That was kind of the point of Bernie’s campaign.
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https://twitter.com/dhookstead/status/1306215784348180481
https://twitter.com/RealMNchiefsfan/status/1306261306672996353
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I wonder if some of these people get doped up on opioids to numb themselves from what they’re doing under Trump.
Or if all of them are complete sociopaths.
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There are a number of these places that were once hot, got less hot, and are now hot again. I’m not sure how much lower SDI is playing into what’s going on now, but in the county I’m going to describe in this post the SDI is comparable with the worst SDI there that led to the biggest spike there. The difference is that low SDI then was one week, this event lasted about three weeks.
Tarrant County, TX had TCU open up and they’re number 7 as of yesterday. First day of classes was August 17. From July 28-August 3, Tarrant County was number 14 in the country (around the peak of Texas). From September 1-September 7, they were number 25. In a shortened measurement period of September 8-12, they’re number 7.
Before what I say next, the lowest SDI Tarrant County ever hit during the pandemic was 28 the week of June 8-14. It happened for one week. They had a huge spike approximately 3 weeks later. They hit 28 SDI again August 3-9 and August 10-16 (probably when kids began arriving and doing things like orientation/finding housing). August 17-23 had 29 SDI (first week of classes). SDI has gone up significantly there since, though it looks like it will probably go back down to the mid-30s at best in this next measurement period.
Basically, we know anything below 30 is bad for Tarrant County. The below 30 started in a daily measurement on August 4, but went to 22 on Friday, August 7 (disaster). August 13 (24) and August 14 (22) also were high contributors. I’d be pretty surprised if most students hadn’t shown up in some fashion by those dates. Looking at the following week, it moves back up above 30, meaning everyone had seemingly settled in and classes had begun (though Wed-Fri were again bad with another 22 on August 21).
I think just based on the little I wrote out in that paragraph, that’s for certain a college effect. I’d imagine this pattern repeats just about everywhere. If you have other counties you want me to check, I’m fairly sure we can find many more of these effects.
Giant facemasks handed out to Greece’s schoolchildren
Face masks for all students and teachers was a laudable aim by Greece’s interior ministry, but it has turned into a oversized embarrassment after hundreds of thousands arrived in the wrong size.
A breakdown in communication over sizing meant that the first batch of face masks delivered for the start of school this week were, in the words of politicians, the size of “small parachutes”.
Greek social media has been flooded with photos of children sporting the giant facemasks.
The General Secretariat for Public Health has since admitted that the dimensions given had in fact referred to the size before the fabric was made into a mask.
G Stathopoulos, whose company made 500,000 of the oversized masks, told local media they had noticed the masks were too big before delivery: “We also commented on the size. In fact, samples were given from all suppliers. We were not asked to create, but to produce masks to specific dimensions."
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whooo boi that ratio on hookstead
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This will be Biden’s socialist hellscape America - the US will turn into Greece. No other examples can be used. Except Venezuela.
Guess my school ain’t that bad after all.
Though maybe yours is larger.
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I don’t get it. It looks like Hookstead is a RWNJ who writes for the Daily Caller? What does he have to do with Big Ten football, besides liking it?
Yeah. It is. By a few hundred students.
Crazy amount of people under quarantine for you though. We got 3 with confirmed covid (acquired from outside the school). Two teachers and an entire class of students are under quarantine. Two of my classes have half the students under quarantine.
It’s really only a matter of time before the entire school is under quarantine. I’ve already set up distance learning for them when that time comes.
I know that I wear my mask if I’m not socially distant from other people in school.
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being a grade a fuckhead
Since you seem to be trying to undermine whatever it is you think I was doing with the SDI project, I’m going to give you a little background on it so you can understand that this wasn’t ‘looking at graphs and making the data fit’.
SDI is a metric that had appearances of making a huge difference in the overall case numbers at the start of the pandemic. There was no data to support this, which meant events needed to happen to start compiling data in a useful way. This was a project I started at the beginning of April once I learned of the SDI metric and I didn’t release any findings until mid to late June just as things were heating up to the peak. Graphs weren’t released until the middle of July. It also didn’t help that this metric was always going to be around a week or so behind. It’s not meant to get in front of anything, it’s meant to suggest behavior that will lead to good outcomes.
If I remember correctly (pandemic time makes for time loss), I spent well over a month messaging Dan about this long before I returned to this site. I specifically asked him if it looked like there was something there. This isn’t nun ‘playing scientist’. I would share some findings and he thought there was maybe something there. We both knew much more data was needed to know anything but that it looked like a pattern was happening. As I’ve said a number of times in this thread, SDI is predictive of an entrance to a spike and an exit from a spike as long as testing data is reliable (it hasn’t been for well over 2 months in most places). It absolutely does not tell how you long a spike will last without major changes in SDI behavior, because there is no data we can go on (we’re in week 28 of the pandemic with only 27 SDI measurements available).
What we know is low SDI sucks and any places that didn’t adjust were hit. Any places that were hit hard early in the pandemic eventually adjusted their SDI. Within a period of time, cases went down with increased SDI. The higher the increase, the bigger the case fall. The original tracking of when things went ‘well’ was in mass shutdowns. Mask usage doesn’t show up there. We found out what it took to reduce spread in Part 1 (2 more weeks in a lot of places would have likely created a crushed curve, but we never got to find out due to Trump). In Part 2, we found out what the reduction in SDI was that led to increased spread. That was the secondary part of my process. Could I make a relatively accurate guess about when a place would get hot? In many places, I was right on the money and I hadn’t graphed anything. A number of places didn’t have anywhere near enough data to make a guess. Many of those places are now providing plenty of data to get an accurate target SDI score guess in Part 3.
When I started graphing the stuff a couple of weeks later, it was clear as day to me as to what the effects of high and low SDI were. I released my findings and you can say whatever you want about them. The main difference between Part 1 and Part 2 is that Part 1 had low mask usage, mass shutdowns, and high SDI at the beginning leading to lowering. For Part 2, SDI in general started at a higher baseline than the beginning of Part 1 and mask usage was high. When SDI fell from the peaks, cases started rising. The pattern repeated nearly everywhere and that was the ‘backing in’ of the target SDI guess. I even said it was backed in once I saw it presented visually. Nearly all of those case rises lined up with my non-graphed guesses, but it wasn’t until I graphed it that I saw doing it at the weekly level was the only potentially useful metric.
Again, SDI is solely ‘predictive’ of what it takes to reduce spread and what will lead to increased spread. It’s something states or counties could use to assess the level of shutdown needed in a place and shouldn’t be thought of as anything more than that. And as should be very obvious, all places are not even remotely considered equal in relation to SDI needs to reduce spread. This isn’t a one size fits all thing which is extremely clear when looking at the county level.
I don’t think there are any places that have low SDI, low mask usage, and somewhat adequate testing that have had case reduction. There also don’t appear to be any places that have high SDI, high mask usage, and adequate testing that have big spikes. I think high mask usage probably increases the SDI score (not a measured variable obviously) by anywhere from 5 to 10 points but there’s zero way to prove this.
My suggestion is to not try to make SDI into some big predictive factor that it’s not intending to be, which leads to your full sail dismissal of it for not doing what you want it to do or seemingly what you think I want it to do (I don’t even know what you want it to do). Dismiss it all you want, but it’s done a very good job of ‘predicting’ outcome in a place based on behavior. If you want cases to lower, have SDI above its target. If you don’t, be below it. I haven’t even been graphing that much of this stuff for the last month other than at the county level due to how busy I’ve been. It’s extremely difficult to get a handle on what target SDI is at that level because testing is screwed and reporting is all over the map. I’ll graph out the SDI for states today to see if I get any surprises.
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Now I’m waiting for some enterprising Northwestern law or pre-law student to sue over unequal access to the new fancy, daily testing that is being done for the football players.
Apparently the B1G claims their testing detects BELOW the infectivity threshold so a negative means NOT contagious.
Lots of scattered reporting on the latter. My first point is a guaranteed certainty that someone will sue.
When does he get his WH job. That guys twitter feed…
We’re really gonna kill off some elderly people just so we can have college football. WTF.
Over under of weeks of play before a B1G program decides it’s not worth it? Since they’ve gone this far, I’m gonna guess 4.