinese Famine of 1959 to 1962. At least 30 million died of starvation.
On the one hand, this rehabilitation is shocking. Genetics almost certainly won’t be banned in Russia again, and the rehabilitation effort remains a fringe movement overall. But fringe ideas can have dangerous consequences. This one distorts Russian history and glosses over the incredible damage Lysenko did in abusing his power to silence and kill colleagues—to say nothing of all the innocent people who starved because of his doctrines. The fact that even some “qualified scientists” are lionizing Lysenko shows just how pervasive anti-Western sentiment is in some circles: Even science is perverted to promote ideology.
Again and again, we hear the question “Don’t you believe in science?” This is an odd question because pure belief isn’t necessary for science. One must understand the process of confirming empirical results against a hypothesis. If the empirical results (the facts) don’t confirm the hypothesis (the narrative) then there is no science whether you want to claim to believe in science or not. Of course, one believes in Religion because it will by definition go beyond any empirical result. In the 20th century, we have something called ideology. This has the unfortunate quality of looking or sounding like science but it isn’t. One believes in the narrative but not the facts. There is no science in ideology. This can be a very dangerous phenomenon. Lysenko had a powerful understanding of Stalinist Communist Ideology. His theories of crops adapting to the environment were completely false scientifically. Yet, because these theories so pleased the Soviet Idologues he was put in charge of Soviet Agriculture with disastrous results. Not only did it cause huge numbers of deaths in the Soviet Union but his ideological idea was also persuasive in China and caused even more deaths there.
From 1970, the first Earth Day, until about 2003, there were a variety of sophisticated hypotheses about Man-Made Global Warming. Unfortunately, the accuracy of the empirical data available was so weak that it wasn’t possible to confirm or deny these hypotheses. Yet, the ideology of Made-Made Global Warming insisted on belief without any actual scientific confirmation. Massively draconian legislation was drafted and put into effect on these unconfirmed hypotheses. The economy of the United States and the rest of the World was severely restricted by this legislation.
In 2003, after multiple satellites with well-proven look down infrared temperature measuring technology had been in orbit for a few years, accurate measurement of the surface temperature of the earth was finally available. What was announced was that there was a pause in Global Warming. As the models, now long in use, had relied on the surface temperature of the earth in their measure of Global Temperature did not match the hard reliable empirical data now coming from the satellites, these hypotheses would be rejected as scientifically invalid. In short, to the extent we understood the science at the time, Man-Made Global Warming did not exist. Certainly, all of the draconian legislation resulting in the massive restriction of the economy in retrospect looked completely unwarranted.
Of course, as they say, the story wasn’t over. Dealing with an ideology is not like dealing with normal scientific inquiry. The ideologues will cling to their hypothesis, claiming and acting, as if their naked hypothesis, without confirming data, had been science. Now, these idealogues realized that their game was up. The new data threatened the existence of their ideology. So they tried first to undermine the new data. From NOAA, an organization never designed to be collecting such data, new data was referred to as showing that Man-Made Global Warming was still happening. Of course, the scientific community asked to see the actual data set as would be customary. NOAA refused to release the actual data set. A freedom of information lawsuit finally forced them to release the data set. It was soon discovered that as one might have assumed, NOAA had no new data but had repackaged the old data available before the accurate satellite data had become available. With this scheme exposed now the ideologues’ ideology was in real jeopardy. They were forced to try to do real science. First, as would be their right, they constructed new hypotheses that involved the temperature of the deep ocean. However, immediately, they moved to their non-scientific, ideological, interpretation. There was no accurate data on the temperature of the deep ocean to confirm their hypotheses (same old problem). The ideologues went ahead and claimed that Man-Made Global Warming did exist even though, once again, they had not proved this scientifically. If the Biden administration, irrespective of massive proof of massive fraud, is allowed to take office it is likely that this ideological unconfirmed hypothesis will once again be accepted as scientific proof when it is not. Then a new round of unjustified draconian legislative measures will be applied and the world economy will be crushed needlessly.
We have been hearing the term “Excess Deaths” in the media over and over. It sounds simple enough but is it? Look up the CDC definition and it is anything but simple.
Counts of deaths in the most recent weeks were compared with historical trends (from 2013 to present) to determine whether the number of deaths in recent weeks was significantly higher than expected, using Farrington surveillance algorithms (1). The ‘surveillance’ package in R (2) was used to implement the Farrington algorithms, which use overdispersed Poisson generalized linear models with spline terms to model trends in counts, accounting for seasonality. For each jurisdiction, a model is used to generate a set of expected counts, and an upper bound threshold based on a one-sided 95% prediction interval of these expected counts is used to determine whether a significant increase in deaths has occurred. Estimates of excess deaths are provided based on the observed number of deaths relative to two different thresholds. The lower end of the excess death estimate range is generated by comparing the observed counts to the upper bound threshold, and a higher end of the excess death estimate range is generated by comparing the observed count to the average expected number of deaths. Reported counts were weighted to account for potential underreporting in the most recent weeks.
Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19. Additionally, death certificates are often initially submitted without a cause of death, and then updated when cause of death information becomes available. It may be the case that some excess deaths that are not attributed directly to COVID-19 will be updated in coming weeks with cause-of-death information that includes COVID-19. These analyses will be updated periodically, and the numbers presented will change as more data are received.
Estimated numbers of deaths due to these other causes of death could represent misclassified COVID-19 deaths, or potentially could be indirectly related to COVID-19 (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems). Deaths with an underlying cause of death of COVID-19 are not included in these estimates of deaths due to other causes, but deaths where COVID-19 appeared on the death certificate as a multiple cause of death may be included in the cause-specific estimates. For example, in some cases, COVID-19 may have contributed to the death, but the underlying cause of death was another cause, such as terminal cancer. For the majority of deaths where COVID-19 is reported on the death certificate (approximately 95%), COVID-19 is selected as the underlying cause of death.
These estimates are based on provisional data, which are incomplete. The weighting method applied may not fully account for reporting lags if there are longer delays at present than in past years. For example, in Pennsylvania, reporting lags are currently much longer than they have been in past years, and death counts for 2020 are therefore underestimated. Conversely, the weighting method may over-adjust for underreporting, given improvements in data timeliness in certain jurisdictions. Unweighted estimates are provided, so that users can see the impact of weighting the provisional counts. However, these unweighted provisional counts are incomplete, and the extent to which they may underestimate the true count of deaths is unknown. Some jurisdictions exhibit recent increases in deaths when using weighted estimates, but not the unweighted.
We have learned from the very first sentence that I have quoted above that excess deaths is an estimate completely dependent on the number of deaths that historically occurred from 2013 to the present. The validity of this depends first on the statistical pure model that is employed. There are three questions. First, does the data actually conform to the model and thus verify the hypothesis? Second, is the model itself a valid model of reality or is there some inherent flaw in its logic? Third, was the data gathered properly and reliably? For this discussion, I am most concerned about the third question but does not imply that the first two can be dismissed. For me, an old instrument salesman, the really glaring problem is in the empirical measurement itself. COVID doesn’t seem to kill anybody all by itself. It kills very elderly people with compromised lungs and multiple comorbidities. Thus, determining the actual cause of death would be a very murky process. Add to this that there is a financial incentive for the hospitals to name the cause of death as COVID one really must look very carefully at the results. There is going to be a reevaluation of all of this at some point and I expect cooler heads may have a very different interpretation. From above:
Additionally, death certificates are often initially submitted without a cause of death, and then updated when cause of death information becomes available. It may be the case that some excess deaths that are not attributed directly to COVID-19 will be updated in coming weeks with cause-of-death information that includes COVID-19. These analyses will be updated periodically, and the numbers presented will change as more data are received.
The CDC comments as if the fact that weeks after a death was recorded as “not attributed directly to COVID” it is then changed to a COVID death, means that COVID is being underreported so keep watching as the numbers go up. This completely fails to accept the possibility that the hospital administrator may have visited the physician and mentioned offhand the amount of money the hospital would receive if the death in question would be attributed to COVID. He then asks the physician to “reconsider” his finding. This would mean that the COVID death numbers we will end up with will be very high, not too low, and will be adjusted down. This also points out just how difficult it is to judge the cause of death because COVID isn’t capable of killing all by itself and will always be part of a team effort with other causes. Again, to me, this suggests a weak virus and not a strong one. By the way, it should only take one visit for the administrator to make it clear that the physician should get with the program and bend his judgment so as to record the death as COVID.
I haven’t got to all of the “fudge factors” that are being baked into these “Excess Death” calculations. We know that such fudged models are prone to confirmation bias. The famous “hockey stick” curve that tried to prove Man-Made Global Warming was a result of multiple fudge factors that were adjusted and even then the data was cherry-picked to produce the hockey stick.
Hysterical headlines that scream about deaths caused by COVID, which can only mean the estimated Excess Deaths, should be classified as some sort of ideological belief system and not science. Even more hysterical headlines about new outbreaks of COVID cases, which mean that many more people have been tested and show anti-bodies, is used to imply that more lockdown is justified. This too is not science but an ideological belief system at work.
We have seen this before. Lysenko is on the loose again. There are two Krakens that have been recently released to harass the American public. First, a massively fraudulent election, and second, a massively overhyped pandemic. Perhaps not so oddly enough, the same people have perpetrated both.