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OBSOLETE COVID THREAD Maths and Covid 19

OBSOLETE COVID THREAD
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If I look at
I see that China has stopped the exponential growth of the cases. They try to start their economy again... and I hope very much that they can do it without starting the exponential growth of the cases again. If they can do it and the numbers are true it can be done.
The strict measures against corona started in Europe about a week or half a week ago. So it will take maybe one or two weeks until we hopefully see the first results.

I think it is too early that you can say anything. I can only wait and see until the governments and WHO (World Health Organization) say we can travel again... but for me there is still the hope for a camino in 2020.
 
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If I look at
I see that China has stopped the exponential growth of the cases. They try to start their economy again... and I hope very much that they can do it without starting the exponential growth of the cases again. If they can do it and the numbers are true it can be done.
The strict measures against corona started in Europe about a week or half a week ago. So it will take maybe one or two weeks until we hopefully see the first results.

I think it is too early that you can say anything. I can only wait and see until the governments and WHO (World Health Organization) say we can travel again... but for me there is still the hope for a camino in 2020.


Seems to me that China has their reduction because they have the ability to complete shut down and isolate cities of fifteen million people .. I just cannot see that sort of thing happening in the western democracies .. in my country (UK) we are now forecasting an absolute minimum over the next few months of 20,000 deaths and our worst case is 260,000 deaths ..... sure, if we could enforce martial law and totally quarantine tens of millions of people we could control it, but we cannot .. what is coming is coming - God help us all.
 
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No, not bell curve, as is inverted curve, therefore exponential.

sigh ... This is my positively LAST post on this ...

Curves can only be exponential in an infinite environment ; but the number of human beings is finite. Even assuming a perfect 100% incidence of contagion of person to person, the curve would be at very worst logarithmic, not exponential.

You just can't see a bell curve from pure numbers until after it has reached peak and started to descend. As this one will, as there is not a 100% mortality rate either.

Yes I am being pedantic, but this one does seem to annoy me more than usual sorry ... o_O 💬
 
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sigh ... This is my positively LAST post on this ...

Curves can only be exponential in an infinite environment ; but the number of human beings is finite. Even assuming a perfect 100% incidence of contagion of person to person, the curve would be at very worst logarithmic, not exponential.

You just can't see a bell curve from pure numbers until after it has reached peak and started to descend. As this one will, as there is not a 100% mortality rate either.

Yes I am being pedantic, but this one does seem to annoy me more than usual sorry ... o_O 💬

Exponentials can be achieved until the end of the series, they do not need to be infinite, especially in finite environments. Until it descends/reduces it is not a bell curve - it may be seen as a bell curve after we have data from the end of it - if there is an end to it.

Bell curve -

OIP.jpg

Current Exponential (not logarithmic) Coronavirus curve -


coronavirus-chart.png
 
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No, not bell curve, as is inverted curve, therefore exponential.

However, presumably the pool of susceptible people in the populace will progressively reduce and the curve will then flatten out and the begin falling...? somewhat akin to the traditional bell curve?

Edit: I got it wrong - I should have said the logistic curve which flattens out at the top (not like the bell curve which trends downward after reaching to top point)
 
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Given the high rate of growth, it's much better to plot the number of cases with a log scale - this also allows you to compare the resulting slopes for different countries
 
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Given the high rate of growth, it's much better to plot the number of cases with a log scale - this also allows you to compare the resulting slopes for different countries

This would be far less annoying.
 
Here's the bell curve :

coronavirus-5.jpg
 
These are the numbers of https://www.worldometers.info/coronavirus/ ...
I have taken the numbers one time a day... so sometimes there may be an update afterwards so that the numbers are not always the final numbers per day.

I calculated the growth in percent (growth/total cases). I think it it very promising that the growth rate in Italy (the worst non-China country) seems to be declining. If it would be exponential growth it would stay at e.g. 20%.
But there are random day to day changes as well... so it is too early to conclude very much.
growth_rates_corona_200318.png
 
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Are the cases less "active" for a particular reason?

Always worry about statistics and their interpretation
 
From yesterday's Guardian: I'm an ER doctor. Please take coronavirus seriously. Most people don’t understand exponential growth. The article isn't about math(s) as such but it has a graph of confirmed Covid-19 cases for a number of countries from the day they had reached 100 confirmed cases in each country.

The scale is logarithmic, i.e. the axis on the left, the one that goes up, is not divided into segments of 1,000, 2,000, 3,000 but segments of 1,000, 10,000, 100,000. The coloured graphs for Italy, Spain, UK, and the US look very much like straight lines going upwards. This is exponential growth in the mathematical sense. The lines are "above" the 6-day doubling trajectory. This means that confirmed cases currently double in less than 6 days. The measures taken by regional or national governments aim to break or bend these lines. That's what we all have to achieve by doing as we are told.

And time is of the essence. Or, as the writer, an emergency medicine resident physician at Massachusetts General Hospital in Boston, puts it: Our inability to appreciate how extraordinarily powerful exponential growth can be has concrete consequences. [...] It’s also why people seem to be struggling to understand why every single day matters enormously in limiting the spread of the coronavirus, which follows an exponential growth pattern.

ER Guardian.jpg
 
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And just a clarification - @Kathar1na please correct me if I'm wrong - about the business of an exponential versus a normal ('bell' ) curve:
A normal curve describes a probability distribution, not a time sequence - which is what an exponential curve describes.

So when an exponential curve reaches its asymptote, and maybe declines after that, the shape may mimic a nirmal curve, but what it describes is something else altogether.
 
@VNwalking, I'm going to sidestep your question and will leave answers to others 😇. We don't know how long all this will last and how the curves that describe aspects of the coronavirus spread will look like in the end and which functions from the rich treasure chest of mathematical tools will describe sections of it in the best way. In the real world, epidemics come to an end eventually, for a variety of reasons.

One thing is certain: Many countries have taken the decision that they must not let this epidemic run its natural course.
 
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It's never been exponential ; it's following a typical bell curve.
No, not bell curve, as is inverted curve, therefore exponential.
sigh ... This is my positively LAST post on this ...

Curves can only be exponential in an infinite environment ; but the number of human beings is finite. Even assuming a perfect 100% incidence of contagion of person to person, the curve would be at very worst logarithmic, not exponential.

You just can't see a bell curve from pure numbers until after it has reached peak and started to descend. As this one will, as there is not a 100% mortality rate either.
Exponentials can be achieved until the end of the series, they do not need to be infinite, especially in finite environments. Until it descends/reduces it is not a bell curve - it may be seen as a bell curve after we have data from the end of it - if there is an end to it.

Lordy...now I remember why I ditched maths. My non-scientific, humanities-slanted brain is positively aching. 🤔 😓
Appreciate the intellect of your discussion though, even if it reads like a foreign language to me. Is there a '.... for Dummies' book on this stuff?! 😄
👣 🌏
 
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Is there a '.... for Dummies' book on this stuff?!

Here is a pretty simple explanation of exponential growth from study.com. My favourite example is this:
When graphed, exponential growth always looks like it is starting off slowly and then rapidly becomes steeper. It's a lot like spreading gossip about your ex: you might only tell your two best friends that he cries during chick flicks, but each of them tells a couple others, and pretty soon there is no one in the Western Hemisphere that doesn't know his secret - thanks to the power of exponential growth.
 
And just a clarification - @Kathar1na please correct me if I'm wrong - about the business of an exponential versus a normal ('bell' ) curve:
A normal curve describes a probability distribution, not a time sequence - which is what an exponential curve describes.

So when an exponential curve reaches its asymptote, and maybe declines after that, the shape may mimic a nirmal curve, but what it describes is something else altogether.
The bell curve is not appropriate

Instead what we are plotting in a time-series is say exp(k*time) with k being the rate of growth
If we take the log of the formula (just k*time), we get a chart that increases linearly with a slope of k (say k=0.2 or 20%) - so log-linear and exponential growth are two sides of the same coin
That's why I suggested such exponential growth should be plotted on a log scale so that we can more easily see the daily percentage growth in cases in a linear form which makes comparisons between countries much easier
 
Can someone please help me understand something? It drive me nuts that I don't get it.

It's around 2:30 into an excellent video clip that was posted on the forum some time ago. The guy says:

It lets as be able to be a little more quantitative about how exactly close the exponential fit really is, and to use the technical statistical ..... here, the answer is that it is really freaking close.
What does he say after statistical?
 
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The bell curve is not appropriate

It is if you're considering active cases rather than number of cases -- but if it's number of cases, then it's a log curve.
 
...
It's around 2:30 into an excellent video clip that was posted on the forum some time ago. The guy says:

It lets as be able to be a little more quantitative about how exactly close the exponential fit really is, and to use the technical statistical ..... here, the answer is that it is really freaking close.
What does he say after statistical?
Between 2:30 and 2:40 he says R^2 is 0,975 which is very close to 1,000 ( = perfect model) which means the development of the real cases are very close to the numbers estimated by this model.

But this is not very important for the rest of the video.
 
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New York Times has chart ( from around 19 March) comparing the curve to a logarithmic scale and it shows a sharp rise in US cases versus current Italian cases. My 2 cents.
 
For a vivid illustration of the effect of social distancing check out this site:

 
From yesterday's Guardian: I'm an ER doctor. Please take coronavirus seriously. Most people don’t understand exponential growth. The article isn't about math(s) as such but it has a graph of confirmed Covid-19 cases for a number of countries from the day they had reached 100 confirmed cases in each country.

The scale is logarithmic, i.e. the axis on the left, the one that goes up, is not divided into segments of 1,000, 2,000, 3,000 but segments of 1,000, 10,000, 100,000. The coloured graphs for Italy, Spain, UK, and the US look very much like straight lines going upwards. This is exponential growth in the mathematical sense. The lines are "above" the 6-day doubling trajectory. This means that confirmed cases currently double in less than 6 days. The measures taken by regional or national governments aim to break or bend these lines. That's what we all have to achieve by doing as we are told.

And time is of the essence. Or, as the writer, an emergency medicine resident physician at Massachusetts General Hospital in Boston, puts it: Our inability to appreciate how extraordinarily powerful exponential growth can be has concrete consequences. [...] It’s also why people seem to be struggling to understand why every single day matters enormously in limiting the spread of the coronavirus, which follows an exponential growth pattern.

View attachment 71455

This plot intrigues me.... The graphic shows the trajectory for doubling on infections every 6 days (doted grey line) but what interesting is the nesting of infection curves in the 'doubling every 2 to three days' zone to the left of the '6 day' line.

This rather suggest to me that the initial response of just about every country, whatever strategy they have adopted has been quite ineffectual - the three unlabelled grey lines excepted which have shown an early deviation from the general pattern. From other sources, I think these three are (from the bottom) Singapore, Japan and South Korea.

In Australia , we have moved from the initial 'exclude foreigners who may have been in contact with infected people' to a self-isolating and 'social distancing' approach. If my interpretation of the graphic is correct, this would seem to have been a case of "too little, too late"...
 
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Here is a point that affects the current statistics.

The divergence between the % and numbers of Covid19 deaths in Germany and Italy is that those who pass away in Germany from one condition who also happen to be positive for the virus are recorded as having died from that other condition ; whereas in Italy they are recorded as Covid19 deaths.
 
This plot intrigues me.... [...] If my interpretation of the graphic is correct, this would seem to have been a case of "too little, too late"...
It is my understanding that it takes at least 7-10 days before the extreme measures ("lockdown") will show any effect in these graphs. I think the main point of this graph is the fact that development is so similar in all these countries, ie the fast exponential growth in the beginning phase and that this growth is typical for this kind of infectious diseases, once started, there is an automatism that is hard to grasp for many laypersons.

Also note that the x axis is "number of days since 100 confirmed cases", so all graphs start at the same point. Countries didn't reach their first 100 confirmed cases on the same day. For example, Italy reached it around 4 March, Spain around 12 March, the UK around 18 March and Germany around 22 March. This last sentence wasn't correct; these are the dates when the number of deaths amounted to 100 for the first time. But the principle is the same for confirmed infections.

The absolute numbers of confirmed cases is not directly comparable between countries because of differences of who is tested. Also, these countries did not implement extreme measures ("lockdown") at the same moment as everyone else nor at the same moment of progress of the disease in their own country.
 
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the initial response of just about every country, whatever strategy they have adopted has been quite ineffectual
In terms of preventing widespread infection, perhaps. From the very beginning

This was an eye-opener about that:
If my interpretation of the graphic is correct, this would seem to have been a case of "too little, too late"...
There's a difference between dissemination and numbers infected in any given area. It's everywhere right now. That's water under the bridge. As I understand it, the task now is to minimize numbers so as not to overwhelm hospitals with the masses of cases that could be coming. And I hope you're wrong. Because, yes, it may be to late for even that.
 
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For example, Italy reached this number [100 confirmed infected cases] around 23 February, Spain around 2 March, the UK around 5 March and Germany around 1 March.
The German RKI, the leading infectious diseases institute in that country, gets often asked by reporters why in particular their number of fatalities is so strikingly low. From their faces, I don't see that they rejoice in that fact. They said yesterday that everyone who has a confirmed coronavirus infection, is ill and dies, is counted as a coronavirus death. They themselves don't really know why the number is currently so low but have a number of ideas - the current age profile of those infected is quite different in Germany than in Italy for example - patients are noticeably younger on average - while the percentages of old and very old people in the population are quite similar in Germany and Italy. Perhaps because many German Covid-19 patients had come back from skiing holidays initially and got infected there.

Another reason may be their policy of early comprehensive testing. It allowed them to trace all contacts and break the transmission chains ... it doesn't matter much now. The whole country (ie every one of their 15 regions/provinces) is in lockdown, similar to Spain etc etc.

In real life, the main point is the number of severely ill people in a hospital and whether that hospital and their staff can cope with it. The main point of the data is to steer and modify the measures to control the spread of the disease.

Edited on 25 March to correct quote.
 
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View attachment 71455
[/QUOTE]

I think it is more relevant to look at rates per population than cumulative numbers. This tells you more about the impact (And data on number of tests, too, if we had them and how reporting is done). Italy has 69,176 cases. Spain has 42,058. But if you compare the numbers per population of each country, the rates are very close to the same.
 
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The focus is on reducing the risk of failure through being well prepared. 2nd ed.
I think it is more relevant to look at rates per population than cumulative numbers. This tells you more about the impact (And data on number of tests, too, if we had them and how reporting is done). Italy has 69,176 cases. Spain has 42,058. But if you compare the numbers per population of each country, the rates are very close to the same.
Despite the fact that these absolute numbers (cumulative confirmed cases and cumulative deaths) are not directly comparable between countries because of different size in populations, different approaches of who gets tested and where this approach even changes over time, etc, they do illustrate the exponential growth during the initial stage. I've seen graphs in log scale for confirmed cases per population and also of deaths per population of 65+ years olds, and it is always a straight line that does not stop going up at the moment for European countries and for the USA (as a whole and the states most concerned right now).

It obviously goes without saying that the real models that are currently used to determine measures are a lot more sophisticated.

PS: Sorry if my language is often imprecise, I'm not used to writing about this stuff in English.
 
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In Germany if you recover there is no law that you have to report this.
Post Mortem test are not be done on Corona activity Only if their is a suspicious circumstance. But mortality rate is determined of a confirmed Corona virus patient. The labs are overworked anyway.
And these numbers only represent the testet and positive populous.
It does not tell us how many people already had it or have it.
So our stats are flawed to the point of not realy giving us much to go by. Unless to show people the necessity of distancing and rudimentary hygiene like hand washing.
Why , because we do not have anything yet to fight this disease with.
Do you realy trust the Chinese or the Russian numbers.?
 
If anyone is really interested in more details about this I recommend that you call up the Twitter feed of the Robert Koch Institut. They livestream their press conference every Monday, Wednesday, Friday at 10 am. Similar for Spain, look for the Twitter feed of SaludPublicaEs. You don't need to log into Twitter.
 
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I'd love to go take a wander on the beach next to my building but there are hordes of mindless fools here in Florida for spring break. You simply have to start believing.View attachment 71067
I have barely glanced at more than two replies. You end your post by saying you simply have to start believing. I thank God for my poor little mind that resists any attempts to be taught about maths and graphs and stuff. I agree. We just have got to start believing. and keep our distance. And carry tissues. And clean our windows. Again and again and again. Please forgive me for introducing this, but it is not meant badly: The best book on maths I ever read (and I had to teach maths for years as a primary school teacher!) was entitled: Mister God This is Anna. I can recommend it. I lost my copy years ago, having lent it out and it was not returned. many people are quoting Juliana of Norwich at the moment. I quote an old nun, long since gone from this earth: When all is said and done, there is more said than done. Please, everyone, let today grow in exponential amounts of deep hope and belief in tomorrow and future. It's up to you.
 
There is a great deal of statistical data and discussion at this website :


And as I look at the chart of the total number of cases worldwide, logarithmic scale version, the curve seems to be broadly divided into three sections, each representing about 3 weeks in length.

An initial 3 weeks of sharp increase ; then 3 weeks of relatively low increase, then the past three weeks sharp again.

It is possible from that to speculate that, perhaps, it takes about 3 weeks for a group of infected patients to start showing signs of recovery ; and given that the Italian epidemic began in earnest just over three weeks ago -- http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1 -- it is quite possible that the lessening in the number of new cases over the past 3 days is the start of an actual downturn.

Now that doesn't mean that it might be "over" in three weeks, as the number of new daily cases is still quite high, but if that downturn is confirmed over the rest of the week, then it may be the case that this epidemic is one that can be measured as lasting weeks rather than months.

Does anyone spot any flaws in that reasoning ?
 
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es anyone spot any flaws in that reasoning ?
Once people emerge from seclusion it's likely that there will be another spike in new infections. As well as perhaps a summer lull followed by an autumn resurgence. In the 1918 epidemic, the mortality rate in the second wave exceeded that of the first.

So I'd love for your analysis to be right, and what's happening now in China will be proof of the pudding. But historical precedent does not bode well.
 
Once people emerge from seclusion it's likely that there will be another spike in new infections. As well as perhaps a summer lull followed by an autumn resurgence. In the 1918 epidemic, the mortality rate in the second wave exceeded that of the first.

So I'd love for your analysis to be right, and what's happening now in China will be proof of the pudding. But historical precedent does not bode well.
This would be a catastrophe for Spain and many more countries.
I think is better not to write so bad predictions to live today with more morale.
 
Once people emerge from seclusion it's likely that there will be another spike in new infections. As well as perhaps a summer lull followed by an autumn resurgence. In the 1918 epidemic, the mortality rate in the second wave exceeded that of the first.

So I'd love for your analysis to be right, and what's happening now in China will be proof of the pudding. But historical precedent does not bode well.

Whilst I agree with that in principle, the major difference between 1918-1919 and 2019-2020 is that we understand far better how to treat and indeed create vaccines for viruses in the flu family, and apart from that, also how to create effective contamination test kits to help better contain infection zones and help individual infected patients, so that a second outbreak next autumn/winter may be less likely than it would be without these provisions.

I fully agree with your point about China.

That the Covid19 is significantly less virulent than the SARS and MERS is a huge relief, else this would easily be as bad as the black death and a lot worse than the Spanish Flu.

But we're drifting a bit off-topic again -- I was really asking if there were any flaws in the analysis that I made of the statistics ... 😷
 
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I was really asking if there were any flaws in the analysis that I made of the statistics

I see no point in this kind of analysis speculation. I am sure that there are hordes of very qualified epidemiologists and statisticians worldwide who are scrutinizing the numbers; if any real significant trends were spotted, I guess we would have been informed. Anyway, no statistics can be better than the underlying data and the underlying data are (still) very uncertain alone from the fact, that different countries have adopted different strategies for testing for Corona infection. How the curves are going to develop in the future will also be affected by the likely very large ‘dark number’ of undetected cases and by the different methods used to try to contain and limit the spread of the disease.
 
if any real significant trends were spotted, I guess we would have been informed.

Well, the French Scientific Council has advised the President that they think the lockdown in France should last 6 weeks -- so I did not base my speculation on nothing

But I do very much appreciate your excellent caveat !!
 
...
And as I look at the chart of the total number of cases worldwide, logarithmic scale version, the curve seems to be broadly divided into three sections, each representing about 3 weeks in length.

An initial 3 weeks of sharp increase ; then 3 weeks of relatively low increase, then the past three weeks sharp again.
...
The initial 3 weeks of sharp increase was the "exponential" growth in China. The low increase was the phase when China had "controlled" their corona. And the next sharp phase was the "exponential" growth in "the rest of the world".

(my calculations with numbers mostly of worldometers )
1585167050511.png
The growth in percent decreases in Germany and Italy. It looks a little bit like linear growth there in the last days... so we should be somewhere in the middle of a logistic curve (simplified).

So, yes, I think the situation will be rather stable in Germany and Italy in about 3 weeks (simplified)... if the measures against corona will continue. And Spain will be there a little bit later.

The difficult part will be to return to "normal life" and "Camino life" without getting the next growth phase of corona. But if I look at the Chinese numbers I think there is hope. (without having a timeframe when we will be there)
 
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No, not bell curve, as is inverted curve, therefore exponential.

Given the plot is of cumulative number of cases, it can never trend down as a bell curve would - rather it follows the general form of a logistic curve... As the virus infects an increasing proportion of the population (which develops some immunity?), the daily increment of infected people must become smaller as the plot approaches 100% of the population are infected.

1585172691684.png

What interests me about the plot is the broad similarity of the slope of the plots for the various countries - number of infections doubling between two and three days - despite the varying national approaches to exclusion/containment of the virus - and the divergence of Singapore, South Korea and Japan from the general trend.
 
Given the plot is of cumulative number of cases, it can never trend down as a bell curve would

No, but if you look at the active cases numbers instead, it can and it will.
 
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The French curve has flattened, though that may be because people with mild or no symptoms are no longer being tested -- still, it's encouraging.
 
The French curve has flattened, though that may be because people with mild or no symptoms are no longer being tested -- still, it's encouraging.
No, but if you look at the active cases numbers instead, it can and it will.

Agreed - but the plot under discussion is of cumulative numbers of cases against number of days since 100 cases....
 
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If you are plotting cumulative cases, it is IMPOSSIBLE to have a bell curve. If you see a bell curve, you are recording incident cases, not prevalent.

That has already been established several times in the thread.

Agreed - but the plot under discussion is of cumulative numbers of cases against number of days since 100 cases....

What's under discussion is mathematical representation of the outbreak, and that is not limited to one single type of representation thereof.
 
About the terminology: While I know next to nothing about this, it would probably be more accurate to speak of a bell shaped curve or bell shaped function than a bell curve since many people - I certainly did and so did others - associate the latter with probability theory and normal distribution.

A collection of the graphs of bell shaped functions from the Wikipedia article:

Blle shaped.jpg

They are symmetrical.
 
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Given the plot is of cumulative number of cases, it can never trend down as a bell curve would - rather it follows the general form of a logistic curve...
Ooooh, I am feeling a bit excited right now so I hope the moderators will forgive this excursion that goes a bit deeper into mathematics - after all, this IS the math(s)/coronavirus thread. It appears that the derivative of a logistic curve is a bell shaped curve. We know already the meaning of the former in the context of this discussion but the latter has apparently also a specific meaning in this context. I'm just paraphrasing what I read on the internet, it goes a bit above what I can do on the back of an old envelope myself right now. I quickly googled for an image as an example. Logistic curve in blue, corresponding derivative in red. How cool is that?!

derivative.jpg
 
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We know already the meaning of a logistic curve in the context of this discussion but its derivative has a meaning in this context, too
Here's an article, there must be many more: Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world. Quote: The logistic growth model can capture most of the dynamics at the cumulative level, daily increase level (1st derivative) and the daily growth rate level (2nd derivative). The paper has been published on arxiv.org, an open access archive hosted by Cornell University.

You don't need to read the whole text, there are plenty of graphs. Note that all these graphs don't predict anything for sure. They are adapted from day to day according to the available data to make the curve fit. The data produce the curve; the curve doesn't produce the data.

Moderators: I don't mind in the least if you feel it is time to close this thread. But I rather fill my time in solitary confinement (I am already on day 13!!!) with the topic of this thread than with cleaning my windows. ☺
 
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Here's an article, there must be many more: Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world. Quote: The logistic growth model can capture most of the dynamics at the cumulative level, daily increase level (1st derivative) and the daily growth rate level (2nd derivative). The paper has been published on arxiv.org, an open access archive hosted by Cornell University.

You don't need to read the whole text, there are plenty of graphs. Note that all these graphs don't predict anything for sure. They are adapted from day to day according to the available data to make the curve fit. The data produce the curve; the curve doesn't produce the data.

Moderators: I don't mind in the least if you feel it is time to close this thread. But I rather fill my time in solitary confinement (I am already on day 13!!!) with the topic of this thread than with cleaning my windows. ☺
The "bell curve" in this paper:
1585259300498.png
plots daily increases, not cumulative infections. One would expect daily infections to begin decreasing when the pool of susceptible people starts to shrink and the virus has difficulty in "finding" a susceptible person...
 
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Moderators: I don't mind in the least if you feel it is time to close this thread. But I rather fill my time in solitary confinement (I am already on day 13!!!) with the topic of this thread than with cleaning my windows. ☺
I think that it was a brilliant idea for this thread to be started - not that I am reading it carefully! :) Enjoy!
 
the derivative of a logistic curve is a bell shaped curve.
Voila!
Wait. So (rusty neurons firing here), when you plot the area under the logistic curve you get a bell-shaped function, right?

Which makes sense...since the rate of growth at the beginning and ends of the logistic curve are the same, but it's much higher in the middle.

(Sometimes maths are very satisfying in a chaotic world. ☺
@Kathar1ina, please - the windows can definitely wait.)
 
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The log curve for Italy is clearly tending towards flattening :


And while the daily number of new cases is still very high, and close to the highest it's ever been, in 5 of the last 6 days the number has been lower than the previous day.

Statistically, this looks like Italy has hit the peak of its epidemic.
 
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The absolute growth of the new cases is falling now in Spain and Italy. So hopefully the worst is soon behind us:
1585600244591.png
 
My aging math brain (last diffEq in 1966) says to beware of interpretations that new cases falling means the worst is past. It most likely means we have successfully flattened the curve. But the area under the peak curve and area under the flattened curve are roughly the same; so we should continue to have steady new cases for a time long enough to get to whatever the upper boundary is (herd immunity?? ) But flattening the curve will likely allow a lower death rate as resources for beds, oxygen, respirators, medical staff will be less stressed. All a good thing but does not allow me to get enthusiastic about my possible fall camino.
 
From the London Spectator (written by Dr John Lee a professional pathologist) :

The data on Covid-19 differs wildly from country to country. Look at the figures for Italy and Germany. At the time of writing, Italy has 69,176 recorded cases and 6,820 deaths, a rate of 9.9 per cent. Germany has 32,986 cases and 157 deaths, a rate of 0.5 per cent. Do we think that the strain of virus is so different in these nearby countries as to virtually represent different diseases? Or that the populations are so different in their susceptibility to the virus that the death rate can vary more than twentyfold? If not, we ought to suspect systematic error, that the Covid-19 data we are seeing from different countries is not directly comparable.

Look at other rates: Spain 7.1 per cent, US 1.3 per cent, Switzerland 1.3 per cent, France 4.3 per cent, South Korea 1.3 per cent, Iran 7.8 per cent. We may very well be comparing apples with oranges. Recording cases where there was a positive test for the virus is a very different thing to recording the virus as the main cause of death.

Early evidence from Iceland, a country with a very strong organisation for wide testing within the population, suggests that as many as 50 per cent of infections are almost completely asymptomatic. Most of the rest are relatively minor. In fact, Iceland’s figures, 648 cases and two attributed deaths, give a death rate of 0.3 per cent. As population testing becomes more widespread elsewhere in the world, we will find a greater and greater proportion of cases where infections have already occurred and caused only mild effects. In fact, as time goes on, this will become generally truer too, because most infections tend to decrease in virulence as an epidemic progresses.
 
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From the London Spectator (written by Dr John Lee a professional pathologist) :

The data on Covid-19 differs wildly from country to country. Look at the figures for Italy and Germany. At the time of writing, Italy has 69,176 recorded cases and 6,820 deaths, a rate of 9.9 per cent. Germany has 32,986 cases and 157 deaths, a rate of 0.5 per cent. Do we think that the strain of virus is so different in these nearby countries as to virtually represent different diseases? Or that the populations are so different in their susceptibility to the virus that the death rate can vary more than twentyfold? If not, we ought to suspect systematic error, that the Covid-19 data we are seeing from different countries is not directly comparable.

Look at other rates: Spain 7.1 per cent, US 1.3 per cent, Switzerland 1.3 per cent, France 4.3 per cent, South Korea 1.3 per cent, Iran 7.8 per cent. We may very well be comparing apples with oranges. Recording cases where there was a positive test for the virus is a very different thing to recording the virus as the main cause of death.

Early evidence from Iceland, a country with a very strong organisation for wide testing within the population, suggests that as many as 50 per cent of infections are almost completely asymptomatic. Most of the rest are relatively minor. In fact, Iceland’s figures, 648 cases and two attributed deaths, give a death rate of 0.3 per cent. As population testing becomes more widespread elsewhere in the world, we will find a greater and greater proportion of cases where infections have already occurred and caused only mild effects. In fact, as time goes on, this will become generally truer too, because most infections tend to decrease in virulence as an epidemic progresses.


This raises a number of questions in my mind... I have not read the full article so I may be misinterpreting some of the arguments in it and I am happy to be corrected...



The plot that started this thread is of "number of cases" (ie positive tests) and not the number of deaths... Consequently, on the assumption that e compilers of the information are competent and capable of differentiating between infections and deaths, we can be reasonably happy that the plot compares apples with apples.



The article does not consider the effects that management may have had on the spread of the virus - exclusion of foreigners, self-isolation, forced quarantining of infected people, and the health care that can be offered. These clearly change from country to country and from time to time, and may well be sufficient to cause the different infection responses without having to invoke systematic errors, differing susceptibility, or different strains of the virus in different countries.



The value of testing in understanding the impact of the virus on the population as a whole is well made. It is indeed quite likely that the actual morbidity rate per unit infection is substantially lowered that what is currently understood because of limiting testing to those who are clearly ill and have had clear opportunity to become infected. If a random sample of the population was tested, the proportion of the population that has been infected without severe health effects would probably rise substantially. Different testing regime sin different countries may give rise to different infection rates, again without having to invoke systematic errors, differing susceptibility, or different strains of the virus in different countries.



Lastly, I cannot see waiting for the virulence of the virus to attenuate becoming an acceptable model for infection management (USA excluded if one takes public pronouncements from the top as indicative?).
 
The plot that started this thread is of "number of cases" (ie positive tests) and not the number of deaths... Consequently, on the assumption that e compilers of the information are competent and capable of differentiating between infections and deaths, we can be reasonably happy that the plot compares apples with apples.
I freely admit that I didn't read the article (or is it a reader's letter?) either. In fact, I am skipping over most of the stuff that deals with "numbers" in contrast to those that deal with "math(s)".

The issue (still) at hand is how fast the number of people grows who are seriously ill and who require hospital treatment and whether the hospital capacities (rooms, special equipment, staff trained in using the equipment on a patient, supplies) within a city, or within a region, or within a country can deal with the workload created by these cases. I have little doubt that those who are in charge of modelling and interpreting data and ongoing results know how to do this properly.

The next (emerging) issue is to find practical ways of determining when it will be meaningful to relax the lockdown measures and other measures.

BTW, I understood from public comments in two different European countries that the total number of infected cases will be evaluated at the end of the epidemic and one way to gain valuable data will be the blood donation banks. In fact I read this morning that a large-scale investigation has started in a region in Belgium where their Red Cross will deliver 6.000 blood samples (anonymised of course) per month to researchers to test for coronavirus antibodies.
 
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This raises a number of questions in my mind... I have not read the full article so I may be misinterpreting some of the arguments in it and I am happy to be corrected...

The parts of the article that I did not quote went into politics to a degree, so I did not quote it ... but if you like, and you can get past the paywall : https://www.spectator.co.uk/article/The-evidence-on-Covid-19-is-not-as-clear-as-we-think

He has a second article :


Every day, now, we are seeing figures for ‘Covid deaths’. These numbers are often expressed on graphs showing an exponential rise. But care must be taken when reading (and reporting) these figures. Given the extraordinary response to the emergence of this virus, it’s vital to have a clear-eyed view of its progress and what the figures mean. The world of disease reporting has its own dynamics, ones that are worth understanding. How accurate, or comparable, are these figures comparing Covid-19 deaths in various countries?

We often see a ratio expressed: deaths, as a proportion of cases. The figure is taken as a sign of how lethal Covid-19 is, but the ratios vary wildly. In the US, 1.8 per cent (2,191 deaths in 124,686 confirmed cases), Italy 10.8 per cent, Spain 8.2 per cent, Germany 0.8 per cent, France 6.1 per cent, UK 6.0 per cent. A fifteen-fold difference in death rate for the same disease seems odd amongst such similar countries: all developed, all with good healthcare systems. All tackling the same disease.

You might think it would be easy to calculate death rates. Death is a stark and easy-to-measure end point. In my working life (I’m a retired pathology professor) I usually come across studies that express it comparably and as a ratio: the number of deaths in a given period of time in an area, divided by that area’s population. For example, 10 deaths per 1,000 population per year. So just three numbers:

  1. The population who have contracted the disease
  2. The number dying of disease
  3. The relevant time period
The trouble is that in the Covid-19 crisis each one of these numbers is unclear.

etc ...

The plot that started this thread is of "number of cases" (ie positive tests) and not the number of deaths... Consequently, on the assumption that e compilers of the information are competent and capable of differentiating between infections and deaths, we can be reasonably happy that the plot compares apples with apples.

He does not accuse anyone of "incompetence" ; he's addressing a question that the statistical systems themselves may be flawed from a lack of coherence from country to country.

The article does not consider the effects that management may have had on the spread of the virus - exclusion of foreigners, self-isolation, forced quarantining of infected people, and the health care that can be offered.

erm, yes it does.

These clearly change from country to country and from time to time, and may well be sufficient to cause the different infection responses without having to invoke systematic errors, differing susceptibility, or different strains of the virus in different countries.

He is proposing hypotheses, having varying degrees of likelihood.

The value of testing in understanding the impact of the virus on the population as a whole is well made. It is indeed quite likely that the actual morbidity rate per unit infection is substantially lowered that what is currently understood because of limiting testing to those who are clearly ill and have had clear opportunity to become infected. If a random sample of the population was tested, the proportion of the population that has been infected without severe health effects would probably rise substantially. Different testing regime sin different countries may give rise to different infection rates

He makes that point himself, and it is unnecessary to repeat your suggestions concerning "systematic errors, differing susceptibility, or different strains of the virus in different countries".

Lastly, I cannot see waiting for the virulence of the virus to attenuate becoming an acceptable model for infection management

There is nothing whatsoever in the article to suggest any such thing.

Instead, "Covid-19 can clearly cause serious respiratory tract compromise in some patients, especially those with chest issues, and in smokers. The elderly are probably more at risk, as they are for infections of any kind. The average age of those dying in Italy is 78.5 years, with almost nine in ten fatalities among the over-70s. The life expectancy in Italy — that is, the number of years you can expect to live to from birth, all things being equal — is 82.5 years. But all things are not equal when a new seasonal virus goes around.

It certainly seems reasonable, now, that a degree of social distancing should be maintained for a while, especially for the elderly and the immune-suppressed.
"

In the second article he makes substantially the same pertinent point as one of yours, and it should be fairly clear in people's minds, in this thread anyway :

"The only way to identify people who definitely have the disease will be by using a lab test that is both specific for the disease (detects this disease only, and not similar diseases) and sensitive for the disease (picks up a large proportion of people with this disease, whether severe or mild). Developing accurate, reliable, validated tests is difficult and takes time. At the moment, we have to take it on trust that the tests in use are measuring what we think they are.

So far in this pandemic, test kits have mainly been reserved for hospitalised patients with significant symptoms. Few tests have been carried out in patients with mild symptoms. This means that the number of positive tests will be far lower than the number of people who have had the disease. Sir Patrick Vallance, the government’s chief scientific adviser, has been trying to stress this. He suggested that the real figure for the number of cases could be 10 to 20 times higher than the official figure. If he’s right, the headline death rate due to this virus (all derived from lab tests) will be 10 to 20 times lower than it appears to be from the published figures. The more the number of untested cases goes up, the lower the true death rate.
"

---

Clearly the position that he has taken in his articles are hypothetical and theoretical, so that I wouldn't expect people to agree with that position, and I have my own doubts about it too.

But I think that his underlying point is that having any sort of understanding of what these numbers mean in real terms is made more difficult by variations from country to country in how they are established and reported. The systematic error that he refers to is, I think, the very existence of those variations in the first place.
 
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In fact, I am skipping over most of the stuff that deals with "numbers" in contrast to those that deal with "math(s)".

That seems strange to me, though you do go on to point out that :

The issue (still) at hand is how fast the number of people grows who are seriously ill and who require hospital treatment and whether the hospital capacities (rooms, special equipment, staff trained in using the equipment on a patient, supplies) within a city, or within a region, or within a country can deal with the workload created by these cases. I have little doubt that those who are in charge of modelling and interpreting data and ongoing results know how to do this properly.

... which I can heartily agree with. 😷

But Dr Lee's articles are addressing a different question of statistics than that. What is the actual prevalence of the disease in the general population ? And from this, what is the true mortality ratio ?

And of course real numbers are a lot more important in the applied maths than the pure.
 
But Dr Lee's articles are addressing a different question of statistics than that.
As I said, I haven't read this article by a retired pathologist, just glanced quickly through the comments here. On this wobbly basis I make the following bold judgement: the issue that is addressed here is how do general purpose journalists and general purpose media report about specialist issues in the field of medicine and research and related topics and how do the readers process the information presented by them.

And my answer would be: not very well. And that is not new. That is not really meant as criticism. I do think that the media have an essential public role to play in our societies. And quite a few try to do their very best.

The great thing about our age is the wealth of excellent information sources that we can access thanks to the internet, especially now with increasingly free and easy access to the primary sources that generate and provide this information. And some of these primary source are doing quite a good job at explaining things to the general public. Kudos!
 
I try to follow official press conferences in about five countries, on and off. I must say that the most useful ones are the ones where there are only virologists/epidemiologists on my screen, certainly not politicians of any kind, and not even journalists in the room; in some countries they have to ask their questions in writing now, as a result of the lockdown measures.

I happened to watch the RKI press conference today and there was something interesting and new: they mentioned that R0 had been 5 and even 7 (!) some time ago and they know that it is currently around 1 but it needs to be pushed lower. I guess this refers to outbreak centres and not to the country as a whole. When R0<1, an epidemic peters out.

R0 is the number of non-infected persons (Wikipedia, consume with caution!) to whom an infected person transmits the virus on average. That number is not a virus-specific constant but depends on a number of factors and changes over time.
 
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It looks like R0 is all the rage right now. Here's an article published today in La Voz de Galicia, with a table with the current figures for R0 for all the Spanish regions. Ignore the title of the article, it's nonsense.
 
@Kathar1na -The medical experts who advise the province of Ontario (Canada) just gave a presentation (and answered press questions) on their model. It is likely online already ... or will be soon. It was covered by Globalnews, CBC, and others.

They mention a second and possible third waves, going forward 18 - 24 months.
 
Article :

This is such an interesting article
I can't go into the maths "opinion" on it as I was so bad at maths growing up, the school exempted me from state maths exams...true

Whatever way the UK is counting fatalities from the covid 19 ...I am intrigued as how those that die from other conditions eg...heart attacks , strokes etc may, or may not be included in the death rate

For example.......a person is admitted to hospital with a severe heart attack/ stroke....and they develop covid 19 ....then die......is this included in the covid stats ...or the fact that the person may have well died anyway due to a very poor prognosis??.

Every day in the UK , approximately 1600 die from various conditions or accidents ..are these deaths being tested, ..and could the covid 19 deaths be higher....lower than publicised
Sorry if this seems like a silly question
Just my non - mathematical mind wondering/wandering.
 
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@Annette london good question. No answer available until some time in 2025/6 when a review has been completed.

If you are listening to BBC Radio 4 you'll have heard various Government Spoke Persons stating that "X people "who had tested "positive" for Corona virus in Hospital"" have died. That number excludes anyone who has died who had not tested positive but may not have been tested. It also excludes anyone who doesn't die in Hospital but at home, in a care-home or in a Hospice. The language is unusually precise for a GSP.

We can take little comfort from the thought that the 684 deaths announced today were, most likely, in addition to the national daily average 1600.
 
We can take little comfort from the thought that the 684 deaths announced today were, most likely, in addition to the national daily average 1600.

Whether that's true, or not, or partially true, is another one of the unanswered questions.
 
The one from Galicia (the round) and the one from Castilla & Leon. Individually numbered and made by the same people that make the ones you see on your walk.
Seems to me that China has their reduction because they have the ability to complete shut down and isolate cities of fifteen million people .. I just cannot see that sort of thing happening in the western democracies .. in my country (UK) we are now forecasting an absolute minimum over the next few months of 20,000 deaths and our worst case is 260,000 deaths ..... sure, if we could enforce martial law and totally quarantine tens of millions of people we could control it, but we cannot .. what is coming is coming - God help us all.

Hola David - here in the Land Down Under, we have not gone to full lock down but appear to be on track for an extended (up to 90 day) isolation routine. Maybe HM needs to amend her broadcast recommending that the people of the UK follow the lead of NZ or Aust. In the meantime keep isolated and stay safe mate!! Best wishes M
 
The medical experts who advise the province of Ontario (Canada) just gave a presentation (and answered press questions) on their model. It is likely online already ... or will be soon. It was covered by Globalnews, CBC, and others. They mention a second and possible third waves, going forward 18 - 24 months.
Thanks. I've not found a video yet but I found an up to date written summary of their model: Ontario - COVID-19 Modelling. This summary is done well!
 
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What still stuns me a bit is the general predictability and inevitability of an epidemic process of this kind during the initial uncontrolled phase. A quote from the Imperial College Response Team: Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality ... As I understand it, on average, very roughly, for a sufficiently large sample of persons, it takes, from the start of the infection: up to a week until symptoms occur; up to two weeks until health conditions seriously deteriorate and intensive medical treatment becomes a necessity; up to three weeks until a fatal outcome. Again, very roughly and on average and not for an individual person but for a sufficiently large sample of people.

Spain's severe lockdown started on 14 March. Today, 4 April and three weeks later, there are news that Spain sees sharp drop in daily coronavirus deaths. In the last 24 hours, 809 people have died from Covid-19, the lowest figure recorded in seven days.

What's very sobering to read in the article quoted above: Since the outbreak began, five Civil Guard officers, two National Police officers, one member of the Catalan regional police force, and a Madrid local police officer have died from the [Covid-19] disease.

I also have the impression - a gut feeling, not a fact and not related to Spain - that some leading figures need to see a really big number, say a 6 digit number, i.e. a digit followed by five zeros, of projected fatalities to "get it". While others got it as soon as they saw the first graph of a function on a logarithmic scale. And while I would love to expand on this, I won't. And neither should anyone amongst you ... 🤭
 
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Statisticians will have heaps and heaps of data, based on how various interventions were made and not made, to play with for decades to come for modelling for the next pandemic.
 
I had heard a suggestion that the confinement measures were lowering deaths from the flu and other sources, casting into doubt the idea that Covid19 was causing an increase in mortality in the general population.

Needless to say, I was sceptical -- particularly as I could find no corroboration of this theory.

Well, the French are good at statistical analysis, and good at examining such questions dispassionately.

Le Monde here -- https://www.lemonde.fr/les-decodeur...ement-depuis-le-1er-mars_6035485_4355770.html -- does just that, and it shows a clear increase in mortality in the regions and areas worst affected by this outbreak -- up to +141% in the Haut Rhin département.

This is despite the drastic reduction in mortality from car accidents due to the confinement measures.

So : debunked.
 
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I'm a (retired) epidemiologist. And I'd like to ask you all to stop talking dirty to me in this thread. Carry on.
Disregarding.

A virologist meets an epidemiologist in a bar. After buying the epidemiologist a few drinks the virologist makes a proposition. The epidemiologist responds with "Sorry, I'm looking for a meaningful relationship."
 
My cousin’s chemo treatments have been suspended because of Covid-19. Her doctors think her short term survival stands a better chance without exposure to the virus and her immune system is stronger before a treatment than after. The more chemo she misses (and she has been beating the cancer) the more precarious her longer term survival is. If it’s only a couple of treatments, perhaps no issue.

I say this, as the broader impact of the virus on our health care system is yet to be clear. How many will die as they are too afraid to go to the hospital, so they ignore acute symptoms of something else? How many will die in future as too many doctors & nurses die?
 
I had heard a suggestion that the confinement measures were lowering deaths from the flu and other sources, casting into doubt the idea that Covid19 was causing an increase in mortality in the general population.

Needless to say, I was sceptical -- particularly as I could find no corroboration of this theory.

Well, the French are good at statistical analysis, and good at examining such questions dispassionately.

Le Monde here -- https://www.lemonde.fr/les-decodeur...ement-depuis-le-1er-mars_6035485_4355770.html -- does just that, and it shows a clear increase in mortality in the regions and areas worst affected by this outbreak -- up to +141% in the Haut Rhin département.

This is despite the drastic reduction in mortality from car accidents due to the confinement measures.

So : debunked.

I don't think you can extrapolate numbers from France to all countries.

Up here in Norway they first reported a couple weeks back that the Corona-measures might be working after seeing how the number of people infected by seasonal dropped 70% opposed to earlier years.
An average Influenza season apparently kills aprox 900 people in Norway. If we say the season is 6 months that would mean 150 deaths in an average influenza season month.
To date, aprox 4 weeks after first death reported, Covid-19 has apparently killed 101 people.
By those numbers it seems plausible the reduction in influenza deaths is comparable to the number of Covid-19 deaths so far.
Haven't seen any mortality numbers in Norway compared to previous years though, but as it stands it
wouldn't surprise me too much if they find that mortality rate in Norway in about the same as in a normal year when the time this is over. Well, if they are able to continue controlling it that is (big if). Just a few days ago the government reported that the outbreak is "under control" and that the Covid-19 R0 number is supposed to be about 0,7 (I find that number to be quite plausable as the number of people in ICUs and on ventilators are dropping noticeably)

Seeing low covid-19 death numbers from other countries like Taiwan, South Korea, Finland, Australia and New Zealand, it wouldn't surprise me if the situation is much the same there.

I've also seen plenty of reports like the one you are referring to where mortality is way up on previous years in other countries like Spain, Italy, France and UK, so for most countries that "debunked" assertation will probably be true though.
 
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I don't think you can extrapolate numbers from France to all countries.


Seeing low covid-19 death numbers from other countries like Taiwan, South Korea, Finland, Australia and New Zealand, it wouldn't surprise me if the situation is much the same there.

A definitional issue here - how is mortality defined? deaths as a proportion of the population, or deaths as a proportion of infected people?

In Australia, confinement (self-isolation in advance of infection and voluntary or compulsory confinement after infection) certainly appears to be reducing the infection rate. Fewer infections should lead to fewer deaths. The only obvious way that confinement can reduce the mortality rate as a proportion of population is by containing the infection rate to within the boundaries of hospital services available.

Note however that the major downward impact on infection rate seems to be border controls - preventing the free entry of people from overseas.

Note also that the (daily) rate of infections, as a proportion of the population, has no immediate bearing on the total number of people infected - 'flattening the curve' is all about keeping the demand for medical service below the supply capacity...

Until a vaccine is available, current lock-down approaches are the only way to contain the number of infections (short of wishing and hoping, which some national 'leaders' seem to have adopted).
 
A definitional issue here - how is mortality defined? deaths as a proportion of the population, or deaths as a proportion of infected people?
I know something 🤓. They distinguish between lethality (death per infected population) and mortality (death per population). I am currently listening to a press conference from the people who present the first results of in-depth study of a population in a small focus area in Germany where one of their outbreaks started. The area is called Heinsberg, the study was done for the town of Gangelt (2500 inhabitants).

They found that 14% of this (small) population had developed antibodies, so are presumably currently immune. 2% were acutely infected during the study. The speaker says that they calculated lethality as 0,37% while the number calculated by John Hopkins for the whole of Germany is currently given as 1,8% (and mortality as 0,06%). Although, obviously, the infection was/is more intense in the Heinsberg area than in the country on the whole.

This confirms again that many of these numbers for whole countries are mainly useful for each country themselves for monitoring likely developments in the immediate future and for adapting mitigating measures but not good for drawing general conclusions and comparisons.

One of the reporters asking questions in German has a pronounced English (AE?) accent. You may read about it in a newspaper near you soon 🙂. Again, one of the speakers says now that the daily numbers of infections and deaths that we see daily online or on TV are nearly meaningless. I paraphrase.
 
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I had heard a suggestion that the confinement measures were lowering deaths from the flu and other sources, casting into doubt the idea that Covid19 was causing an increase in mortality in the general population.
...
So : debunked.

Delayed (week 14 = about 1 week delayed) real mortality data with graphs for Euopean countries you can get here:
https://www.euromomo.eu/
So it is true for some countries and false for other countries... and for some we do not know until now.
 
The focus is on reducing the risk of failure through being well prepared. 2nd ed.
Another caveat concerning current UK statistics, as presented by the Media, from a forum poster in a different forum (won't link as it's a political one) :

"I see what the BBC are doing. They're comparing the cumulative figure for today with the cumulative figure for yesterday, and reporting that the difference between the two figures is accounted for by people who have died in the previous 24 hours, which is incorrect. What the difference is in fact accounting for is the deaths which have been entered in the records in the previous 24 hours. This is a number which, as Dr North explains, represents a quite different set of deaths from the set of those who have actually died in the previous 24 hours."
 
Le Monde has created a graphic showing the areas of Western Europe worst affected
Thanks for posting. The Le Monde infograph that you posted is a good visual, independent of how the underlying data are collected and independent of their degree of comparability or lack thereof. Stunning to see how large the area is that is severely inflicted in Spain. The infograph also shows the focal points in each country (even when they are not comparable between countries in absolute figures or per capita figures). Lots of good information out there. Do I now add the Le Monde blog to my reading list, too???
 
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The Ideas section of the April 12, 2020 Boston Globe has an article (possibly published earlier in the week) about herd immunity studies done at Harvard University. There are graphs and links. The scary part for me was that the longer the period of social distancing the more deaths overall.


https://www.bostonglobe.com/2020/04/10/opinion/its-possible-flatten-curve-too-long/

The webpage pops up with a subscription request but the close button worked.
 
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Official Camino Passport (Credential) | 2024 Camino Guides
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