The HART group gives some interesting arguments as to why they are rejecting the idea that viruses do not exist. One is a flawed statistical argument and others are observations of genome sequences. None of this explains the epidemiology. This page shows why they are wrong and suggests an alternative model for influenza outbreaks.
The statistical argument is listed at number 1 and is tackled first.
The screenshot below is from their website
- Note that it is number 1 on their list.
- Not everybody got sick as part of an outbreak and that number of people is considered “significant“
- People who shared the same environment got sick at the same time – consider then an environmental cause or trigger
- The majority of sufferers were part of an “outbreak” i.e. an event where many people got sick.
If an outbreak is defined as a large number of people getting sick at the same time then it isn’t surprising, given the population distribution in a city, that the majority of sick people got sick as part of an outbreak.
Shown here is a city grid of equal sized squares where some squares are densely populated and others not so. This sort of situation is ideal for studying epidemiology if only decent data were available. We can look at how many people in each cell got ill and try to correlate it with population density.
First imagine that some non-infectious pathogen is introduced into some of the cells on a completely random basis. Think of toxic gas being released, 5G death rays maybe or some sort of bio-energy beamed down from space.
We would see then that the chance of a cell showing disease in any of its residents is unrelated to the population of the cell so that a sparsely populated cell is as likely to show disease as one containing many occupants.
In this case then, disease would be correlated to location and since the release of a toxin into a densely populated cell would result in many people being sick we would see in the overall population that the majority of sick people would necessarily come from cells where there were many other sick people.
This is really just saying that the majority of people in a city come from population-dense areas.
This much is obvious. It is also precisely what is described in point 1 of the HART group’s statement above. They even attribute the illness to “having shared the same environment” as opposed to “having been with other sick people“.
Now let us fantasise about infection. If this were possible then we would expect that infection would spread more effectively in densely populated areas. We would also expect an increased likelihood of seeing disease in these areas as there are many more people to introduce the pathogen into the grid square from elsewhere.
Statistical analysis should then show a correlation between cell population and the occurrence of disease in that cell. Recall that in the first case, cells get affected at random and there was no correlation with population density; a 5G death tower does not know how many people there are living nearby.
There is the possibility then that an infection model can be proved or disproved merely by looking at some statistics.
If only somebody had thought to collect some data..
Fred Hoyle (1915-2001) was an astronomer and statistician who looked at the epidemiology of flu by studying incidence of the disease in English public (boarding) schools. Some pupils will board at the school and be in contact with each other 24-7 whilst others will go home at weekends or evenings. [paper]
All these schools have children of the same age groups, eat similar food, are subjected to the same harsh exercise regimen and engage in stereotypical social contact. Schools themselves are organised into ‘houses’ and dormitories, giving a controlled structure to the possible transmission routes.
These are ideal conditions to study incidence of influenza. Hoyle looked at epidemiological patterns as described above and found that, for example, a dormitory full of boys was as likely to demonstrate influenza as a solitary boy sleeping at home with his parents.
Hoyle believed in viruses but still concluded:
- Person to person transmission is ruled out as a significant cause of the disease.
- The overwhelming cause of the disease comes from ‘elsewhere’.
- Low level outbreaks occur completely at random and unconnected to each other.
- Larger outbreaks occur in geographical clusters which vary in size from a whole school and its environs to a single dormitory or part thereof.
- A virus is ruled out as the actual cause.
- Viruses are manufactured within the body in response to an external trigger.
- The external trigger is some kind of ‘virion’ that comes from outer space.
- [The results do not indicate food poisoning or collective detox.]
The chart below shows that population influenza is best modelled by a simple gaussian distribution with a mean around winter solstice and a 90% interval of only a few weeks.
Observations must be explained and the ‘viral model’ does not explain the epidemiology.
The members of the HART group know this and know that transmission studies have failed, but are still sticking to their model because: “The virus model explains all of the above in a way that no other proposed model can (yet).“
Other individuals have complained of a lack of a better alternative and that viruses are ‘still the best explanation‘ for what they are seeing.
Again, from the HART group: “Scientists form a model that best explains the majority of the evidence. ” So we need to explain the epidemiology.
A statistical model:
- Seasonal incidence: Outside of the tropics, populations will succumb to influenza in the two weeks either side of winter solstice
- Latitudinal patterns: Finer grained structure is seen along lines of similar latitude (Hoyle and others)
- Local outbreaks are delineated by location and are distributed at random
No mechanism is suggested here but we have achieved:
- Prediction of timing: over 95% of cases will be in midwinter although how many seems to vary a bit.
- Characterisation of outbreaks as being somehow related to location (we can’t even say environment).
- Better interpretation of epidemiology: The assumption that clustering implies contagion is incorrect and has been dangerously misleading.
- Scope for further research: What is there that is special about certain latitudes and locations?
- A model that actually fits the observed data: Other models based upon the flawed assumption of transmission have failed spectacularly.
This seems like a good basis for a model as being grounded in observed reality. We can refine it later and look for biological mechanisms to explain these patterns but the foundation should be as described above.
The HART group, by contrast seem to want to plunge straight in with assumed bio-molecular causes and to worry about the facts of disease later on. This is the wrong way round to do science: the ‘majority of the evidence‘ needs explaining.
The group is asking the virus sceptics to explain various molecular and genomic phenomena. These all sound very interesting but they do not of themselves constitute disease and have not been shown to cause any disease.
Towards a mechanism
The model described above is purely statistical in nature and may well make useful predictions but it gives us no ‘understanding’ and describes no biological mechanism whereby disease might be caused by seasonal change.
- Seasonal incidence: This is so precise that the only possible way that this can be achieved by resonant entrainment to some seasonal influence, either daylight hours or maybe the Earth’s magnetic field.
- Latitudinal coincidence: This again suggests the Earth’s magnetic field is involved.
- Local outbreaks: Tricky. Hoyle suggests virions from outer space, I will suggest cosmic ray showers or eddies (vortices) in the Earth’s magnetic field, but I am certainly open to alternatives.
What else is it that we need to explain?
The HART group is asking to explain things like a unique RNA sequence found in people who appeared to have similar symptoms, Now nobody goes to their doctor complaining about a unique RNA sequence. We don’t need to explain this, we need to explain the symptoms.
The symptoms, even by mainstream accounts, are caused by an altered bio-regulatory state. This state (erroneously referred to as the ‘immune response’) consists of an an orchestrated sequence of events leading to symptoms that include sweating, muscle aches, elevated temperature and lasts usually five days before returning to normal.
I don’t say ‘returning to homeostasis’ because this state is managed by the body itself and is perfectly stable although not sustainable.
It is this state that causes distress and constitutes what we call ‘disease’.
For this process to fit within our model then, we are looking for some way that it is produced as a direct result of the seasonal rhythms and without the intermediary of a viral particle.
This is the research to be done. It sits firmly within the purview of bio-regulatory medicine and not so much virology or genetics.
Top-down causality is common in biology and is implemented via means of attractor systems which interpret external stimuli to effect change at the cellular and even molecular level. Attractors can be highly sensitive to rhythmic input. It is quite conceivable that people in similar physiological states can produce RNA with similar sequences.
Now since most disease is just assumed to be viral in nature it follows that most disease research is performed by virologists who are really geneticists and think almost exclusively in terms of bottom-up causality, that is to say, that a small piece of RNA has the ability to destabilise a system that demonstrates organisation and robustness to perturbation at all levels.
The idea of an attractor is not something that would readily spring to mind to one trained in biology but it is essential for the understanding of living systems. Attractors are the key to top-down causality, providing an interpretive interface between the organism and its environment.
Influenza is just a sudden phase change in an attractor state triggered by some external input. Genetic events are the end point of attractor activity, not the primal cause.
Why do symptoms differ between individuals?
This is behaviour typical of chaotic attractors. Paths will converge to the attractor but diverge on the attractor so no two people will demonstrate identical disease progression. Attractor phase states are general patterns which are not precisely definable or predictable. Attempts to refine diagnosis by increasing accuracy or number of measurements will just cause confusion as there is no meaning in these details.
Since each individual is on their own specific attractor path and in their own ‘state’ at midwinter, it is now expected that not everybody will get ill at solstice. This is natural behaviour for attractors. We would expect that there is a component of disease risk that is actually independent of other health factors; an element of ‘randomness’.
Attractor states are highly stable, making routine treatment somewhere between difficult and impossible. Phase changes can be sudden and apparently non-causal, resulting in what are usually described as ‘miracle’ cures. So miracles do happen and we now have a scientific explanation for them.
Attractors present a problem from the point of view of determinism . Their behaviour is stable and predictable insofar as they will reliably produce meaningful biological patterns that result in a robustly functioning organism. However, this behaviour is not predictable from examination of their parts and not predictable from any finite history of that behaviour. This is a hammer blow for traditional reductionist science; there will always be something incalculable and unknowable where biological systems are concerned.
Summary:
- The data comes first, the explanation comes later
- The mechanism of genetics is interesting but does not cause flu
- Influenza is a disturbance of organisation – not cellular damage
- Top down causation is provided for by attractor patterns
- The presence of attractors implies a ‘cloud of unknowing’
- Observations (seasonality) require explanations
Related pages:
References:
Why HART uses the virus model – Arguments against “the virus doesn’t exist”
https://www.hartgroup.org/virus-model/
Viruses from space – Fred Hoyle
https://www.hoyle.org.uk/resources/virusesfromspaceCompressed.pdf
Surveillance of influenza and other seasonal respiratory viruses – UKHSA
https://www.gov.uk/government/statistics/annual-flu-reports/surveillance-of-influenza-and-other-seasonal-respiratory-viruses-in-winter-2021-to-2022
The gene: An appraisal – Keith Baverstock
https://pubmed.ncbi.nlm.nih.gov/33979646/
Epigenetic Regulation of the Mammalian Cell – Keith Baverstock, Mauno Rönkkö
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0002290&type=printable
A theory of biological relativity: no privileged level of causation – Denis Noble
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3262309/
“Meaning of Life & the Universe: Transforming” – Mae-Wan Ho
ISBN-10. 981310886X ; ISBN-13. 978-9813108868