26 karma · joined August 10, 2017
GrayScale = 0.299 * R + 0.587 * G + 0.114 * B
Whenever I need a color-space I make sure to use Grayscale as an axis.
This gives me an easy way to get "the same, but in green", and I can easily choose between background and foreground color.
I don't know if this has an official name, but for color-picking I wouldn't want to use anything else.
With these kind of bricks it would at least not be so difficult to motivate people to find appropriate storage facilities.
On the other hand closed groups of experts also don't work. I once knew some really extreme religious guys - pretty much flat-earthers. They were all heading for bioscience and networked like crazy - they got jobs in academia. Lonely brilliant scientists didn't.
Most likely a lot of science-communities have already been overrun by "networks".. Maybe the era of rational thinking is coming to an end.
Lets do some real mathematics.
1) Get good data (e.g. death by date).
2) Take a look at the Logarithm of the growthrate ln(f'/f)
3) Spot all those straight lines - those are the curves of outbreaks
4) Turn those lines into a working formula for an outbreak
5) Calculate predictions - evaluate trend-changes - look when new outbreaks happened.
It's just that simple. Results won't give you insight about the mainstream, but it will give some insights about reality.
Some countries are easier (Mexico, Germany, ...).. Some have more outbreaks and complications. But the basic principles of the mathematics of COVID-19 outbreaks around the globe are the same.
Calculating the new outbreaks of Sweden is more advanced because the start of the new curve is hidden behind another outbreak. But now there's at least enough visible to give good estimates.
Start is hidden under other smaller outbreaks, so it's not so easy to measure it exactly. But soon it should peak (if it hasn't already...).
If they don't do any nonsense that changes the mathematics of COVID-19 completely Sweden will be fine. The curve has normal size of a seasonal flu. Other countries in Europe are doing a lot worse.
And COVID-19 is strictly following Gompertz-Mathematics - just like a seasonal corona-virus. For any usual outbreak (f'/f) is falling exponentially all the time... There are no visible trend-changes around most policy-changes.
And there's no statistical epidemiological evidence that masks did anything good. In some countries with masks things got a lot worse. (more efficient catch+inhale? Or catch+transmit without being infected?)
Most of the hype around COVID-19 is just cheating by using "reporting date" instead of "date of death" to make Gompertz-Functions look like seriously dangerous exponentials...
If you have some datasets that allow to compare "reporting date" with "date of death" it is astonishing to see how many reporting-anomalies appear around policy-changes...
Usually if you look at death by "date of death" you see Gompertz-Functions all around. If you look at death by "reporting date" you often see a very uneven lag forming some exponentials...
People seem to always assume independent identical random variables. Corona Virus are seasonal - infections highly locally clustered.
The spreading might refuse to follow slow brownian motion of the herd. If e.g. an aircondition blasts an entire room of people with bad air, how much do herd effects really help?
Does the term "herd immunity" make sense for a seasonal virus at all?
GrayScale = 0.299 * R + 0.587 * G + 0.114 * B
Grayscale tells you if the color is for foreground/background - it's something like the weight the color has.
When I had to pick (too many) colors manually I finally automated color picking by using the following rules - and never had to worry about this subject any more.
A) When combining colors it's good to have matching Gray-Values. (Otherwise one will dominate - you can also use this to make one color stand out)
B) Gray value of background and foreground should differ a lot.
C) Colors of similar gray value should not be picked too close to each other (obviously...)
Not doing containment while it is growing will make the situation a lot worse.
Spreading the virus deliberately to get some immunity will make the situation worse.
Using non-functional lockdowns that only slow down the descend and spread the virus more evenly also make things worse.
Also the numbers of Covid-19 are mainly following a Gompertz model - like a seasonal virus that starts big and then fizzles. The high R-Values people talked about in the beginning aren't a real thing.
Michael Levitt in the beginning already calculated the outbreak in China perfectly. He made good Videos to explain the basic math. https://www.youtube.com/watch?v=hCgPf1SuPNY https://www.youtube.com/watch?v=Uw2ZTaiN97k https://www.youtube.com/watch?v=8aHrx68IT7o
Maybe take a look at some f'/f in a logchart... It's easy to do - just look at death per day/total.
Some countries are easy to model (e.g. Germany) - US is difficult with policies and outbreaks coming from different sides, ... - but some states are not that difficult to understand)
Last month around me it worked like this: Big spikes in case were caused by a test-campaign around somebody who died (or was ill). So a trend in case might in fact be an old trend of death - and the burst in case might in fact be asymptomatic or mild cases not leading to any death.
When you try to fit the spikes of death and case things are strange. In Germany maybe case and death have completely detached from each other.
Currently there are a lot of corrections going on. Bavaria had a deathcount of -3 on Saturday. Maybe they are piling up to report a big spike soon, or they stopped someone from reporting nonsense? We will see...
If you subtract the trend of the first outbreak from the main trend it's R²>0,995 from the beginning down the point where old people started to wear masks and go to church. Here things get worse - as expected.
New trend is still collapsing exponentially - numbers now are so low that the curve has reporting issues (national deathcount on Sunday was -1).
It's not really clear if actions and visible behaviour changes did much - all of them came way too late - and there is no sign of a trendbreak that indicates any improvement.
https://www.ecdc.europa.eu/en/publications-data/download-tod...
You need nothing but Excel/LibreOffice and a few simple formulas to get this picture.
Germany has a heavy 7 day rhythm, because they're doing their reporting in a very strange way. Using 7 day averages helps a lot to get rid of the reporting-noise.
You need: Daily death (averages over 7 days) Total death (Sum) (averages over 7 days)
daily/total
Just use SUM() and /
https://www.facebook.com/photo.php?fbid=10157283645163015
https://www.youtube.com/watch?v=8aHrx68IT7o
There is no constant CV>1 - it all depends on the situation.
In my area they made masks mandatory and cut down the protection of hospitals - now we have an infected nurse. I think politicians currently are using seriously wrong models for their actions.
I had a discussion with one of the "official" epidemiologists. It went like the discussion with you - just at the point where we are now he decided to go into all-caps swearing, said that he will never look into this, called his models perfect and then blocked me.
Good luck that most countries on the planet have already passed the inflection point, so finding the saturation point isn't that difficult anymore.
Interesting to see that just asking people to look into this will generate downvotes. It's just basic mathematics. And it really isn't that hard to verify...
You may see some wobble caused by outbreaks here and there, but the mechanics of COVID-19 are always the same.
Going to zero exponentially does mean it will converge mathematically. And t^-1 won't look like the things that can be observed. There is no such thing as a constant upward trend away from the exponential. If you look carefully and do some regressions you can see some ups and downs. Changes in the behaviour of the people are able to modify the trend, but there is no constant movement away from an exponential.
You can always add an upper exponential bound that will converge below single digits per day before there is any chance that the lines cross.
a) Take the number of deaths per day, divide it by the total number of deaths. f'/f.
b) Take a look at this in a log-chart. Use 7 day averages for f' and f to get a cleaner picture.
You will see that f'/f falls down exponentially since pretty much the beginning.
c) determine the exponent by estimating the derivative ln(f'/f)'.
d) Now use that to calculate the function f.
e) Calculate an enddate when it will fizzle.
With mask usage in Germany I see only negative trends in the growthrate of both case and death. I think to make them work something has to be changed.
There is a good chance that it now only works as a reverse placebo - it makes people feel confident and pushes them into more risky behaviour - most likely net negative.
After looking closely I found a small move into the wrong direction (maybe caused by something else?!??)