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<\/figure>\nBy Michael J. Talmo, April 19, 2024 This is a follow-up to my previous\u00a0article,<\/a>\u00a0The Virology Racket.\u00a0<\/em>The purpose is to explain in lay language exactly what modern virologists are doing when claiming they have discovered new pathogenic viruses. The methods they now use are part of a rapidly evolving field of scientific research called virus genome sequencing.<\/p>\n All living things have a\u00a0genome,<\/a>\u00a0which is an organism\u2019s genetic blueprint. It\u2019s what makes us humans what we are and everything else what it is. Therefore, whole genome sequencing (WGS) isn\u2019t limited to virology. As explained\u00a0here<\/a>\u00a0and\u00a0here,<\/a>\u00a0this technology is also used for disease prevention, as in seeing if you have genetic mutations that can impact the health of your future children; predicting what diseases a person may acquire in old age; preventing and treating cancer more effectively; determining in advance the side effects of medications on a particular person, etc. But the focus of this article is the genome sequencing of viruses. So, let\u2019s understand what it means.<\/p>\n As explained\u00a0here,\u00a0<\/a>viral genomes, which can either be DNA or RNA, are very small compared to the genomes of larger, more complex organisms. This makes sequencing them a faster and simpler task. For example, our human genome is composed of 3 billion DNA nucleotides, or letters. But the RNA genome of a coronavirus is supposedly only about 30,000 nucleotides; the RNA genome of an influenza\/flu virus is supposed to be 13,500 nucleotides; and the DNA genome of the redondovirus is supposed to be a mere\u00a03,000 nucleotides.<\/a><\/p>\n As explained in this 2017\u00a0article<\/a>\u00a0in the peer review journal\u00a0Nature,<\/em>\u00a0three major methods are used for viral genome sequencing: \u201cmetagenomic sequencing, PCR amplicon sequencing, and target enrichment sequencing.\u201d<\/p>\n Regardless of the methods used, the CDC (Centers for Disease Control and Prevention)\u00a0explained<\/a>\u00a0that what virologists are doing is using computers to sequence RNA and DNA found in cell cultures into what they claim is a whole viral genome. They then further claim that it proves a particular virus exists or that a new virus has been discovered. But this is done without isolating actual virus particles, which, as\u00a0reported\u00a0<\/a>by the CDC, have always \u201cbeen critical in the detection of previously unrecognized viruses\u201d and warned that these \u201cclassical techniques\u201d should not be abandoned. Instead, modern virologists will access\u00a0virus databases<\/a>\u00a0that contain millions of artificially sequenced viral genomes and\u00a0compare them.<\/a>\u00a0If they don\u2019t exactly match, they just claim it\u2019s a new variant.<\/p>\n To quote this 2019\u00a0report<\/a>\u00a0from Harvard University:<\/p>\n \u201cComputational biologists use pattern-matching algorithms, mathematical models, image processing, and other techniques to summarize and derive meaning from the sequencing data.\u201d<\/p>\n The University of Washington\u2019s Paul G. Allen School of Computer Science and Engineering further states in this\u00a0article:<\/a><\/p>\n \u201cComputers are needed to process, analyze, and store billions of DNA bases that can be sequenced from a single DNA sample. Even the sequencing machines themselves run on computers\u2026After DNA is sequenced, it is usually processed and analyzed by a number of computer programs through what is called the DNA data processing pipeline.\u201d<\/p>\n As a result, virologists assert that \u201cNew sequencing techniques have helped us uncover a world of new viruses,\u201d as\u00a0reported<\/a>\u00a0by\u00a0Penn Medicine News.<\/em><\/p>\n The article goes on to ask the crucial question:<\/p>\n \u201cOverall, we are asking whether we can take unknown DNA sequences and make sense of it by identifying new viruses from the whole universe of sequences in the human\u00a0virome.\u201d<\/a><\/p>\n What all this means is that virus genome sequencing, along with other terms used to describe it, are just fancy words for non-specific molecular markers. It\u2019s like claiming footprints of Bigfoot and that sonar blips of what appears to be a large animal known as the Monster in the waters of Loch Ness prove these creatures exist and that you have actually found them. Obviously, you haven\u2019t. You need the actual creatures. You need a body. In essence, virologists are claiming that you don\u2019t need a body. Instead, they are claiming that fragments of genetic material artificially assembled into a complete genome prove the existence and\/or presence of viral pathogens. And since these viral genomes are sequenced by computers, virus genome sequencing is really computer modeling.<\/p>\n The problem with computer models<\/strong><\/p>\n Computer modeling,<\/a>\u00a0as explained\u00a0here,<\/a>\u00a0is not scientific evidence. Nevertheless, it has its place in scientific research. I have no problem with using computer models for predicting weather patterns like<\/p>\n hurricanes, evaluating the performance of aircraft, fleshing out dinosaurs, and a plethora of other<\/p>\n things. But I have a big problem with using it as a\u00a0substitute<\/a>\u00a0for medical research or to guide public health policies, for which it has failed miserably.<\/p>\n A 2014\u00a0study<\/a>\u00a0in\u00a0Issues in Science and Technology,<\/a>published by the National Academy of Sciences and Arizona State University, emphatically states:<\/p>\n \u201cEvidence of poor modeling practice and of negative consequences for society abounds.\u201d<\/p>\n This 2022\u00a0study,<\/a>\u00a0published in the\u00a0International Journal of Forecasting,<\/em>\u00a0further states:<\/p>\n \u201cFailure in epidemic forecasting is an old problem. In fact, it is surprising that epidemic forecasting has retained much credibility among decision-makers, given its dubious track record\u2026implausible, exaggerated forecasts should be abandoned. Otherwise, they may cause more harm than the virus itself\u2026Despite these obvious failures, epidemic forecasting continued to thrive, perhaps because vastly erroneous predictions typically lacked serious consequences.\u201d<\/p>\n When it came to COVID-19, there should have been very serious consequences because, as\u00a0reported\u00a0<\/a>in the journal\u00a0Science,<\/em>\u00a0as early as March 2020, \u201cEntire cities and countries have been locked down based on hastily done forecasts that often haven\u2019t been peer reviewed\u2026Long lockdowns to slow a disease have catastrophic economic impacts and may devastate public health themselves.\u201d<\/p>\n As\u00a0reported<\/a>\u00a0in\u00a0National Review,\u00a0<\/em>\u201cthe most influential\u201d and \u201cone of the most wrong,\u201d not to mention \u201cwildly inaccurate,\u201d computer models was created by epidemiologist\u00a0Neil Ferguson<\/a>\u00a0of London England\u2019s Imperial College. Ferguson\u2019s COVID-19 model, which, by his own admission, \u201cwas based on undocumented, 13-year-old computer code that was intended to be used for a feared influenza pandemic, rather than a coronavirus.\u201d<\/p>\n Ferguson\u2019s COVID-19 computer model predicted, in the worst-case scenario, that without an immediate lockdown, over 500,000 deaths would occur in the UK and over two million deaths in the USA, which was totally wrong. As of April 2024,\u00a0Worldometer<\/em>\u00a0reported<\/a>\u00a0only 232,112 deaths in the UK and 1,219,487 deaths in the USA. It doesn\u2019t matter if Ferguson\u00a0denied<\/a>\u00a0calling for lockdowns; both countries locked down because of his incorrect computer model. Of course, lockdown apologists will argue that it was because of the restrictions or NPIs (non-pharmaceutical interventions) that the worst-case scenarios didn\u2019t occur. This\u00a0peer-reviewed<\/a>\u00a02021\u00a0study<\/a>\u00a0and this 2023\u00a0article<\/a>\u00a0in\u00a0New York Magazine\u00a0<\/em>emphatically document that the lockdowns were a failure and had little or no impact on case numbers and deaths.<\/p>\n It\u2019s important to understand that computer models can be used for things that are real and for things that aren\u2019t real. For example, experts in this field could construct a model using CGIs (computer-generated images) for how long it would take Godzilla to melt Manhattan with his atomic breath if he came ashore in New York City. Or a model could be done predicting who would win if Batman fought Captain America. None of these characters exist. But the computer doesn\u2019t know or care about that.<\/p>\n Take fight sims as another example, which you can find all over YouTube. This is where they match up boxers from different eras and predict who would have won. Muhammad Ali (1942-2016) and Mike Tyson are used the most because they are two of the best-known boxers. But because Ali died, Tyson is used the most. They even had a CGI fight sim where he took on Rocky Balboa. The computer predicted that Rocky would win. Iron Mike is a real person. But Rocky Balboa is a fictitious character created and brilliantly played by actor Sylvester Stallone. But this is irrelevant to the computer.<\/p>\n Simply stated, computers are stupid. Even though they can process data faster than us, they will accept the assumptions and biases of the people who program them. So, if programmers assume that a particular pathogenic virus is real, the computer will follow that incorrect reality.<\/p>\n Genome sequencing is no different<\/strong><\/p>\n As stated in this 2017\u00a0study<\/a>\u00a0published in the journal\u00a0Nature,\u00a0<\/em>\u201cSequencing viral nucleic acids, whether<\/p>\n from cultures or directly from clinical specimens is complicated by the presence of contaminating host DNA.\u201d (including contamination from other organisms, as explained\u00a0here<\/a>) \u201cBy contrast, most bacterial sequencing is currently carried out on clinical isolates that are cultured; thus, sample preparation is comparatively straightforward\u2026 It is also important to remember that the detection of viral nucleic acid does not necessarily identify the cause of illness.\u201d<\/p>\n In other words, as stated in my previous article and on page 702 of this 1993\u00a0study<\/a>\u00a0published in\u00a0Nature\/<\/em>B<\/em>iotechnology,\u00a0<\/em>a virus must be\u00a0isolated<\/a>\u00a0or\u00a0purified<\/a>\u00a0to establish its existence. Meaning, the viral particles, which have a genome that can be DNA or RNA encased in a protein coating called a\u00a0capsid<\/a>\u00a0and sometimes an additional outer envelope called a\u00a0lipid bilayer,<\/a>\u00a0must be separated from everything else that\u2019s in a cell culture. The viral isolates or\u00a0pure culture<\/a>\u00a0must then be photographed under an electron microscope. Its proteins and genome can then be identified or characterized. Once its existence has been established, it can then be determined if the virus causes a particular disease by applying either\u00a0Koch\u2019s postulates<\/a>\u00a0or their modification, known as\u00a0River\u2019s postulates.<\/a><\/p>\n Isolation of actual viral particles is essential because this 2022\u00a0study,<\/a>\u00a0published in the journal\u00a0Current Opinion in\u00a0<\/em>V<\/em>irology,\u00a0<\/em>emphatically states:<\/p>\n \u201cContamination, or false discovery, of non-viral sequences is a feature of all virus prediction software and should not be ignored\u2026In most cases, the time, expertise, and\/or computational resources are not available to manually validate all recovered viruses.\u201d<\/p>\n The study further states:<\/p>\n
\nTHE REAL FACTS<\/a><\/p>\n