Asymptomatic = Not Sick
23 May 2021 - 11:59 am
So if you believe the “act like you have it” nonsense where the Holy Science claims that perfectly healthy people are walking virus factories that need to be muzzled, kept at home and tested as often as possible to see if they have an illness they don’t know they have, you might also believe that the PCR “test” can actually detect infection and has some clinical significance.
It turns out neither of those are true. If you are “asymptomatic”, a word now used for what we referred to 2019 PCE (Pre-COVID Era) and before as “healthy” (specifically regarding major respiratory illnesses, of course you could have something internal wrong and not know it at first) it really does mean “not sick”, and this study, despite how it is framed, proves it.
The study is called “Household Transmission of SARS-CoV-2 A Systematic Review and Meta-analysis” and is dated December 14th 2020. It includes lots of statistical stuff that isn’t always easy to unpick. The important part of this study is on page 5 of the PDF version that says:
To study the transmissibility of asymptomatic SARS-CoV-2 index cases, eFigure 8 in the Supplement summarizes 27 studies reporting household secondary attack rates from symptomatic index cases and 4 studies from asymptomatic or presymptomatic index cases. Estimated mean household secondary attack rate from symptomatic index cases (18.0%; 95% CI, 14.2%-22.1%) was significantly higher than from asymptomatic or presymptomatic index cases (0.7%; 95% CI, 0%-4.9%; P < .001), although there were few studies in the latter group. These findings are consistent with other household studies reporting asymptomatic index cases as having limited role in household transmission.Page 5 – https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2774102/madewell_2020_oi_200987_1607354087.12032.pdf
The takeaway bit is that the “Estimated mean household secondary attack rate” from “asymptomatic or presymptomatic index cases” was “(0.7%; 95% CI, 0%-4.9%; P < .001)”. This means the “attack rate” for asymptomatic/presymptomatic cases is statistically zero. This is because the CI (Confidence Interval) is technically 0.7 ± 4.2 which results in a range of -3.5% to 4.9%, but as an “attack rate” can’t be negative, the statistical value is truncated to 0 (zero).
Interestingly, the next paragraph in the study starts with:
There is evidence for clustering of SARS-CoV-2 infections within households, with some households having many secondary infections while many others have none.Page 5 – https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2774102/madewell_2020_oi_200987_1607354087.12032.pdf
They don’t appear to have an explanation for why some households had no secondary infections at all, which considering this is allegedly the most contagious virus to strike humanity seems odd. As this considerable lack of secondary infections would suggest that perhaps there is another cause that does not involve transmissibility and the whole contagion thing the fraud is predicated on, it is no surprise that they simply ignore that and just average (mean) the figures out across the households.
This study is not seeking to put people’s minds at rest, or undermine the mainstream narrative, but the information is there for all to see if one chooses to look. Inside households, asymptomatic spread is statistically zero, and symptomatic infection is not guaranteed to spread, even in the same household. This pokes gigantic holes in the lockdown/masks and social distance zealot’s arguments, even more so outdoors.
The strategic use of averages is the statistician’s go-to when irritating discrepancies crop up in datasets. In the 1954 book “How to Lie with Statistics” by Darrell Huff (one of Bill Gates’ favourites) the use of averages to present a pre-determined narrative is described in detail. Another method described in that book is the implied “cause and effect” with no actual proof, or what is referred to as the “semi-attached figure” which is data that appears to be related but is in fact irrelevant. These kinds of statistical shenanigans are standard fare with State Science and the mainstream media.
Darrell Huff’s book is not intended as an instruction manual, although Saint Bill likely lifted a few ideas from it like he has with pretty much everything in his “career”. It is meant to be a nice, easy to understand warning to the rest of us about the misuse of statistics. It is well worth a read as like any magic, the spell is broken once you know how it works.