Howell - covax adverse effects (covax = corona virus vaccine)

Table of Contents

• Introduction
• 21Feb2022 Do covax deaths account for ~50% of the official reports of covid deaths?
• Images : covax drives covid [case, death]s, plus it's own adverse effects
• Howell : comments on selected [paper, presentation]s
  • Howell comments : Jessica Rose's analysis of VAERS Data, increase in Deaths Following covax Shots
  • Howell comments : Kyle Beattie's Bayesian analysis of covax - ~30% increases in [case, death]s
  • Howell comments : Pardekooper's videos are handy to get started with database usage
  • Howell comments : Covax 'how might I cover my ass?'

• [random, rare, scattered] news items
• Covax references - [random, scattered] list
• Howell emails - reverse chronological order -

21Feb2022 Do covax deaths account for ~50% of the official reports of covid deaths?

Introduction

• ~400,000 people might have died from covax adverse effects
  (within ?+-100%?, don't really know the error bar, but there are estimates which I forget)
• Assuming that all covax deaths are reported as covid, then covax = covid deaths!!
  Reasonable assumption : given shannanigans in health system to date, we just can't trust [data, scientist, health professional, government]s.

Images : covax drives covid [case, death]s, plus it's own adverse effects

Rose 11Oct2021 "World Council for Health, presentation of VAERS data" Video posted on BrandNewTube.com 22Oct2021 by Kristall VAERS under-reports, (Howell : recent estimates by a factor of 40 for covax) teach immune system = natural immunity best; promote early treatment with known [efficacious, safe] drugs - ivermectin & hydroxchloroquinine (Howell : like Japan & India)	Howell - Jessica Rose's earlier ?Mar2021? presentation is the [earliest, most convincing] large-population study that I had seen. This presentation includes new results, and again, is some of the best work I know of in the area. I have seen no signs of any intelligent criticism of the study yet, although I'm not actively looking for it either. (non-priority for me at this time, and there is no intelligent life on planet Earth - especially not me)

Kyle Beattie 30Oct2021 "Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19" Abstract : ...This study analyzed publicly available COVID-19 data from OWID (Hannah Ritchie and Roser 2020) utlizing the R package CausalImpact (Brodersen et al. 2015) to determine the causal effect of the admin- istration of vaccines on two dependent variables that have been measured cumulatively throughout the pandemic: total deaths per million (y1) and total cases per million (y2)... Results : ...Causal impacts of the treatment on y1 ranges from -19% to +19015% with an average causal impact of +463.13%. Causal impacts of the treatment on y2 ranges from -46% to +12240% with an average causal impact of +260.88%. Hypothesis 1 Null can be rejected for a large majority of countries.

Howell - This study suggests that BOTH covid [case, death]s are strongly INCREASED by the vaccine programs!! Furthermore, that doesn't even include : serious adverse effects of the vaccine itself, other than covid-19 disease related potential exploitation by existing pathogens [virus, bacteria, microbe, fungus, parasite]s : of [weak, damage]ed physiology, caused by the vaccine itself of unknown new potential [mechanism, pathway]s arising from mRNA mechanisms potential [accelerated, cascading] evolution of new disease strains, perhaps entire new classes blah-blah - work on this later...

These graphs follow from intial work by my brother to see if covax effects could be discerned by comparing excess deaths to vaccinations over time. Neither of my crude super-positions are a convincing basis for saying much about covax adverse effects. A proper download of the data to create a coherent graph with better time-resolution accuracy might help, but I don't like torturing the data and shouting out "Eureka" bare-naked, especially when the great graphs above are sufficient starting points. This type of work is very easy to do yourself at home, but don't be as lazy as I was by just doing overlays. (instead, properly plot data downloads from OurWorldInData.org, who sourced from John Hopkins U, Baltimore USA)

OurWorldInData.org - UK covid [case, death, vaccine, excess deaths]	OurWorldInData.org - South Africa covid [case, death, vaccine, excess deaths]

Howell : comments on selected [paper, presentation]s

Howell comments : Jessica Rose's analysis of VAERS Data, increase in Deaths Following covax Shots

• great background, scientist (at least a strong thinker @<1:10k scientists, can't see "creative revolutionary breakthrough" thinker yet) - Canadian, immunology, applied math, Computational Biology! (I have been subscribed to a journal for years but never read it), a little bit of [biochem, biology]. What is her affiilation?
  
• It does seem that she IS the Canadian female research who presented a briant result back in Mar-May2021?I need her previous presentation to be sure, but also for historical purposes. (I will search for it)
  
• slide 08:03 : "causation is notoriously difficult to prove" (ahmen!!) - uses Bradford-Hill criteria (I am unfamiliar with this, medical-specific but includes common general criteria.)
  
  • "Most important is temporality" >> Howell - OK as a rule-of-thumb, but it depends! (Granger analysis I hear most frequen for this in time series analysis - but this are is nothing like hard-core electrical engineering time series math!
  • "The effect has to occur after the cause" >> Howell - NOT true in general!!! This is very important, as a key class where this may not apply is living systems (also some chaotic "dead" systems as well). But that is far beyond essentially all (@<1:10k) [math, stat, science] experts, and in practice is [unknown, ignored]. But it really should check at later stages of analysis, as with all [postulate, assumptions]s.
  • Coherence -"... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations" >> Howell - cart before the horse, and no shit Einstein!! [Common, massive] failure of scientist, reality cannot be ignored. experiments priority over theory, and neither can be [fully, permanently] trusted for complex systems! (see the next point on "Experiment").
• Reversibility - "if the cause is deleted then the effect should disappear as well" >> Howell - NOT true in general, only for very simple systems where one cause accounts for the entire effect. Very DODGY for [living, dead, engineering control] systems!
  
• Many iffy explanatory graphs ignore [test variance, state changes, phase shifts, etc] - kind of OK for public, not so serious to me.
  
• Bimodal distributions, eg slide 11:21 - these are important, and beg explanation.
  
• Evolutionary pressures -
  
  • Israeli ?Mex Train? group
  • 15:06 - did new strain ARISE because of mRNA coves? I asked that question as SOON as their development was announced, LONG before testing etc available, very long before deployment. Jessica Rose was slow on this!
  • [cosmic, galactic] rays - theme from long ago, and we just went though highest-ever levels recorded (several decades of measurements, maybe post 1950?) She has no clue here, but neither do hardly any scientists. I did mention these for influenza, covid] on my webSite (I think, perhaps as early as 2007?).
• Breakthrough infections - Always the case anyways, again, whe is a bit slow here. Perhaps smallpox, typoid others as exceptions, but more and more finding that supplementary vaccinations are required (eg for travel). Biology isn't nearly as stupid as we are!
  
• Booster doses concomitant to delta variant spread in Israel. >> nice
  
• >> Howell - "evolutionary pressure" is likely WRONG! "Evolutionary opportunity"is more likely, and there is a huge difference. Again, Rose is a bit slow here, falling back on [common, politically-correct] jargon, and not thinking very deep. For example, physiological changes arising from the covaxes, provide great opportunities for covid (virus). Think war, and the enemy capitilising on new weaknesses (eg Enigma code breaking), and not just for bioweapons. Think also binary [chem, biolog]ical weapons, which pprnents of a China conspiracy theory always seem to miss!
  
• slide 17:45 higher transmissibility is LUCKY! >> Howell - Ah, here she shows great insight. But that is until lethality very high (eg >30%?), and she misses that point. But extremly high transmissibility AND mortality is a completely different kettle of fish.
  
• ~17:50 ?Jurick VandenBosch? has warned of highly [transmissible, dangerous] from the beginning >>Howell I don't remember his name.
  
• In general, she uses the phrase "that's true" far too often (once is too often) - bad habit
  
• !18:25 Conclusions
  
• "The AEs themselves are a new injection-induced disease" >> Howell - brilliant way to put it.
  
• "The onus is on the opponents to prove that there is no causitive relationship" >> Howell - that's the opposite of the norm. In a precautionary principle" kind of way (usually a one-way BS distortion for conclusions-driven research) it is an appealing theme, and may be common in medicine? It's a REALLY bad principle in the sense that it is probably programming dumb-ass scientists to think that way in general!
  
• "Vaccine failure" >> Howell - ahmen
  
• https://ipaknowledge.org/joshua-kuntz-reserch-fellowship.php
  
• 20:58 "care in a real way for other people" >> Howell - most of them are socialists. Fuck them before they kill me! (OK - that's not gentlemanly, I am being an ass)
  

• A simple view is that Lamarckian changes occur during the life of an individual (thick arms of a woodcutter is a typical example), and that these can be passed on, for example up to 10 generations suspected in worms for one expweriment that I can't remember.
• There are suspicions that Lamarckian evolution is vastly [faster, easier] than Mendellian, [even if, because of] relative instabilty of changes. For example of one potential advantage (?), with important multi-generational changes to the environment, this could provide a very [fast, anticipatory] mechanism for [state phase] changes of the environment (Howell's guess).
• Lamarckian changes are often posited via DNA masking (methylation) and unmasking (de-methylation), but from my reading ~2005, there are many other mechanisms that could be involved!!
• Lamarckian heredity may actually be a dominant form of evolution for individual and "limited-generational" adaptations (evolution). (I hope that I got that right)
  Peter Ward 2018 "Lamarck's Revenge : How epigentics is revolutionizing our understanding of evolution's past and present" Bloomsbury Publishing, 273pp ISBN : 978-1-63286-65-8
  https://www.amazon.com/Lamarcks-Revenge-Epigenetics-Revolutionizing-Understanding/dp/1632866153
  >> this is a popular science book, not sceintific, but hte author is pretty [aware, sharp]
  

Howell comments : Kyle Beattie's Bayesian analysis of covax - ~30% increases in [case, death]s

• p4h0.8 uses OurWorldInData! me too!
• p2h0.0 "... Ibn Khaldun, 1379 A.D. ..." YES!! YES!! YES!! One of my (and Arnold Toynbee's) favourite historians!!!!
  "The Muqquadimah" 1410 AD and forever! Worked for Tamerlane (Timur Lenk) and saved the Janissaries in Damascas siege (castle of skulls built from all others?) Forget the rest of the paper, I love this author, even if he is from Edmonton [Oilers, Eskimo] territory.
• Causality is always very trickey! I wonder how Granger causality (common in neural networks) relates to the CausalImpact software package?
• Great to see the source code in the paper!!!
• natural distributions are often NOT Gaussian - but it appears that something a (OR)[windowing, kernel, other] approach addressed this in the CausalImpact software, this I need to look at more closely out of interest (maybe someday ...)
  • in Neural Networks, Gausian assumptions are often sub-optimal, occasionally misleading
  • this is a very [common, lazy] assumption - ceck reslts and re-analyse, but that is beuond most scientists?
• sloppy author : Dotplots are linear in rates, should be logarithmic!! (can't see the data!) Tmay hidea logarithmic trend, which seems to emerge in the plots, and which could reinforce autho's conclusions
• Robert Hecht-Nielson's Confabulation Theory - concept used is often NOT Bayesian (albeit Baesian by practice), especially with "naive Bayes" assumption, contradiction of concept - lack of understanding of what "Bayesian" means and its implications, pragmatic fudge - but theoretically priors are almost infinite in many cases
• missing labelled scatterplot of all individual country cause-effect [case, death]s
• "... To the latter point, the calculations in the CausalImpact software package and the code presented above accounted for the differing dates when the vaccine administration started in each country and it is not likely that the Delta variant arrived in each of these countries precisely at the time each vaccine administration also started. Rather, it is more likely that the vaccine administration causes a bottleneck effect in each region and helps to create even more deadly variants as Ausschuss et al. (2021), Bossche (2021), and Ricke and Malone (2020) all warned, which may translate into a rise in cases and deaths associated with COVID-19 as a result of the causal impact of vaccine administration. ..."
• "... Normally, seasonal die off from pneumonia, influenza, or COVID-19 is in the winter, so this spike that appears in many countries after vaccine administration at this time of the year or in countries with no previous outbreaks is highly suspect as not being a natural trend, but rather vaccine-induced. ..."
  • >> forgot to mention common cold
• "... As mentioned above, a future study utilizing these methods, but looking at all-cause mortality rates rather than total deaths/cases per million will provide better pre-intervention data (as it goes back decades in some cases), which will be useful for prediction of trend lines. It will also provide a better understanding of the overall causal impact of COVID-19 vaccine administration on the general health of the population rather than just the causal impact on COVID-19 associated deaths and cases. ..."
  • >> Steve's "excess death" analysis suggested as important future work? (11 Future research p58h0.2)
• "... Finally, due to the announcement of the use of ivermectin by the Tokyo Medical Association and the state of Uttar Pradesh in India and the dramatic reduction in cases and deaths after administration (Seth 2021; Hannah Ritchie and Roser 2020), and based on other promising research in repurposing ivermectin for use against COVID-19 (Santin et al. 2021) a similar study to that presented here could be done to understand the causal impact of mass ivermectin administration as has been practiced now in Japan, India, Peru, and El Salvador, aside from the many tropical countries that administer ivermectin bi-yearly as a malaria prophylactic. Should enough countries adopt this policy and hould the data become available in the future, this would be a worthwhile causal impact analysis to pursue. ..."
  • > Steve noticed this long ago
• New Zealand (Catherine) see graph p16 - bucks the trend : in spite of very little exposure until recently NZ seemed to benefit from covax, or maybe from Catherine's magnetism? estimated : -19% deaths, cases not shown - not signifant results?
• Vancouver (Sarah) - acts like a separate country, but not treated as such (no results)
• Singapore (Sarah) - like New Zea, positive death benefit of covax?
• Calgary - we don't care, we're sick of covid

Howell comments : Pardekooper's videos are handy to get started with database usage

• Craig Paardekooper video seems similar to Jessica Rose's ?earlier? analysis?
• uses VAERS database - This is a training video for researchers - how to generate [data, plots], with helpful commentaries
• snoozer of a video, nothing past what Rose already did and stated? but very handy for database newbies
• again - no explanation of low-bimodal distribution

• ~00:45? uses vigiaccess.org - World Health Org (WHO) >> Howell - now this is handy! I didn't know this one.
• This is a training video for researchers - how to generate [data, plots], with helpful commentaries
• ~11:46 Finally presents summary tables by [effect, vaccine], but :
• text is too small to read clearly
• doesn't show ratios for instant [viewing, interpretation] - just show one table at a time (via screen capture)!
• Pfizer vaccine affects twice as many women as men >> Howell - CAREFUL! My guess is that women make hugely more [compalint, demand]s, and are far more scared than males. Might not mean anything, but could, especailly if most entries are by professionals not under political pressure. (a bit later in video) OK - very common 2:1 ration across databases etc. Much more telling.
• EudraVigilence - Europeabase, URL provided, instructions for use
• Repeated comment that government has done NO follow-up, and have active "no-concern"!!
• Pfizer report URL : bonehead shows link without URL!?!? dumb

Howell comments : Covax 'how might I cover my ass?'

• there is not much chance of changing "turbic thinking" anchored in the great religions of science
• scientists almost never apologize
• absolute crap [science, scientist]s can endure for [year, decade, century, millennium]s

• socialism is the best example of this - how they actually won out has great lessons

• this will dampen covax deaths if misleading [graph, analysis]s are done right, stealing ammunition from the "covax is a BIG mistake" argument
• manipulate health databases, change historical records - as was done regularly by NASA Goddard (James Hansen) for "CO2 is the primary driver of climate since 1850" theme. It became a running joke among thinkers that when James Hansen gave speeches, historical datasets would change overnight. There are probably many other examples : it may even be a standard misinformation technique for policies.

• divert attention from [government, academic] culprits to everybody's' favourite whipping horses
• Pfizer especially is seen as "evil"
• leverage off most deniers (eg Jennifer Rose, even Mallone), same socialist viewpoints
• built up litany of "corporate malfeasance" - as has been done with Pfizer
  • settlements are often simply a low-cost legal reality in spite of innocence
  • a few bad apples spoil the bunch - except for [government, science, health] where all is excused for public good
• I suspect "under the table" protections and immunity from lawsuits was provide BEFORE mRNA covax [develop, deploy]ment

• blame deniers for a big portion of covid AND covax deaths
• spread this label to any media who give report what "deniers" say, or show any sympathy
• 14Jan2022 Steve added - if all cause deaths go way up Fauci and the left will automatically claim deaths are because the unvaxed loaded the hospital system delaying treatments, and delays causing all those excess deaths
• this is different from the [censor, repress] item below

• it has to be done the right way. Although thinkers will see through this, most [public, scientists, medical, media] are programmed intellectual robots and will follow the media churn
• INCREASE current measures : have them [fired, emoted, moved] from government jobs (mostly academic) - as with the CO2 theme
• [mass, social] media censorship of deniers - pressure on [journal, [health, science] association]s

• make it sound like experts all agree that covax effects are minimal and were always expected -
• keep hammering that very few people are affected (near zero), while smothering that covirus effects are also near zero
• suppress comparisons of co[virus, vax] cart [case, death] rates - public doesn't suspect that the rates are not much different
• fund promotional effort with left-wing foghorns in the name of [honest, all people, protective, etc] themes
• use perenniel left-wing jargon :
  • "we are the only ones who care"
  • "we are the first to initiate this"
  • "Trump did nothing"

• [confuse, delay] the "covax denier's" arguments under a sea of horseshit science
• advantage here is that most scientists produce horseshit anyways, and can't tell the difference, so modus operundi or something like that
• hype the "surprise new variant" theme
• few (<1:100) [public, health, scientist]s seem to have caught on to the "normal expectation" of variants (asexual reproduction NORM), in spite of centuries of experience, just as they don't understand the limited effectiveness of vaccines historically
• create new initiatives to develop new technologies - again diverting attention, confusing [expert, public]s

• they are mostly left wing, make it an exaggerated political survival imperative?

• 14Jan2022 Steve added : war with Russia or some great distraction like that
• 14Jan2022 Steve added : opportunity to declare martial law
• hype even harder on vaccinations - without the intent of ramping them up (socialist rhetoric approach)
• hype even harder on Trump as the culprit
• continue past deceptions, they are working well
• [blackmail, extort, ridicule] "covax deniers" - create whole new effective [words, phrase]s appealing to the [stupid, left-wing]
• silence "covax deniers" (as per item further below)

• funding agency messaging is often a key
• almost all journals are government fund dependent (eg [library, university] purchases, conference support via science budgets)

• eg [hydroxychloroquine, ivermectin] - used in other countries effectively from the start, only now are anti-vals being softly pushed in USA< but won't allow [safe, proven] products because of risks of being seen as politically-driven [liar, moron]s
• emphasise anti-virals recently approved & used in US, start going more aggressively now that immunization is higher
• [ignore, suppress] sppression of anti-virals for ~1.5 years, non-approval of foreign anti-virals
• actually, keep pushing "America first, we're the best" to justiy ignoring foreign [treatment, pharma, science, result]s