Sturgis motorcycle week draws thousands of bikers from all over the country. With the current pandemic , and the reopening of lodges, how safe do you feel if you have members attending this large gathering and coming to lodge ??
I hesitate to move too far from the purpose of the group, but two points:My personal opinion is this "pandemic" is a farce and a media caused distraction to the election this fall. Numbers are no where near the Bird flu we had years ago but the hype is over the top.
But like I said this is my opinion and the only place I wear a mask is in lodge due to the Grand Masters orders.
My personal opinion is this "pandemic" is a farce and a media caused distraction to the election this fall.
I agree that the stats and tests themselves are flawed.I would like to report on an interesting video that I saw yesterday.
The first fact that was presented is that current tests for covid give about 1% of false positives. Apparently that has to do with other bits of RNA being mistaken for covid RNA. I don't know the details of this phenomenon, but apparently the end result is that 1% of tests come out as positive even though those people are not infected.
The second fact was that in Germany at least the number of tests per week that are currently performed has recently more than doubled.
By combining those two facts together, we can deduce that if 100,000 tests a week are performed and 1,000 new cases are reported, then that number is within the margin of false positives, that is 1%.
Furthermore, since the number of tests that are performed in Germany now is more than twice the number of tests that were performed at the peak of the infection, the number of false positives has more than doubled.
The bottom line is that if a measurement error margin is bigger than the measurement itself, then that measurement is inconclusive. Using the example above, if in a certain region you perform 100,000 tests in a given week, and the number of false positives for that test kit is +1%, and you report 1,000 new cases, then the floor of the actual number of new cases is probably 1,000 - 1% * 100,000 = 0.
This extra level of analysis shows that the number of new cases that we are fed every day is meaningless unless we also know how many tests are performed every day with what test kit, and the margin of false positive for that test kit.
One thing for sure is that there are many more tests per week performed in Europe and the US during the last month than at the peak of the infection.
I'm not saying that covid doesn't exist, or it's all a big conspiracy. However the numbers that are presented on the news are incomplete to the point that they could depict a skewed scenario, since the number of daily tests is much higher than before, that is the number of false positives is much higher than before.
I agree that the stats and tests themselves are flawed.
But think on this, something which has been around for decades
"How accurate are home pregnancy tests?
Many home pregnancy tests claim to be 99 percent accurate. However, home pregnancy tests differ in the ability to diagnose pregnancy in women who have recently missed a period. If you have a negative test but think you might be pregnant, repeat the test one week after your missed period or talk to your health care provider."
So, it would actually be surprising to me that the false results are only 1% with a swab test. But even if you get generous and knock out 10%, the numbers are still high. I think as we progress in this, the numbers which will matter are how many are in ICU and if you can develop maths to work out, given incubation, and known cases in ICU, where we will be in 2-4 weeks, and if that's not the measure we before we take steps like shutting down businesses.. but that's why the reproduction number is so important. And the reality is, if more than 1, then COVID-19 cases will grow. And that growth is exponential. That's a key driver of many reactions to COVID, exponential growth.
(and oh man ! That search on the accuracy of pregnancy tests lead me down a rabbit hole to 20 minutes of reading lol... )
But here is another thing.. when I was right into reading all about COVID, I remember reading of a test group of pregnant women who were tested in New York, in the theory that they would be social distancing and taking special care. They were not swab tested, they were blood tested, and 15 % had had COVID. They had been asymptomatic. That's a big issue and consider further, I am a causal employee (or someone without sick leave) and I have a tickle in the throat. I have a choice, ignore it and keep working or get a test and it is positive, and I cannot earn an income to pay my bills and support my children ? What do I do ? I would say many would ignore it and just keep working and in doing so infecting others.
There are so many problems with COVID-19 stats, 1% error rate on testing would be the smallest factor in trying to analyze stats..
I was thinking Swine Flu....Not Bird.
Thanks - interesting comments. Discussion on this has almost become akin to the same passion of extreme ideologies, but really, what's at work is math and the mysteries of nature and science unfolding. People seem to forget that its the scientific method which needs to be applied, not spin... but on the other hand, the down side of not taking action like shutdowns, masks and social distancing is death, the down side of taking action is economic and psychological (which would also play in the first.).. and people will use spin and argument to move people to the path they want them to follow. But be that as it may or may not be, the real problem is fear of the unknown and the reality is we are working in a space unknown to the modern first world.You make quite a few good points.
I also worked with my own company and I understand the logic that if you don't show up you don't get paid. It's a big conundrum if you get sick.
Over here there seem to be quite a few people who just don't care of their own and other people's safety.
I would like to go back to the 1% false positives subject however and I'll try to make some example to show to what extent it matters.
I'll make 3 examples that contain yesterday's figures from worldometers.info/coronavirus and assume that the rate of false positives is 1%, although as you pointed out it may be higher than that.
tests = 609,917
new cases = 59,696
1% false positive = 6,099
=> In this case the number of false positive is only 10% of new cases
tests = 45,914
new cases = 953
1% false positive = 459
=> In this case the number of false positive is 48% of new cases, that is about half of them
tests = 51,095
new cases = 104
1% false positive = 511
=> In this case the number of false positive about 511% the number of new cases, that is 5 times larger. The margin of error is larger than the measurement.
This is what I meant by saying that perhaps we're getting a skewed view by only looking at the reported number of new cases without considering the total number of tests, the rate of false positives, and the consequent number of likely false positive.
Said that, keep safe and take care of yourself and your near and dear.