The % Positivity and daily deaths by Date of Death trends meaningfully diverged for the first time during the first two weeks of November…
In the 1st wave, correlation between (i) deaths by date of death and (ii) fatal cases with an episode date 12 days prior was almost perfect, with R=0.983 and R^2=0.976…
The second chart shows the onset-to-fatality lag correlation signal…
In the 2nd wave, this correlation between episode date of a fatal case and deaths 12 days later weakens slightly (R=0.966, R^2=0.933)…
But the lag correlation signal is much weaker…
In the 2nd wave, starting in early November, the correlation impairs materially, and some “noise” appears to enter the dataset… (R=0.758, R^2=0.575)
Here are all three correlation/regression graphs shown again together… 1st wave, 2nd wave, 2nd wave (Nov 1 to Dec 15 only)….
Essentially what is implied by the data, is that the relationship between (i) symptom onset date and (ii) date of death *changed*, for the 1st time ever, beginning in November… consistent with divergence in %pos and # of deaths noted above. I don’t have an explanation for this.
We can see this visually a second way… a log chart showing the clear lag between (i) the episode date of fatal cases and (ii) deaths by date of death in the 1st wave, while the lag in the 2nd wave is a bit less pronounced, especially beginning early November...
One interesting (I think related) phenomenon occurring in Nov & Dec, however, is a material increase in the percentage of fatal cases (especially in an outbreak setting) that have a matching symptom onset date and test date… big change from first to 2nd wave and in Nov/Dec…
Same chart… perhaps it is that testing became more efficient in Nov/Dec (especially in outbreak settings) driving prevalence same day symptom onset and testing…
Either way, the basic question is: why are more and more fatal cases having matching symptom and test dates?
"Global Canada” (@GlobalCanadaOrg) appears to be the funder of the #CanadianShield strategy (@csc_canada_), essentially a #COVIDZero rebrand, supported by Ont Science Table members. It seeks extended, hard-lockdowns Canadawide.
Global Canada is funded by Bill Gates’ Foundation…
Global Canada (“GC”) is run by Executive Chairman, Robert Greenhill (@RobertGreenhill). Mr. Greenhill is a former high-ranking official at the World Economic Forum, serving as Managing Director and Chief Business Officer for 6 years until 2014, when he founded Global Canada…
Global Canada (“GC”) was founded in 2015 and has since received three grants as disclosed on the Bill & Melinda Gates Foundation website. The first two were in November 2015/2016 and total a modest ~$1.3m…
Quick summary of the total *garbage* economic “analysis” by @csc_canada_ and the #CanadianShield folks (aka #COVIDZero). This is their “evidence” for more damaging, widespread lockdowns in Canada, which I now believe are inevitable given this new PR push….
I believe we're now in danger of full Canadawide "hard-lockdown" for at least 3 months. This is the appalling #COVIDZero strategy unofficially supported by the Science Table, rebranded as #CanadianShield. Economy/society won't make it 'til Spring. ⬇️is based on garbage analysis!
This is the thin, bankrupt economic analysis the group puts forth, which contains no analytical rigor, and assumes the economy can bounce immediately to full capacity and the "end" of a lockdown. Utter absurdity. drive.google.com/file/d/1ZDcal5…
There is no evidence of a deadly epidemic in the community....
We are now seeing striking correlations between (i) the # of COVID19 tests and (ii) % positivity within/across certain important individual PHUs in Ontario.
This has implications for local lockdown policy, and for the global PCR/false-positive debate...
2/
If you are not in Ontario, but involved with / following the PCR debate, this is still of interest to you. We are seeing similar testing/positivity correlation dynamics as seen in the UK. This thread borrows/builds substantially from @profnfenton here:
Upfront caveat: correlation does not mean causation, and it is difficult to draw definitive conclusions from simple testing data. However, it is important to determine whether lockdown policy (for its unmeasurable collateral damage) is informed by false/flawed test results.