“Step #1: Find some vague criteria for what constitutes the symptoms that you want people to look for. Anything subjective that a lot of people can identify with is ideal. Let us take memory problems and/or confusion + a few common ones from the Covid list. Tiredness, aches and pains are common and subjective enough. (For covid19 the symptoms are: fever, dry cough, tiredness. Less common symptoms: aches and pains, sore throat, diarrhoea, loss of taste or smell, a rash, or discolouration of fingers or toes). It would be a good idea to take something that is very common in old people so that we can use death from old age as proof of the lethality of the new virus.
Step #2: Then we would need something biological to test. Any RNA sequence would do, as long as it is not present in the whole population. If it were, someone might claim herd immunity very quickly. Actually it could be an RNA sequence that does not really exist in humans but something that could exist as contamination in labs, e.g. in dust or water. [I've left out the next two paragraphs in this step simply for brevity purposes]
Step #6: All you need now is for people to bring their old and confused elderly in for testing, and with 5% false positives, we will soon have most hospital beds filled with old sick confused patients. We can count on doctors to treat them aggressively. Most of these old people will be on a cocktail of drugs already, so adding a few more drugs as “heroic treatment” will be sure to push them over the edge. Many will have pneumonia from the seasonal flu, so we can just prolong this by putting them on ventilators. Then they will die a month later and we can say it wasn’t the flu since the flu season should have stopped a month earlier.”
In recent months, accelerating seemingly by the week, there has been an obvious shift in emphasis away from what really matters in any health related situation, legitimate hospitalizations & fatalities, and on to the so called 'case'. IMHO, and I explain my reasoning in a separate article, the manner in which the corp media, govt groups et al are using the term case is a complete misrepresentation of the meaning of the word. IMHO this is obvious simply by examining some dictionary definitions so I can only conclude that we're not looking at random chance here, incompetence or simply 'the evolution of a story' but rather a conscious decision to do so. Example: for pretty much everyone in the Northern Hemisphere, most assuredly in Canada at least, the colour orange has signified warning/caution and the colour red has signified danger/threat. I think it would be hard to effectively argue against the extremely pervasive use of this colour scheme in exactly this manner. I also think it would be hard to argue against this scheme having been used this way for literally generations.
Why then, during a time they themselves are incessantly promoting as active danger, active threat, would the CBC choose to use orange to represent fatalities and red to represent cases? I'm highly skeptical that this can be attributed to incompetence for example; the choice really suggests narrative promotion instead. Interestingly enough, an earlier image on the page (right) could be considered 'use appropriate' given cultural norms:
Here's another one: given all the possible colours of the rainbow, why would the Ontario govt group choose to use brighter colours for cases and washed out gray for fatalities on their dashboard page?
Check out these 7 slides for some interesting observations.
Casedemic . . .This one I found to be particularly sneaky. 'Active', meaning the so called experts use of the term 'cases', is totally irrelevant but here we can see an attempt to make 'em appear more concerning and scaring by an attempt to link cases to hospitalization as well visually promote the we-should-be-very-afraid narrative via the red colour…wankers.
Note also the statistic that has way more meaning than active/cases is the line at the bottom, the one that never goes very high…gee, I wonder why…rhetorical.
Another trick, tests: how about downplay or don't even mention the amount of tests being completed and/or any issues at all with the tests themselves. Second point first: promote the overall public impression that these tests are like pregnancy tests: pee stick, simple, it either is or isn't and one of two results means something major is going to happen in the life of the testee. Further, give the public the impression that these tests are common, ‘tried, tested and true’, easy to execute and performed by trained professionals thereby suggesting in the mind an automatic assumption of effectiveness and high accuracy. For starters, a RT-PCR is NOT a test and has never been a test. Secondly, a RT-PCR Assay tells nothing about illness or potential for illness, that's not its function and yet the clown parade would love you to believe otherwise…I wonder why…rhetorical.
To the left is the past 10 days during which there were a few separate days of govt/media shouting the covid equiv of fire. This is a sample of the data which has been used as justification to once again infringe on your rights and reimpose social restrictions:
Not 100% accurate in methodology and result to be sure but close enough to correlate the point:
Total test increase: 64.85% (65%)
Total test decrease: 39.44% (39%)
So, in just the last 10 days alone, there has been an overall 26% increase in the amount of testing performed. Now, it only stands to reason that if one is looking for something that is potentially scattered throughout a group, a group of almost anything really, there is an increasing probably of finding what you are looking for as a ratio of the amount of anythings that were tested.
In the context of SARS-CoV-2 testing, assays performed from folks in hospitals, LTC (Long Term Care), any institutional based setting really, stand a much better chance of returning positive simply due to the nature of the setting. For example, LTC is close quartering in facilities that concentrate possibilities: incontinence in the case of transmission and aged immune systems in the case of fatality as a couple of examples. People are presenting at hospital and being admitted to hospital because they are exhibiting the published symptoms…or having anxiety attacks. What we need to remember is that the average Joe and Josephine are not in those groups and I would wager that there's not a lot of secondary contact(s) with those groups either.
The overall testing point is, the way to even begin to come close to painting an accurate picture of a public health scenario via a testing regime would be, among other important considerations, to start with the same number of individuals being tested and staying with that number throughout. But really though, that's still not enough. It would need to be every individual in a designated geographical area every day at the exact same time as the previous day just for starters. This is really knocking on the door of impossibility and, given the data we've seen so far from the covid fiasco, it is not even remotely warranted.
Time to really dig in! Was that a groan I heard, the clicking of an X perhaps? Honestly, I really appreciate you hangin' in this far, thank-you!
Here is the statistic I'm going to use:
At 5.7%, this is the highest number of assays returning a positive result since May 24th. In the context of the current media dramatics, this then represents a best case scenario for them because it's representative of a worst case scenario of sorts for us. Further, it can't be explained as easily by testing increase since it is only a 2.5% (724) increase over the previous day. Second last finally, a note on results prior to May 24th: there are many instances of higher positive percentages than 5.7%, however, these occur during the time frame when sanity still prevailed somewhat and testing was limited to those presenting symptoms or working in high(er) risk settings thereby rendering a comparison of that time frame moot: it paints an entirely inaccurate picture of what could be occurring in the general population. It's like testing for eyesight issues among those who wear glasses; any results from these tests cannot accurately be used to determine overall eyesight issues in the general population. For our purposes here, I'm going to assume that the 29,125 PCR assays completed where taken from a reasonably wide sampling of the general population and that each of these were performed on separate individuals ← NOT the case in Ontario; recorded test results can be from the same individual tested multiple times.
First thing we need to do then is grab 5.7% of 29,125 which is 1,660. Remember, all this means is as a result of running a certain number of cycles of a amplification/multiplication/manufacturing tool called RT-Polmerase Chain Reaction and using a sampling of 29,125 individuals, 1,660 of them returned a positive indicator for the presence of RNA material believed to be from a virus that has been given the designation SARS-CoV-2. It means nothing other than this, period. Here's another reason why any meaning derived from these assays is very limited in scope, from the insert of a PCR manufacturer: “Positive results do not rule out bacterial infection or co-infection with other viruses. The agent detected may not be the definite cause of disease.”
So, using the average of the recover percentages, 92%, of the 1,660 positive assay results, 1,527 will recover leaving 133 fatalities. For some context, on average last year 2019, ~2056/week died in Ontario.
To arrive at the most accurate picture possible with the data at hand and the context with which it is being used, we have to remove the high risk group from the list. This is justifiable given what we know about the disproportionately high mortality rates among the elderly, particularly in LTC, rates which are not reflected in the data of the 0-69 age group.
Further, with LTC only, this segment of the population does not circulate among the general population to a significant degree; they're approaching a closed loop when compared to the general population.
Using only the data from the 0-69 age group then, the average recovery rate becomes 99.25% should one have a positive assay result. Of the 1,660 positive results then, 1,647 will recover leaving 13 fatalities.
Just for shits ‘n giggles, let’s take this out across the entire population of Ontario in the 0-69 age bracket. According to the 2016 census, there are 1,814,990 folks in Ontario 70 and over. The entire population was 13,448,494 so our sample for this exercise is:
13,448,494 - 1,814,990 = 11,633,504.
Using the 99.25% average recovery rate, conversely a 0.75% fatality probability (yes you read that right, only 3/4 of one percent), this translates into 87,251 fatalities out of a possible 11,633,504. Even removing the 0-20 age group still leaves a statistically insignificant fatality rate!
A govt group infringement on rights is never justifiable as far as I'm concerned but for many it's OK, even in that context though, does this really seem like a data reality that can justify the draconian actions taken by gov/media, the same actions they are threatening to take again?
For any thinking so: this is why people avoid you in life if they can, delusional ends justify the means type thinking tends to do that.
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