Norm_Fenton.md 5.3 KB
Flawed Data Driving the Narrative
Intro
- Bayesian Networks - Smart data > Big data
- appled in Law and Forensics
- looking at data for 22 months
- concern about how statistics are used to drive the narrative
- lack of evidence for lockdowns and mandates
- bias to work: even if as dangerous as claimed and measures as effective as claimed, no justification for lockdown measures and vaccine passports
- don't need sophisticated statistical skills to understand that the threat and preventative measures are overstated and exaggerated
- qualified people remain silent on this
Itinerary
- Censorship
- Data problems
- Vaccine efficacy/safety (all-cause mortality)
Specialization
- Bayesian-causal modeling techniques
- use publicly-available data
- national differences in CFR/IFR
- published in peer-review
- called for random testing early (regret this)
Censorship
Initial perspective
- Infection rates higher than predicted
- Fatality rates lower than predicated
- Disease less lethal than what was reported -> before effective treatment protocols were available
- Extensive research into risk factors/symptoms
- Incorporate data into causal model
- Packaged as personalized symptom/risk assessment app (web/mobile)
- Exposed flawed in reports about particular risk factors
- Looked into ethnicity
- Extensive work on symptom tracking apps
- All work was well-received (it didn't challenge anything about the narrative)
Changing views
- Sept 2020 clear the narrative is driven by flawed approach of equating case with PCR test and asymptomatic testing
- Pointed out problem of showing increasing case numbers without understanding changes in tests being performed
- Diving two numbers is a subversive act of misinformation - called conspiracy theorist
- Demands to be sacked frm Queen Mary
- Exposed flaw government data/claims which relied heavily on PCR
- Concerns about vaccine safety/efficacy
- Nothing was reviewed / was rejected by pre-print servers
- Now only on ResearchGate and blogs
- All videos have been removed (banned from posting videos on YouTube)
- Letter into American journal of theraputics - looked at Bayesian model analysis techniques
Data problems
Fundamentals
Definitions
- Case => Positive PCR test, dies of reasons other than Covid => Covid hospitalization and Covid death
- All metrics driven by Covid Case
- How many asymptomatic?
- How many hospitalized for non-COVID?
- How many infected at institution?
- How many deaths with infection vs from infection?
PCR Positive
Cases include:
- Virus + symptoms
- Virus + presymptomatic
- Virus (never symptomatic)
- No Virus + symptoms
- No Virus + no symptoms
False-positive rate
Asymptomatic person tests positive and there's a 1% chance of false positives:
- 1 / 100 test positive Person testing positive likely to have virus?
- Depends on infection rate
If 1/1000 have the virus Test 10,000 people
- 10 have the virus
- 100 false positives
- 10/110 = 9.1% chance positive test is accurate Less than 10% chance you really have the virus. The vast majority of cases are false positives.
Example
Cambridge rigorous testing of asymptomatics. 5000 tested per week for 6 weeks 43 positives 7 validated / confirmed cases 36 false positives
Can't know if those confirmed positive went on to develop symptoms. Confirmation test was only PCR, reducing accuracy
Lack of confirmatory testing
- PCR obvious problems
- What's less well known is that the biggest cause of differences in reporting PCR false positive probabilities is whether the probability assumes a confirmatory test is being carried out:
- If the false probability for a single PCR test is 0.5%
- All positives require a second test before announcing confirmation
- If independent test, probability both are false positive: 0.005 * 0.005 = 0.000025 = 0.0025%
If few people are being tested and you have luxury of confirmatory testing, you will almost see no false positives. Confirmatory testing is almost never done Most positive PCR test results really are false positives
Lighthouse Labs
- March of 2021 discovery that lighthouse labs were classifying positive cases on the basis of a single target gene
- WHO and kit manufacturers require minimum of 2
- In some weeks, 50% of total cases began as false positives (before applying above probability logic again)
Impacts
- 41% of those testing positive never develop symptoms
- Jan 2020 - Dec 2021:
- 4.7% didn't have major underlying comorbidity
- Most of these not hospitalized because of COVID
- Post surgical units showing up to 68% COVID hospitalizations acquired infection post-admission
Children (< 18)
- 5830 hospitalizations (2020)
- 251 ICU
- 28 dead
- 8 confirmed likely caused by COVID
- All 8 with comorbidities
- 7/8 had life-limiting condition
Comparison
Daily UK gov cases vs Daily ambulance/emergency calls (NHS dashboard) No relationship whatsoever
Vaccine Efficacy/Safety
Problems highlighted with definition of COVID CASE and VACCINATED PERSON also undermine the results demonstrating vaccine effectiveness. The comparison between cases in vaccinated/unvaccinated that determine effectiveness.
In Lancet-reported observational, any error resulting from case / vaccinated person definitions are compounded by the fact that there are different testing strategies for each:
- Asymptomatic/unvaccinated tested regularly
- Asymptomatic/vaccinated not