17 March at CoronaStavelot.com
E : World COVID19 data this day
(cet article, mais en français)
From Johns Hopkins CSSE
World Population (to allow calculation of world prevalence of COVID19) :
Raw and Derived Data :
Above Raw and Derived data, as PDF
Results:
The CoronaStavelot Index (CSI) increased from yesterday, 0.002048%, to today's value of 0.002388%. That's a 16.61% increase in prevalence around the world. Yesterday that increase was 8.30%.
The CSI represents the best estimate of Total Confirmed Cases of COVID19 (from Hopkins) on this date, at this time, ... divided by the best estimate of the Total World Population (Census bureau) at the same date and time.
One can express it as: "Total Confirmed Cases of COVID19, represent 2.34 onethousandths of 1% of the Total World Population." That's an estimate of prevalence of confirmed cases of coronavirus in the global population.
Trends #1
From Feb 27 to today, Mar 17
Trends #2
Trends #3
In the last week ...
Trends #4
Today ...
Results:
 (red bar): the number of people in the world Currently ill with COVID19, compared with this value on February 27, increased by 106.22%. Yesterday's value: 84.26%. (More people Actively Sick at present, when compared with 27 February, and more people sick than yesterday).
 (green bar): the Confirmed Cases of COVID19, compared with those of February 27, increased by 121.89%. Yesterday's value: 106.06%. This is the change in "world prevalence." (This is counting the cases, independent of current status or final outcome. i.e., it includes thos who have died and recovered from their illness).
 (blue bar): the Deaths due to COVID19, compared with those of February 27, increased by 15.05% Yesterday this figure was lower at 12.79%.
 (yellow bar): Total Recovery after COVID19, compared with those of February 27, increased by 8.764%, less than yesterday's value of 13.92%, and the value the day before, 18.125%.
Interpretation:
 COVID19 Confirmed Cases are increasing. At present, 5,276 new cases per day, up from 1,225 new cases per day on Feb 28, and more than the 4,846 new cases per day, yesterday. Note! This includes those who have recovered and those who died.
 Those who have Recovered continue to increase in number. (Today: 79,433, Yesterday : 77,257 Feb 27 : 32,897)
 Death rate worldwide is at 3.922% of confirmed cases, compared with that value on February 27 of 3.409%, and yesterday's value of 3.845%. That variable increased 2.00% since yesterday, and increased 12.79% since Feb 27.
 The number of people Actively Sick with COVID19 worldwide (95,820 today), increased 11.92% since yesterday, (85,617). Yesterday this increase was 11.78%, and the day before was 12.23%. These rates of increase are all pretty close, suggesting a stable rate of increase of the Actively Sick, worldwide. This figure Feb. 27 was 46,466. With 95,820 Actively Sick today, that's 106.22% above the value on Feb. 27, more than doubling.
Change in number of Active Cases
Interpretation
 The number of Actively Sick with COVID19 continues to increase worldwide.
 This value of Actively Sick has now more than doubled to 106% above that value on February 27.
 Number of actual Actively Sick calculated from the Johns Hopkins data this morning, is 95,820 worldwide. The equation predicts for this day: 96,115.
 This third order polynomial or quadratic relationship defines the data up to today quite well, (on target 99.3% of the time) with an error in this estimate of 0.308%.
 It suggests that if nothing changes in the variables that determine those Actively Sick worldwide, this value will be at 108,911 tomorrow.
 Yesterday, the % error was 0.816% below reprted value. Today, % error is +0.308% with predicted being slightly above the reported value. These are small differences in error of estimation. One would like to hope that since estimated is above actual, we will begin to see a trend downwards. Tomorrow will tell.
In PlainSpeak ...
At the present time, those Actively Sick with COVID19 worldwide continue to increase in number, (95,820) now 49,354 above the level observed February 27 (46,466) when we began to follow these events 19 days ago.
A Closer Look at 20 Neighbors
Prévalence (how much disease burden ?)
Presence of Illness Today (percent of confirmed cases, still actively ill).
Mortality (Death Rate on this day of the Confirmed Cases)
Recuperation (Those Confirmed Cases who have Recovered)
A link between Recovery and Amount of Testing in these 20 countries ?
"Hey, let's run some ZScores on those mortality numbers..."
"ZScores? What? Statistics?! You're gonna hit me with statistics on mortality?
Today ? Now ? When everyone is going crazy, including me ...
Now let me tell you something.
You're gonna have a mortality. Right here in front of you. Me.
My head is exploding already. Please don't do this!"

Let's start by placing before you again, that report of mortality by country for these 20 countries.
These values are current, on March 17, 2020 at 07:30AM.
 "Wow! Everybody's talking about Italy, but what about the Philippines ? Even worse !"
 "But the UK, Japan, Hungary and France are having problems too..."
 "I mean, what are they doing wrong in those countries ?"
 "And clearly, Russia, Israel, Finland and the Czech Republic are really doing the right things. We should be copying them in how they approach this whole thing."
 "Wow! Thanks! I can understand now. I can see who's to blame."
Are the differences suggested by the above graphic real ?
Does USA have a more worrisome Death Rate than Switzerland ? It looks to be about twice the Death Rate in Belgium ... Hmmm ...
"How would you know ?"
Here are the ZScores and some help to understand them:
For this sample of 20 countries, the average death rate is represented by a Mean of 1.866±2.324% of confirmed cases, died.
If that's the value for a normally distributed sample, how far away from that mean, is the value obtained in each country?
That's the ZScore. A country that is quite close to that average, might have a ZScore somewhere between 1 and +1. It's within 1 standard deviation below or above the mean.
And a Zscore value of 4, would place it 4 standard deviations above the mean. That would be quite rare.
One can also calculate the probability that a calculated ZScore arose by chance. Just for some weird reason or another. That's a p value. So for the USA, the chance that their ZScore of 0.01764 is actually some other number that places USA far from the mean, is 1  p = 0.986437, or 0.10563. There are 986 chances out of 1000, that USA Death rate is very close to this average.
Two countries below stand out.
Italy has a death rate of 7.713% this day.
That gets a ZScore of 2.516. The chance that Italy's death rate is actually just like that of all the other countries, is p = 0.011859. There are 11 chances out of 1000 that that result just happened by chance. The odds of that being the case, 7.43 to 1, against. Don't bet on it.
In the Philippines this day,
and talked about much less in the News, the death rate from COVID19 is 8.451% of confirmed cases. ZScore = 2.83391. Again, this explains that their value is almost 3 standard deviations above the mean. Pretty rare, but here, reality. And the p value = 0.004598. About 5 chances out of 1000 that their result happened by chance. Odds against that ? 216.49 : 1, against. Really, don't bet on that one either, that this was all just some kind of a mistake, in the Philipines.
Of course the comparisons were made useing the mean value for all of these 20 countries.
Let's exclude Italy and Philippines and generate an average value for the 18 remaining.
That Mean is 1.191 ± 1.132
So now comparative values for these two outlier countries become:
Italy ZScore = 5.76148 p < 0.00001 (that's 1 chance in 100,000 that it all just happened by chance).
Philippines ZScore = 6.41343 p < 0.00001 (same conclusion as for Italy. Even more 0's could be added to that p value for the Philippines).
So being 6.4 standard deviations away from those playing the game, means that you are not even in the same ball park. Not even in the same city or county.
Something different is going on. But what?
"So who, in Italy and the Philippines, is to blame ?"
People quickly get into that blame mindset ...
A better approach, less aimed at finding some one irresponsible nerd and firing his ass ...
Is to think about what differences could explain this.
For instance:
 Is the basic level of health different to begin with, before COVID19 arrived in Italy and the Philippines, from that in all the other countries?
 Did people in these countries take the news of COVID19 less seriously, and just keep grouping together for some Chianti or Samalamig ?
 Is the distribution of ages different from one country to the next in our sample?
"Gee, I don't know. Let's have a look."
Well, it looks like Italy is number 5 in world rank for age: Median 45.5 years old.
And the Philippines is number 169. A bunch of young guys at 23.5 years, median. Just the opposite!
Is that different than the average age of the 18 remaining countries? (see at the bottom of the following table).
Italy, is about 1 standard deviation above the mean of the other 18.
But the Philipines is almost 5 standard deviations below the Mean value for median age. Whew ! Not even close to having the same age. Much younger.
"How does that fit into the Philippines having the most deaths from COVID19?"
I don't know. But someone in the Philippines and at W.H.O. should be onto that.
Look at this ...
One of the deaths was a man from Wuhan who travelled to the Philippines. The first death, early on.
But the others are all well above that median age of 23.5 yers in the Philippines.
Because of its young median age, is care of the elderly in the Philippines somehow different?
Perhaps less recent experience in hospitals, with care of severe respiratory cases among the elderly?
Are societal approaches to the elderly different in this country?
It needs to be pursued in that country.
It doesn't mean that your country or mine, will likely reach the same levels of mortality, as shown in that bar chart above. Rather than quietly waiting for death rates to escalate to these levels in your country. Tell yourself that they probably never will.
In fact, it probably means just the opposite of what the graphic representation of these rare events suggests.
It is logical that the same virus, injected or infected into a country, would have different outcomes that are highly determined by differences in psychsocial factors.
I'm sorry if I gave you a headache or made you fidget.
The goal was to shed light, as well as heat. The media today tend to do the opposite.
The Moto for Today ...
 It's threefold :
 "Test ! Test ! Test !"
 Preparedness
 Hunker down (social distancing)
Time to start your list of the positives arising from all of this. Here are my first entries :

 Less debate and argument; more Solidarity.
 Reduction in CO2 and greenhouse gases.
 Decide to just get up and go nowhere.
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