My Cousin Vinny - The film deals with two young New Yorkers traveling through rural Alabama who are arrested and put on trial for a murder they did not commit and the comical attempts of a cousin, Vincent Gambini, a lawyer who had only recently passed the bar exam after several unsuccessful attempts, to defend them. Much of the humor comes from the fish-out-of-water interaction between the brash Italian-American New Yorkers (Vinny and his fiancée, Mona Lisa) and the more reserved Southern townspeople. (Wikipedia)
Legally Blonde - The film tells the story of Elle Woods, a sorority girl who attempts to win back her ex-boyfriend by getting a Juris Doctor degree. (Wikipedia)
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Some thoughts on the modelling data released 2020.04.03 by the Ontario government group. First and foremost, I hate to be the hard ass here: as I get on in years, i have increasingly less hard ass to use, but it's gotta be done:
Further, if SARS-CoV-2 is as nasty as is claimed, then wouldn't we want to have the absolute best information to work with? I'm going to keep dropping this in wherever I can, but I really don't think Ontarians have a full grasp on what the economic implications are of shutting down a province, especially in the current turbulent economic climate that the globe was/is in. Canada is wobbly on its own, Ontario too, but our goods and services exchange has become so highly interconnected and highly dependent that other nation's problems are our problems too. I also want to point out again how really precarious and inflated our financial and economic systems have become with the hope of creating an appreciation for the precarious position we were already in.
Given the potential severity of consequences as a result of decisions being made, it's beyond irresponsible not to want as accurate data as is humanly possible. There are thousands of 'literal lives' and millions of 'economic lives' (potentially turning into literal lives) depending on correct choices being made.
That means the source data and modelling functionality have to be the best they can be (do I have to put a TM here?).
Another angle: the inclination to just accept information at face value when it comes from an authority source. I'm curious, specifically in the context of govt, if there is a deep seated "my taxes paid for your salary" planted in the average Joe/Josephine psyche. We hear it often enough through the media, TV shows in particular for me, and I'm sure we've all said it in some form or another in our lifetimes too. We also know when seriously considering the matter that we do indeed pay for their salary, it's how that system works. If so, is this a base motivator for wanting to accept media delivered and authority figure delivered information as is? Further, knowing that the money taken from us pays their salary as is were, if we couldn't or didn't trust the facts and figures, we'd have to admit that a) we were wrong b) we got ripped off. Nobody is really interested in either of these two I'm sure.
The straight up solution to the above is exactly the same as the more above. The only way to know if you got your money's worth is to have the data and modelling peer reviewed by independent sources who have nothing to lose or gain due to outcome. Lastly, just like you paid their salary, you also paid for the costs incurred in generating this report so you own it and have every right to see and examine all the tools used to produce it.
This is not pie in the sky shiite.
The government groups claims all the time they are for the people. The people referred to are the only reason the gov group exists in the first place and every single thing is (forcibly via taxation) paid for by those same people. Setting aside the couple of outliers for a moment, there are two classes of humans on our planet: those who pay taxes and those who live off said taxes. The latter group therefore, for the duration of living off the efforts of others, they are 100% answerable to those others, us, in all things, period.
If claimed not possible! for this or that reason, then those proclaimers are just further proving the point that government is a failure as a concept, cannot work and must get the ole bootola out.
There should never be any apologies or apologists when it comes to demanding our liberty from govt or ANY other group or individual; if those liberties are not protected or respected, then we also have every right to "thanx but no thanx" while heading our separate directions and with no negative reciprocation's (directly or indirectly) from those would be pissin' on your turf, ever.
Enough with that, as mentioned, I didn't look hard and some of the above may be a moot point should it already be available.
- believed to be confirmed cases
- morbidity not necessarily based on COVID-19 alone.
There are some major considerations that MUST remain first and forefront:
a) Where is the peer reviewed data showing the accuracy of the testing process? What are the additional common issues that can affect the outcome of a test?
b) This is almost the number one problem: unless we had an accurate test administered to everyone in the province from Ontario patient zero and really, every day forward from that point, then all these numbers are only valid for the tested/deceased group, the rest is guess work. A simplistic example but it makes the point:
1 case / 1 death = 100% morbidity
2 cases / 1 death = 50% morbidity
10 days pass
3 cases / 2 deaths = ~63% morbidity
3 cases / 1 death = ~33% morbidity
From these example numbers we can see how to approach calculating the morbidity but we can also see that transmission rates are low if only 1 or 2 new cases appear after 10 days. Although extreme for example purposes, we can also conceptually see how numbers as presented can create a pretty grim picture: the morbidity rates are brutal.
Now, keeping with the assumption that the case numbers in the example are the result of testing, let's add one more component: these numbers are taken from an overall population of 100. Now we're left with some pretty important questions and the numbers change meaning pretty drastically. Without testing the entire population the meaning of these numbers is very much obscured:
1 tested / 1 case / 1 death = 100% morbidity (still scary)
100 tested / 75 cases / 1 death = 1.33% morbidity (supposedly on par with seasonal flu, but interestingly or tellingly, I'm having some difficulty in tracking down source-able percentages. This is only interesting in that the morbidity or mortality percentage is slathered all over the MSM media with regards to COVID-19 so why is it difficult to find the same slathering with regards to something that is also a major public health issue and has been happening for eons? Is it me? Is it them? Is it me? This inquiring mind wants to know. Maybe I just need to do the math…eww)
Slide 04 saidON Model Slide 04
This table is no surprise and has been steady pretty much from the get go. The Italian/Spanish/NYC/California data (those being ones I've reviewed) mirrors this closely. The case rate from 0-59 at 2,205 vs 1,047, basically twice the amount is interesting and does show, coupled with the higher mortality rates for the older age group(s) that like many of the viruses that target humans, influenza, pneumonia, very young & older humans are more likely to have difficulty getting past the infection.
There's another key piece missing here and that is co-morbidity/underlying medical conditions. It's critical to determine for each case whether the patient died FROM the coronavirus or just WITH it. I'm able to find this information for many other jurisdictions but not for Canada/Ontario. If anyone else stumbles upon it, please let me know!
Slide 05 saidON Model Slide 05
This one and the next one have no bearing here whatsoever in my opinion; these are apples to zebra comparisons. Homo sapiens are individuals, cultured by region and socially active meaning that even within the boundaries of Ontario itself there are very different areas where the residents are very different in many ways. This difference is formulated in a large part by the environment they reside in and the environment that immediately surrounds them. What then is the point of comparing Ontario to jurisdictions that aren't even on this continent, have vastly different populations, cultures, living arrangements, social habits, dietary/nutritional habits, transportation habits/availability and so on. All of these things have a direct impact on transmission as well as survivability.
Doug Ford had this to say on 2020.04.01:“The hard truth is, right now, today, there is very little separating what we will face here in Ontario from the devastation we’ve seen in Italy and Spain. Thousands of lives are at stake,” Ford said.
Ok then, I'm real curious as to what he was basing his comment on. I added the incremental numbers to make it easier to determine number. I wonder why the authors felt the need to leave those out: it doesn't clutter the chart in any way but when left out it does help to obfuscate. Further, not having a uniform scale up the left side is a graphing no-no, that is Graphing101!. Looking at the last plot on the chart which presumably is April 1, 2020 as indicated by the notation at the bottom: what is separating Ontario from Italy and Spain is roughly 20,000 thousand cases! That's a pretty big separation if you ask me, definitely not "very little" ..dunno, love to hear other thoughts but to me, aside from showing that Doug is grossly misrepresenting the data, 05 & 06 are otherwise meaningless in this context.
Perhaps he was referring to the upward trajectory that appears a little more parabolic in slide 06 whereas Ontario appears flatter; "Look, it's working! If whatever we say is not continued to be done, these two countries could be our fate!" Certainly a possibility, however, I'm leaning more towards a cynical interpretation. The spreads in numbers are substantial making them a long way to go before catching up, if they actually ever would as well…
Question: there's a very LOT separating us so why use the term at all given how dramatic it sounds, dire in tone, fear inducing?
Answer: because of how dramatic it sounds, dire in tone, fear inducing.
[as an aside: from the same article, Ford also says this:
""We know a surge is coming."
Pressed by reporters, Ford declined to provide a specific date about when that surge could happen and defended the decision not to release a forecast of the potential number of novel coronavirus cases that the province could see, saying the models vary widely. "
This is a highly inflammatory and fear inducing comment and yet not a stitch of anything to back it up except a vague reference to a model. Extremely irresponsible and disingenuous!]
Also curious, what exactly are they trying to say with x-axis? It might be my skull thickness to be sure, lemme know, but to me that scale seems kinda weird and really doesn't have much to do with anything relevant to the scientific analysis we need at this point. Future historical purposes perhaps, but not current.
Having said that, it is overridden (I wanted to use trumped, but yeah.. ) by the apples to zebras comparison of Ontario to either one of those countries.
Slide 07 saidON Model Slide 07
This one is a handy one to include so kudos here. As mentioned above, at least I think I did, there is some data missing that is crucial to match up with this timeline. The exact date that an individual took a test and the date the test was processed and received a positive test result. This is imperative to match with a timeline like this to begin to determine how social actions, forced or otherwise, impacted the transmission rate.
Slide 09 saidON Model Slide 09
They say it point one, and I'll say it even more fully:
These are only models and models are only as good as the modelling logic and the source data…garbage in, garbage out.
"There is more confidence in the projections…"
Please see comments on the seventh point.
But I'll bite: without comparison or scale, this is meaningless. Out of 100% confidence, I am 1% confident in short term projections, and I'm 1.5% for long term; whoopdeedoo do, good for you, but forgive me for not jumping up and down if you are trying to be reassuring with the more confidence line.
"Assumptions" 'nough said
What observed data, where and when and by whom was it observed?
How did you compensate for the radically different environmental and socioeconomical differences?
Other than a logical guess, how did you determine and measure "improved public health measures"? Without being able to reset everything and start again, this time doing nothing and comparing, how can you arrive at any answer? Strikes me best case scenario would actually be a few resets, each time changing the action and gauging/comparing the results. I'm not trying to be deliberately obtuse or something here, it's reality as I see it and is exactly how scientific testing is done. This type of testing is impossible for civilizations so we're left with guessing? More assumptions. [as an aside: the study of economics suffers from the exact same problem]
Sorry, I can't resist the sarcasm on this one: "no shit Sherlock", "tell us something we don't know", "state the obvious much?"
Slide 10 saidON Model Slide 10
Had to make the big point on worst case scenario somewhere, enter slide 10. But no bestie? Where's my bestie case? Again, this is nice to look at and all but really has no bearing on what is happening in Ontario and what we need to do going forward. In the real world there may be some value in comparing something like this with slide 07 but the same data integrity restrictions apply. These types of comparisons are for post pandemic, not during.
What it does do however is drop a what is sure to be fear inducing number of 98,000 worst case scenario cases.
We're seeing lots of data that shows most of the cases, assuming they are in the general population and not in a nursing home or among a population with a high density of seniors, particularly with existing conditions, folks have mild symptoms or none at all. This chart to me is the numeric equivalent of word salad.
Slide 11-13 saidON Model Slide 11-13
Welp, since we're dealing with model data only, of course these ones are going to be trotted out for our viewing pleasure. The impression left in the minds of viewers will indeed be the left bar. Also note the drastic difference in the middle and right bars from the left and the further drastic differences between the two.
The first (left) column is purely nothing until my first point, that is, the data and modelling are independently peer reviewed is completed.
The middle column is, well, purely nothing as well. As already discussed, there is no rewind, try again button therefore no way to determine this number with any degree of decision making accuracy.
The last (right) column is, IMHO, an attempt at getting Ontarian's prepped for even more liberty seizing measures "full future intervention". The press conference was not very detailed in this regard and since it is in the future anyway, the numbers here are also purely speculation.
This applies to each of the respective slides.
From Ontario.ca at time of writing: Summary of cases of COVID-19: Ontario, January 15, 2020 to April 5, 2020
Total cases: 4,347
So you're saying that while in 81 days, the province had 4,347 accumulated cases, even including a time frame (much of March) that saw a seemingly rapid transmission rate AND heavy handed societal shutdown, in the next 25 days we're going to add 295,653 more?! I mean seriously, even with the 10,000 case testing backlog now cleared it doesn't even touch it let alone make sense.
The same incredulity applies to each slide as well.
- for both death and cases, when referred to and graph plotted by day, is this the date the data was received and processed or the actual date that an individual passed away or their test came back positive? In the case of the latter, is accommodation made to compensate for the individual potentially having the virus onboard for up to 13 days prior to a positive test?
- we focus on those who are asymptomatic freaking out that everyone needs to stay home and not spread things around. What gets missed is that fact that asymptomatic also means they are not getting sick meaning they are a positive case but no illness, thereby lending strong credence to the point that this is not a horrible virus at all
- we are dealing with science here, not ideology, opinions, beliefs, politics…scientific principles apply
- testing, testing, testing…this is the key, period. Without accurate data, everything else is moot. BUT! Big caveat that falls firmly into the "it's not the technology that's the problem per se but rather how we put it to use" category. First we need as close to 100% confidence in the testing mechanisms as is scientifically possible. Secondly, these tests take genetic material from individuals; what is going to happen to that information post testing?
- this whole model thing seems a bit contrived: first it was not going to, then it was nah, don't wanna create panic (note the word used), then it was you deserve to know, to finally I wanted so share with you what I see as premier thereby bringing the viewer into the fold. Very calculated.
- ""Over 1,600 could be dead by the end of April, that is 50 a day, or that is two people every hour," Ford said." Similar tactic (the per hour or per day death rate) that the German govt group used.
- Sonia: https://www.youtube.com/watch?v=4BzJc1bqauo
- And here's Doug now bringing the fawning public into his circle by saying I want you to have the same access that I do as premier.
- Note the tone he is speaking in, very measured, very calculating, always maintaining that outward 'fatherly' appearance. Further, previous pressers have him not answering questions himself but getting others to…brings to mind 'busy work'. Might be pedantic but he doesn't hide his teleprompter reading very well either.
- Note the use of fear ALL based on estimates. Fear, fear, fear…what is a cost of a life, guilt guilt guilt. Obey Obey Obey…the data tells us, a fooking model. Prove it that what you've done has had the results you claim…you cannot.
- You have saved thousands of lives. [Help save the 1600 out there most of which will experience nothing worse than a (bad) cold.]
- Over dramatic [you should know being prone to it yourself]
- Ford: Lives are on the line…we must stay this course, we must! 583 inspections to shut down 5, WTF??
- Used the term war
- Forced to limit and close, not our fault, forced to, can't blame us for any fall-out, we were forced to take these measures, forced [bunk!]
- Refer again to model projections
- Refer again to model results of govt action
- This is disgusting to watch. To paraphrase Jean Luc Picard: "I feel I'm in the presence of a flimflam artist."
- Dougies eyes firmly on federal leadership?
IMHOThe bonus this time around is science is included in this one instead of just guessing motives and interpretations like politics is for example. 9/11 should have had the same benefit but the majority of the physical evidence that would have yielded clues was removed/recycled/destroyed.
The physical evidence, in this case mostly biological, is apparently widespread so doing the same to it would be an extremely challenging task. The more accurate data, the better the interpretations, the better detection of patterns, the better chance there is with arriving at the right course of action that has the best possible minimization of negative impacts for as widest range of people.
This of course does not negate the fact that there a many challenges that individuals or groups of humans have faced and will always face, it's the very nature of our existence.
No deep philosophical stuff here, again, simple reality: we exist in a harsh environment on our planet and so to does our planet within the solar system and so on. We do our best but there are no guarantees and no owezees; it is impossible to protect everyone from everything.
But with each of us exercising personal responsibility, looking after ourselves, and those we have agreed to look after, family for example, we have the best chance of coming through this, and any other issues when they are thrown our way in the future.
Group think , inspired by government, practiced by it and bank/corp, forced upon the rest of us, cannot work in a manner that offers the best we can get for the widest range of folks we can get it for…allowing individuals to make their own choices can…
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