Foundations of Mind IX
Follow the Sun: how two rebel neuro-scientists solved Covid - 19
© Seán Ó Nualláin & David Bernal-Casas
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The book “Follow the sun” shows that hours of daylight, temperature and relative humidity which all can be gotten from free public sources are the main drivers of covid-19 transmission . In the spring, the decline of cases from peak is almost entirely dominated by daylight hours – or to be more precise, daylight seconds! Here is a simple little app that you can have fun with and check against the real numbers for that day – again available free on the web!
For Ireland we established this formula where Y is number of transmissions and X is number of daylight seconds
Y = exp(a + b*X), with a = 25,8428 and b = -0,000375156.
Y = exp(a + b*X), with a = 25,9625 and b = -0,000368539.
These look daunting but we're going to show you how to use them in 2
minutes! First of all, you look up the daylight hours in Dublin and Madrid using these tables;
Remember Madrid has shorter days than Dublin in summer. Now convert this to seconds by multiplying by 3600. For example, 14 hours is almost exactly daylight in Madrid on May 2 and 14*3600 is 50400.
So we put this in the formula above and get exp( 25.9625-(0.000368539*50400))
Here is the really cool part. You can simply copy and paste this into the following free online resource and press return;
You get 1,616.7 and the real value is 1781 so this is not bad and certainly better than what the state scientists were predicting.
The longest day in Ireland is almost exactly 17 hours or 61,200 seconds long and we put in to get
exp(25.8428 -0.000375156*61200) which as you can verify is 17.872 which again is closer than the prediction of the state “SIR” model.
We invite you to check that for Ireland on May 25, we get the bullseye – both the model and real data give 59!
Please note the real data is what we call “noisy” with bias due to labs closed on weekends, late reports of tests and so on.
Nevertheless, you will find our model works really well from peak which was 20 March in Spain and 16 April in Ireland until the end of June After that, temperature and humidity become more important in ways the book explains as it sets out a model that works for transmission anywhere in the world, anytime.
You probably don’t realize it, but you are almost certainly familiar with Ballinesker,
County Wexford, in the extreme South East of Ireland, where many of the key ideas in
this book were hatched. This beach with a Nordic-sounding name has entered your
dreams, stirred you to fantasies of heroism, and otherwise invaded your psyche.
An esker is a glaciated ridge, and this one runs just west of the chilly waters of the
Irish sea, and of course “Bally” is the Gaelic “baile”, or town. . In turn, just 50m west
of the Ballinesker beach entrance is a testimony to the antiquity of the human
occupation of the place; a further ridge which housed a ceremonial grave and a gold
hoard dating from 800 BC was found there.
If you visit, you will find many swimming sans wet-suit in water that rarely exceeds
10 degrees Celsius. Those young people with metre-long paddles in their hand are not
about to assault you; they are playing the Gaelic sport of “hurling”. Nothing to do
with a bad night; it is a sport arguably dating back thousands of years to ancient
Greece and best summarized as 3-d field hockey with full body contact.
Take a moment to enjoy one of the many web videos of this sport! And now try and
find one of the videos of the invasion of the Normandy beaches in “Saving Private
That is actually Ballinesker, not Normandy. Ironically, Sean lived in Normandy
during most of the writing of this book, and was in Ballinesker mainly to have access
to a beach
– forbidden in France during their “confinement”.
A Londoner called Michael Willmore gave Orson Welles one of his first acting gigs in
Dublin’s Gate theatre. Willmore had re-invented himself as Micheal Mac Liammoir
and, together with his partner Hilton Edwards, became a central figure in the
Thespian development of the Irish Free state. He was wont to flights of fantasy, one
of which was an oft-ridiculed ecstasy about the glory that is an Irish May.
Ironically, late spring 2020 was indeed glorious in Ireland. The Hawthorn emerged,
ebullient, promising a beautiful summer. The twisted foliage of the trees, shrubs and
bushes that betrayed Steven Spielberg’s attempts to make it look like Tom Hanks
was walking through the much lusher Normandy briefly relaxed to offer itself to the
Approaching the solstice, it became noticeably light as early as 3-45 am and at 11pm
was still dusk, rather than dark. In fact, it may even be the case that our childhood
memories about cloudless skies in summer Ireland were not false. For whatever
reason – and the lack of jet plumes might seem an obvious one – the skies were often
cloudless to the extent that betokens a surfeit of water vapour in the air.
There is little to do in Ballinesker at the best of times. Without Wi-Fi or TV, one had
the choice of listening to the flood of warning stating that one was about to die,
though of course one knew that already. Sean is a Gaelic speaker, and contented
himself with a few hours a day listening to the music programs preparing to sprint
across the room to turn off the radio before Pravda/ the news again reminded him of his
Because there was something clearly wrong about the information we were being
given. Unquestionably, Covid-19 is highly transmissible, and was almost certainly
designed to be so, almost certainly in the grotesquely irresponsible biological
tinkering by Shi Zhengli of the Plague 4 lab in Wuhan. It is fact that she and her
colleagues had worked for years on making corona viruses more transmissible
before succeeding in 2015 by grafting a “spike” onto a corona virus. It is fact that
the Chinese government will never release the lab books that could technically
exonerate her. It is also fact that, by undermining our Western traditions of
untrammelled and indeed legally protected free assembly, the Chinese may have
dealt our great western civilization a death blow.
Inevitably, many who died in 2020 had traces of the virus. However, almost 90% of
them had an underlying prior life-threatening condition; and both the mean and
median age were almost exactly at the extreme lifespan for humans of 85. Nobody
under 15 had a death attributed to them by Covid-19 even in the fanciful calculus of
NPHET, the body entrusted by the Irish state with managing its response.
The boozy, sociable Irish were told that their social promiscuity was a national
health menace. Before Tinder and something new called “dating”, the Irish had
perfected a system of maximizing choice and range of sexual and indeed life
partners by creating mingling spaces in bars where young women could feel
perfectly safe in talking to perfect strangers. That has been one of the unspoken
competitive advantages in Irish tourism.
All changed, changed utterly; a terrible boredom was to be born. Bars could
reopen if they served food, and another something new was honoured; “social
distancing”. To complete the end run around Irish hospitality, music (including
non-saliva generating instrumental music) was banned from pubs.
Because, in a truly deranged application of a field called statistical mechanics, the
model stated that we all had a bit of Covid. (Sean is old enough to remember being
told the same about original sin). But this model was Hamlet without the Prince, cast,
director, and actors. Not really Hamlet, surely; and this was not really science. As it
turns out, our models clearly show, using publicly available data, that all this
grotesquerie had precisely nil positive effects on health, as the paramount factor is
the waxing and waning of the number of virus particles in the air due primarily to
hours of daylight, and secondly to water vapour in the air modulated by
There was a second problem. The statistical model used by NPHET, and imposed
worldwide by the WHO, was clearly wrong. Decrease of the number of transmissions
clearly showed a negative exponential. Think of it as compound interest on your loan,
but in reverse as the bank acts wholly out of character and tells you your repayment
schedule is so good that they are systematically reducing the loan further. . Every
day, people saw newspaper diagrams showing this negative exponential, while the
explanation given was due to a 1927 model when the necessary non-parametric
statistics did not yet exist.
Remarkably, that 1927 SIR model, which we explain and appropriately expand, was
used in its virgin state by Neil Ferguson not just for Covid-19 but for the original
SARS as for swine flu, foot and mouth disease, and much else. Since it is above all his
predictions that, channelled by the WHO through the executives of many states in a
manner we found disturbing, ruined several months of your life, let’s take a look.
Parametric statistics are essentially based on the 17th century work by that
Huguenot refugee in London, de Moivre. They assume a normal distribution and a
bell-shaped curve, with concepts like variance, confidence interval, standard
deviation and so on emerging naturally. In one of his frequent visionary moments,
Ferguson felt confident in stating that with a confidence interval of 95%, deaths may
be between 0 and 50,000.
While perhaps the most useless prediction in history, akin to the famous “It will rain
somewhere, sometime”, this approach is also epistemologically wrong.
It has been known since April 2020 that Covid-19 shows exponential decay, and our
first insight was to try and find a signature of this decay in publicly available
transmission data. We immediately hit the jackpot, as chapter 1 shows; but we also
wanted to avoid the clearly inappropriate assumptions of parametric statistics. Since
Covid-19 was decaying rapidly in the springtime of the countries we were studying,
each hour was effectively a different population. Moreover, Kant long ago explained
that you cannot derive causation from correlation; what you can do is establish how
much of a more encompassing concept than variance called variability was related to
an independent variable and that we did.
We are neuroscientists, accustomed to fugitive and noisy data protected by the
effective filter that is our thick skulls and this was not a difficult problem for us. A
typical problem worthy of our skills is, for example, to establish that in the short run
neural data manifest a near-random clustering on several “power law” foci but in the
medium and long term show their real nature. That nature is what is called
modulation of a carrier wave; rather like when you choose 95.7 on your FM dial to be
the carrier wave and wait to hear the music which reflects its modulation. We need
non-parametric statistics for this type of work, and the WHO uses primitive
In fact, we now use the Covid data as an introductory pedagogical tool for
neuroscience students. If they can’t solve this problem, they should go home, get a
job, and raise a family as anybody wanting to practice real neuroscience – and,
increasingly, other forms of real science – will end up doing a lot of their work
unfunded in places like Ballinesker without a son to say Kiddush over them when
they shuffle the mortal coil, which is very unlikely to be the result of Covid-19 .
Ballinesker doesn’t even have a shop! So Melanie and Sean, who was continuing the
writer’s practice of a lifetime in being self-isolating, would drive a few times a week
to the nearby Spar (think 7/11) in Blackwater and we would look at the transmission
rate of the day. Of course, they didn’t call it that; on the basis of swabs and the rather
iffy technique called PCR (polymerase chain reaction), roundly criticized for its false
positives by its discoverer, the renegade Novelist Kary Mullis, NPHET would equate
the tiniest presence of a virus particle with infection.
That aside, the transmission data were noisy, as the now privatized system of elderly
care homes was unwilling to admit how many their system of forced enclosure was
Indeed, they got a judge to agree with them on this in Arizona. So when we graphed
transmission rates against hours of daylight starting from the peak in Spain and
Ireland, we expected at best the kind of mixed result from nature that our mentor,
the great Walter Freeman III, had to make do with from the brain. Luckily, we were
able to present this result at the thermodynamics 2.0 conference on June 24, 2020,
and its analysis with its truly astounding result comprises chapter 1.
But we had a problem. India and other hot places initially proved refractory to Covid-
19 before they got even hotter. Relative humidity did not seem to be a cure-all as
Ireland – to take one example- also has high humidity. However, the number of
grains of water vapour in the air at the Irish summer median of 16 is almost exactly
half that of a climatic zone of the same humidity at 27. We now had two new math
techniques to bring to bear.
One is some real statistical mechanics, as distinct from the lunacy that instructed
citizens not to stray more than 2km from their homes, be they in Ballinesker or
Manhattan, to avoid congregating in groups of more than 4, and to stay 2m apart as
they were all bearers of original sin. Remarkably, the Irish bishops had a similar
obsession during our flirtation with theocracy in the 1930’s, as they fulminated
about the possibility of young people bumping into each other in the stairways of
the new Dublin public housing.
Einstein’s Annus Mirabilis of 1905 included breakthroughs in special relativity and
the photoelectric effect, it was the latter for which he was awarded the Nobel.
However, it was the statistical mechanics paper from that clutch that proved most
immediately useful. The English claim Brown of Brownian motion saw grains being
buffeted around by atoms; as this is impossible, suffice to say that the French claim
that they invented the theory of mass action by billions and indeed trillions of
particles may be right. The vocabulary includes random walks, white and pink noise,
and this is the first topic of chapter 2, the most technical in the book. We propose
that the main carrier of Covid-18 is water vapour; dehumidifiers and ultraviolet light
may be the closest we get to a panacea.
The second topic again derives from our neuro work. We found that the model
suggesting that neurons are harmonic oscillators, rather like singing guitar strings or
a pendulum or anything in such a fixed cycle, emerged naturally that and gave a
much larger palette of explanation than the idea that neurons have a linear threshold
which, when exceeded, firing ensues. This came from a concept from
chaos/complexity theory called a bifurcation. We prove that using a “saddle node”
bifurcation, we can model all the data points from initial cases to asymptote when
cases nearly cease and this is discussed in chapter 3
But did the lockdowns do any good? Absent a vaccine, can we expect repeated
lockdowns until “nerd immunity” ensues”. Dear reader, go to chapter 4 and weep.
Chapters 5 discusses the most controversial issues; whether the virus was engineered,
and deliberately released.
We proceed to Part 2 of the book, concerned about science and society, and more
specifically the biology and politics of Covid-19. In the absence of appropriate theory,
front-line staff undoubtedly did heroic and self-sacrificing work. Nevertheless, the
fact that biology and medical students are forced to learn to parrot the “central
dogma of molecular biology”, an access of foolish archness by Francis Crick, surely
gives pause and makes one long for the return of reasonable ideas like the
Immaculate conception? At least the latter cannot be refuted; Crick’s dogma is
trivially refuted by RNA retroviruses like HIV.
Secondly, we have been asked to give thanks that Ireland, joining the international
scientific bandwagon, had on hand epidemiologists, virologists, serologists,
immunologists, and so on. But it looks very much like none of them had the cross-
disciplinary expertise to handle this problem, particularly as the corona virus called
the common cold has remained aloof from vaccines This chapter delves into such
issues as, grosso modo, why modern medicine has yet to establish why patient A gets
ill while patient b does not.
Chapter 7 then goes deeper into the origins of the virus. The ACE receptor and its
relation with angiotensin is an enormous biology conundrum. ACE2, an antagonist to
ACE, as we shall see is probably the most important of possibly many targets of Covid
19. Why was batwoman/ Zhengli allowed to continue her “work”, particularly in the
face of warnings from the US embassy in Shanghai about the safety protocols in her
lab? And whence Bill Gates’ interest in corona viruses, funded by him in England, and
health in general given that his business model of crappy software with frequent
memory-greedy updates requiring new computers not only provoked the rise of free/
open source software, but resulted in literally billions of PC’s leaching toxic
chemicals? We take some time at the end of this chapter to reflect on greater societal
Because Covid 19 was such an easy problem to solve, we doubt we are the only ones to
have solved it. At the height of our recent war with the UK, an Irish computing
department used to teach 70 students a year how to break British military codes. That
too was not hard to do; the professor involved became internationally famous when
he, a PH.D. from Strathclyde U, pointed out that the “expert” sent to debate him at
the inquest into the murder of 3 IRA activists in Gibraltar had fully 2 years of tertiary
education. Prof Scott went on to write a tech report “Lies, damn lies and forensic
evidence” unfortunately no longer available due to “change in priorities”.
We now know that the British had by the 1970’s invented a form of RSA, still the
industry standard for encryption, but kept quiet about it. They had plenty of real
experts to send to the Gibraltar inquest. The cognitive dissonance of listening daily to
inflated “infection” and “death “ stats gives pause. Is this not take 2 of SARS? Why has
not a single competent scientist been heard? What is wrong with state funding of
science and the tenure/publication system that left us so vulnerable? The book ends
with proposals to rectify all this with a reparse of nature including a complete institutional
and metaphysical overhaul of science research and teaching.
© 2020 Seán Ó Nualláin
"Misconceptions about weather and seasonality must not misguide COVID-19 response."
Seán Ó Nualláin & David Bernal-Casas
Abstract; short version
In their recent article, Carlson et al. (2020) argue that COVID-19's vulnerability to UV, temperature, and humidity does not scale to the real world of epidemiology. We believe this evinces premature closure. On the contrary, our work shows that these vulnerabilities' signature is evident in the publicly available transmissions data. The failure to see it may be a signature of what Kuhn called "abnormal science," wherein epicycles added to the Ptolemaic SIR model to save the phenomena cannot forever prevent the use of non-parametric statistics and non-linear models in epidemiology. In particular, we describe how an extended formalism, including exponential decay and saddle-node transitions, allowed predictions accurate at the 99.1% level in North Dakota as it entered the nadir of its crisis.
Keywords; paradigm, epicycle, saddle-node, non-parametric
Response letter; full version
Based on the SIR/SEIR/SIS model, billions of people had a year taken out of their lives, and trillions of dollars have been lost. The SIR/SEIR/SIS model was formulated in 1927. It is a compartmental model without a variable for the number of virus particles. In 1939, it was established for the first time that viruses are a mixture of RNA and protein. This fact makes them particularly vulnerable to environmental factors, which, as we argue below, are of paramount importance in tracing viral epidemics' progress.
In May 2020, the DHS's biodefence department took the unprecedented step of inviting the media into their sanctum sanctorum (https://www.nbcnews.com/news/us-news/inside-secret-dhs-lab-testing-how-long-coronavirus-can-survive-n1201386), labs wherein they evaluate and investigate ebola, covid-19, and other biohazards.
In case the public could not fully comprehend the miasma of disinformation, they provided a user-friendly app (https://www.dhs.gov/science-and-technology/sars-airborne-calculator).
Carlson et al. (2020) argue that while this may be all well and good – at least as these things go – the transmission effects are 1%. However, the independent evidence they invoke to support this (Carleton et al., 2020) says nothing of the sort and is a carefully phrased assertion of a correlation between a single standard deviation change in UV and a decrease in transmission. Indeed, the abstract ends with this statement, which we believe our work fulfills:
"Thus, while our findings indicate UV exposure influences COVID-19 cases, a comprehensive understanding of seasonality awaits further analysis".
We believe this evinces premature closure.
We start with basic science. COVID-19 is an extremely nasty and extremely transmissible coronavirus. Except in summer in the northern hemisphere, it is not a good idea to have mass assemblies until herd immunity is achieved either through a vaccine or repeated exposure. It is about to wreak havoc all over the northern hemisphere. There will be much more illness and death this autumn and winter among those who did not succumb in spring. However, the Southern Hemisphere will continue to see attenuation of its effects through its spring and summer and transmissions fell off a cliff in South Africa after the Xmas bulge.
One of the central receptors of covid-19 seems to be Ace 2. Receptor Ace 2 is an antagonist to Ace, which is related to hypertension, but Ace 2 has many other human physiology roles. Once the coronavirus has accessed this receptor is itself extraordinarily nasty. However, the intellectual virus that caused the world economy to lose trillions of dollars was much worse. Sir and its variants, the epidemiological model used, do not have a variable corresponding to the number of virus particles. Covid-19 is even more vulnerable to daylight and temperature than most viruses. The model used by governments worldwide can only work near the equator and, even then, in a minimal way.
Our first discovery was to use publicly available data about daylight hours to compare to the decay of covid-19 exemplified as the number of transmissions in Ireland and Spain. We got an extraordinary result from these two very different countries, with the exponents and intercepts converging up the solstice. We extended the methodology to include temperature and humidity - not coincidentally, the two other factors in the DHS app - and successfully modeled the epidemic in both these countries from January and February.
But these results were minor compared to the actual revelations. When we examined the data about Arizona's transmissions, we found that daylight hours, temperature, and humidity explained over 99% of the variability. In short human interventions like lockdown and change of behavior had no effect whatsoever. This finding lets us ask whether lockdown in spring in the Northern Hemisphere hasany good effect whatsoever as clearly there will be many such lockdowns.
Our answer was a categorical no. Heaviside, not Maxwell, was the physicist who wrote the formulas we use in college. We used his stair function and boxcar function to find the variability explained by a lockdown between 0 and 1%. It is also fair to say that locking people indoors almost certainly caused many illnesses and deaths.
We defended our mathematical approach at Thermodynamics 2.0 on the 24th of June, describing a significant modulation of daylight hours on the number of cases. On the 10th of August, we presented our work showing that Arizona's transmission could be explained at over the 99% level only using environmental factors. At the end of this response letter, we further describe our predictions for North Dakota, successful at 99.1% level over 19 days, proving the approach's success.
Indeed, US biodefence has vindicated our approach by making this covid calculator accessible, as we saw. With our viewpoint strengthened by this basis, we confidently predicted spring lockdowns were ultimately harmful and ineffectual, as our work shows the lengthening days did all the work.
However, perhaps due to Trump's cuts, they could not make the predictions we made. So, we decided to utilize our mathematical model for making predictions this autumn.
We were able to predict North Dakota transmissions before it went into lockdown at 99.1% accuracy over 19 days, sending our predictions beforehand to the health authorities there (Danielle Hoff) as well as many politicians and journalists.
Next, you will find the predictions made for the critical state of North Dakota. You will see the real values in between parenthesis:
The 26th of October: 948 (*896*),
The 27th of October: 1010 (*781*),
The 28th of October: 975 (*1222*),
The 29th of October: 934 (*1357*),
The 30th of October. 971 (*1433*),
The 31st of October: 971 (*1128*),
The 1st of November: 1228 (*975*),
The 2nd of November: 1303 (*1198*),
The 3rd of November: 1355 (*1116*),
The 4th of November: 1411 (*1540*),
The 5th of November: 1477 (*1764*),
The 6th of November: 1549 (*1615*),
The 7th of November: 1529 (*1111*),
The 8th of November: 1442 (*1160*),
The 9th of November: 1364 (*894*),
The 10th of November: 1399 (*1039*),
The 11th of November: 1501 (*1801*),
The 12th of November: 1564 (*1441*),
The 13th of November: 1595 (*2278*).
Considering the predictions reported to the Health Department in ND, we estimated in advance 24507 positive cases vs. 23406 in reality. In other words, we were 99.1% correct.
Using this model, a model that includes environmental variables, we have a very significant predictive power. While we could predict the number of cases in North Dakota, we can also generate good predictions in other states and countries worldwide, considering the same generic model.
*With this response letter, we believe we have refuted the arguments put forward in Carlson et al. (2020): "Misconceptions about weather and seasonality must not misguide COVID-19 response". Please, should you require further details, do not hesitate to contact us. We will be happy to give you more evidence.
Carlson, C.J., Gomez, A.C.R., Bansal, S. et al. Misconceptions about weather and seasonality must not misguide COVID-19 response. Nat. Commun. 11, 4312 (2020). httpshttps://doi.org/10.1038/s41467-020-18150-z://doi.org/10.1038/https://doi.org/10.1038/s41467-020-18150-zs41467-020-18150-z.
Carleton, T., Cornetet, J., Huybers, P., Meng, K. & Proctor, J. Ultraviolet radiation decreases COVID-19 growth rates: global causal estimates and seasonal implications. SSRN Electron. J. (2020).
Kuhn, T. The structure of scientific revolutions. University of Chicago press (1962).
Wangping, J. et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Medrxiv (2020). httpshttps://doi.org/10.1101/2020.03.18.20038570://doi.org/https://doi.org/10.1101/2020.03.18.2003857010.1101/2020.03.18.20038570
Ó’Nualláin S. and Bernal-Casas D. Follow the Sun. Published by Red Books Press (2020).