ALL THE DATA COLLECTED LAST SATURDAY, 11/21/2020, HAS BEEN UPLOADED.
Note: Like all the pages on this site, this page is a "work in progress" with text and data being added all the time.
It's offered as a research aid for students and as an information source for parents, teachers, and others.
Best wishes, and I hope it helps.
Note: Like all the pages on this site, this page is a "work in progress" with text and data being added all the time.
It's offered as a research aid for students and as an information source for parents, teachers, and others.
Best wishes, and I hope it helps.
TOP TOPICS FOR THE WEEK OF NOVEMBER 21, 2020
I. US COVID-19 DEATHS SOARING
US Covid-19 deaths are continuing to increase dramatically, going from 223,951 to 260,628 - an increase of 36,707 in the five weeks ending on Saturday, November 21. Weekly CV-19 deaths have more than doubled during that period, going from 4,922 to 10,329 in just those five weeks. That's a 109.9% increase in weekly deaths, and here's the data up to last Saturday:
I. US COVID-19 DEATHS SOARING
US Covid-19 deaths are continuing to increase dramatically, going from 223,951 to 260,628 - an increase of 36,707 in the five weeks ending on Saturday, November 21. Weekly CV-19 deaths have more than doubled during that period, going from 4,922 to 10,329 in just those five weeks. That's a 109.9% increase in weekly deaths, and here's the data up to last Saturday:
| delta_deaths_usa_11-21-2020.pdf |
According to current US daily death statistics published by Worldometer, that trend is still accelerating . Its current seven-day moving average of daily US CV-19 deaths on Wednesday, 11/25/2020, was 1,657. That means the total US Covid-19 deaths for the week ending Wednesday was 11,599. And, even if our daily death count were to level off right there, 11,599 Covid-19 deaths for this week would be an increase of 6,677 dead Americans per week, representing a 135.7% increase in weekly deaths over the last six weeks.
For the most current data, check: https://www.worldometers.info/coronavirus/country/us/
For the most current data, check: https://www.worldometers.info/coronavirus/country/us/
II. WRONG TURN CONTINUES IN EUROPE.
The increase in European Covid-19 death statistics that I first noted three weeks ago, and which had become more general by last week, is continuing and its effects are visible from the Atlantic to Russia's western border. Five maps are provided below with data on total deaths per million residents for all jurisdictions in the affected area. The first four maps shows cumulative DPM data for the five weeks from 10/24/2020 till 11/21/2020, the last map shows the change in DPMs in percent for those jurisdictions for the period from 10/24 till 11/14.
Previously, I've found that changes in national deaths-per-million statistics tend to be gradual, with little change from one week to the next. However, you will see that the increase in DPMs shown in that last map is stunning, with increases as high as 221% during this period, and that situation only got worse in the next week.
I should note that I've been reviewing Covid-19 death statistics and doing weekly reports on them for the last twenty-seven weeks. When I began, the nations apparently affected by the Italian Variant of Covid-19 in Europe were few in number - just the adjoining nations of Italy, Spain, France, Belgium, and the Netherlands - along with the United Kingdom and Ireland. And their deaths per million residents due to Covid-19 were the only ones above 300 in all of the Eurasian Continent - with the exception of outlying Sweden, whose laissez-faire approach to the disease amounted to a self-inflicted wound. DPM totals in the remaining nations on that entire continent tended to be much, much lower.
That situation stayed like that until recently, but it's now changing dramatically. Take a look at these maps - you may have to download and rotate them to see them clearly - and then let's talk about the real story.
The increase in European Covid-19 death statistics that I first noted three weeks ago, and which had become more general by last week, is continuing and its effects are visible from the Atlantic to Russia's western border. Five maps are provided below with data on total deaths per million residents for all jurisdictions in the affected area. The first four maps shows cumulative DPM data for the five weeks from 10/24/2020 till 11/21/2020, the last map shows the change in DPMs in percent for those jurisdictions for the period from 10/24 till 11/14.
Previously, I've found that changes in national deaths-per-million statistics tend to be gradual, with little change from one week to the next. However, you will see that the increase in DPMs shown in that last map is stunning, with increases as high as 221% during this period, and that situation only got worse in the next week.
I should note that I've been reviewing Covid-19 death statistics and doing weekly reports on them for the last twenty-seven weeks. When I began, the nations apparently affected by the Italian Variant of Covid-19 in Europe were few in number - just the adjoining nations of Italy, Spain, France, Belgium, and the Netherlands - along with the United Kingdom and Ireland. And their deaths per million residents due to Covid-19 were the only ones above 300 in all of the Eurasian Continent - with the exception of outlying Sweden, whose laissez-faire approach to the disease amounted to a self-inflicted wound. DPM totals in the remaining nations on that entire continent tended to be much, much lower.
That situation stayed like that until recently, but it's now changing dramatically. Take a look at these maps - you may have to download and rotate them to see them clearly - and then let's talk about the real story.
| map_of_european_dpms_10-24-2020.pdf |
| map_of_european_dpms_10-31-2020.pdf |
| map_of_european_dpms_11-7-2020.pdf |
| map_of_european_dpms_11-14-2020.pdf |
| map_of_european_dpms_11-21-2020.pdf |
The next map shows European deaths per million through 11-14-2020 in black and the percentage of increase over the previous three weeks in red.
| map_of_european_dpms_with_deltas_11-14-20.pdf |
But if you dig a little deeper than the data displayed on these maps, you'll find that the real source of the problem is not in the countries that appear to be the most affected by this surge. To find the source, start by opening:
www.worldometers.info/coronavirus/
Then scroll down to the list of countries and right-click on Ireland opening a new tab. Then scroll down to the graph showing Ireland's "Daily Deaths", left-click the box labelled "7-day moving average", and you'll see this:
Ireland got hit hard early by Covid-19 but fought its daily death count down to single digits and has kept it there since. And that did not result from good luck or magic. It resulted from the hard work of the Irish. Admirable!
Now do the same thing for Italy, France, the United Kingdom, Spain, Belgium, and the Netherlands - opening new tabs for each of them - and you will see an entirely different story. Like Ireland, each of those nations got hit hard early by CV-19 and fought the 7-day moving average of their daily death counts down the single digits by last August. But then they quit. Each of them quit. And now their latest daily death counts - as shown by their 7-day moving averages - have ballooned from the single digits to as many as 674 DEATHS PER DAY, and here are the individual totals for each nation as of 11/24/2020:
Netherlands 59, Belgium 171, Spain 269, United Kingdom 440, France 571, and Italy 674.
The takeaway is obvious. Those nations relaxed their CV-19 restrictions too early, and the victims were not only their own people but also the people in neighboring countries as well, since European travel restrictions ended last summer too, allowing the more lethal Italian Variant of CV-19 to spread more freely.
But I think this situation can still be improved if the leaders of all European nations recognize this problem, recognize its sources, and guard against further surges in infections and deaths in their countries and throughout their continent through strict and selective border controls.
III. AN UNFORTUNATE WEEK IN SAN MARINO
In the nine months I've been doing the research that resulted in this webpage, I've learned that there are no "national" Covid-19 cases, treatments, recoveries, or deaths. There are only local ones. And Covid-19 won't be beaten until it's beaten locally, in every community in every nation, by people who are absolutely devoted to getting their villages, towns and cities to zero. Zero covid cases, but especially zero covid deaths.
Internationally, I've found that zero is a rare number that's tough to achieve, but two nations have stood out in my research, hyper-populous China with its 1.4 billion people and little, lovable San Marino with it 34 thousand. China has not had a single additional CV-19 death for six months, and San Marino hadn't had one since May 23rd. Until last week.
San Marino suffered two new losses last week, and I would like to offer my condolences, my prayers, and my best wishes for San Marino in its continuing struggle.
And, by the way, if - like most people - you've never even heard of San Marino, here's a link to a five-minute introduction to that singularly lovely place by Rick Steves:
www.youtube.com/watch?v=eSqoEhWd9cM
www.worldometers.info/coronavirus/
Then scroll down to the list of countries and right-click on Ireland opening a new tab. Then scroll down to the graph showing Ireland's "Daily Deaths", left-click the box labelled "7-day moving average", and you'll see this:
Ireland got hit hard early by Covid-19 but fought its daily death count down to single digits and has kept it there since. And that did not result from good luck or magic. It resulted from the hard work of the Irish. Admirable!
Now do the same thing for Italy, France, the United Kingdom, Spain, Belgium, and the Netherlands - opening new tabs for each of them - and you will see an entirely different story. Like Ireland, each of those nations got hit hard early by CV-19 and fought the 7-day moving average of their daily death counts down the single digits by last August. But then they quit. Each of them quit. And now their latest daily death counts - as shown by their 7-day moving averages - have ballooned from the single digits to as many as 674 DEATHS PER DAY, and here are the individual totals for each nation as of 11/24/2020:
Netherlands 59, Belgium 171, Spain 269, United Kingdom 440, France 571, and Italy 674.
The takeaway is obvious. Those nations relaxed their CV-19 restrictions too early, and the victims were not only their own people but also the people in neighboring countries as well, since European travel restrictions ended last summer too, allowing the more lethal Italian Variant of CV-19 to spread more freely.
But I think this situation can still be improved if the leaders of all European nations recognize this problem, recognize its sources, and guard against further surges in infections and deaths in their countries and throughout their continent through strict and selective border controls.
III. AN UNFORTUNATE WEEK IN SAN MARINO
In the nine months I've been doing the research that resulted in this webpage, I've learned that there are no "national" Covid-19 cases, treatments, recoveries, or deaths. There are only local ones. And Covid-19 won't be beaten until it's beaten locally, in every community in every nation, by people who are absolutely devoted to getting their villages, towns and cities to zero. Zero covid cases, but especially zero covid deaths.
Internationally, I've found that zero is a rare number that's tough to achieve, but two nations have stood out in my research, hyper-populous China with its 1.4 billion people and little, lovable San Marino with it 34 thousand. China has not had a single additional CV-19 death for six months, and San Marino hadn't had one since May 23rd. Until last week.
San Marino suffered two new losses last week, and I would like to offer my condolences, my prayers, and my best wishes for San Marino in its continuing struggle.
And, by the way, if - like most people - you've never even heard of San Marino, here's a link to a five-minute introduction to that singularly lovely place by Rick Steves:
www.youtube.com/watch?v=eSqoEhWd9cM
IV. EXEMPLARY NEW ZEALAND
Thus far in this whole Pandemic, New Zealand's deaths per million residents is a paltry five. That's 2 DPMs higher than China's 3 DPMs and equal to Singapore's total which is also a five. For purposes of comparison and remembering that DPMs are automatically adjusted for population size, that means that the 787 US DPMs as of last week were 157.4 times higher than New Zealand's five.
Additionally, New Zealand has not had a Covid-19 death since September 16th, while the US had suffered 33,207 more CV-19 deaths from then till my last data run on 11/21. Doubt New Zealand's data? See for yourself. Just open this page and scroll down to the Daily Deaths graph. It automatically updates.
www.worldometers.info/coronavirus/country/new-zealand/
Thus far in this whole Pandemic, New Zealand's deaths per million residents is a paltry five. That's 2 DPMs higher than China's 3 DPMs and equal to Singapore's total which is also a five. For purposes of comparison and remembering that DPMs are automatically adjusted for population size, that means that the 787 US DPMs as of last week were 157.4 times higher than New Zealand's five.
Additionally, New Zealand has not had a Covid-19 death since September 16th, while the US had suffered 33,207 more CV-19 deaths from then till my last data run on 11/21. Doubt New Zealand's data? See for yourself. Just open this page and scroll down to the Daily Deaths graph. It automatically updates.
www.worldometers.info/coronavirus/country/new-zealand/
THIS PANDEMIC CAN ONLY BE BEATEN LOCALLY,
SO LET'S GET TO ZERO!
SO LET'S GET TO ZERO!
I. INTRODUCTION
No matter what your chosen career might be now, or what it might become later, the Novel Coronavirus Pandemic of 2020 will be a major component of your study and research this year and for the rest of your lives. Whether your career is in medicine, mathematics, the social sciences, business, engineering, journalism, literature, government, sports or any other field, the 2020 Pandemic will dramatically affect all aspects of your educations as well as your entire careers in the future just as it already has this year.
My primary purpose in publishing this page is to help you students get an early handle - an unbiased handle - on the whole topic, both to help your understanding and to improve the quality of your term papers. But this page will also be read by some journalists, analysts, public health professionals, and politicians - some of whom are interested in seeing this pandemic through an unbiased lens, and the rest of whom just clicked off this page.
Those who are still here deserve a little background about me. I have a BA in Social and Behavioral Sciences from Johns Hopkins and a JD from the University of Wyoming. After I quit practicing law and returned to business in 1988, I've been steadily engaged in analyzing data for the benefit of my clients, first as a stockbroker - I was the first Edward D. Jones representative on Bainbridge Island, Washington - later as a residential and commercial real estate broker, and for the last eight years as an independent college financial aid analyst - witness this website.
So, I guess it'd be fair to say that I'm alert to the numerical differences around me, I know how to count, and I can present numerical differences in an understandable way for the benefit of my clients. As you've already seen in the rest of this site, when I see a need that can be met by research - as in developing a method for helping students like you and your parents find really affordable colleges - I dive right in and do the research necessary to get it right.
So, there's a new approach to CV-19 with a lot of data on this page, but I think we should start with some basics.
No matter what your chosen career might be now, or what it might become later, the Novel Coronavirus Pandemic of 2020 will be a major component of your study and research this year and for the rest of your lives. Whether your career is in medicine, mathematics, the social sciences, business, engineering, journalism, literature, government, sports or any other field, the 2020 Pandemic will dramatically affect all aspects of your educations as well as your entire careers in the future just as it already has this year.
My primary purpose in publishing this page is to help you students get an early handle - an unbiased handle - on the whole topic, both to help your understanding and to improve the quality of your term papers. But this page will also be read by some journalists, analysts, public health professionals, and politicians - some of whom are interested in seeing this pandemic through an unbiased lens, and the rest of whom just clicked off this page.
Those who are still here deserve a little background about me. I have a BA in Social and Behavioral Sciences from Johns Hopkins and a JD from the University of Wyoming. After I quit practicing law and returned to business in 1988, I've been steadily engaged in analyzing data for the benefit of my clients, first as a stockbroker - I was the first Edward D. Jones representative on Bainbridge Island, Washington - later as a residential and commercial real estate broker, and for the last eight years as an independent college financial aid analyst - witness this website.
So, I guess it'd be fair to say that I'm alert to the numerical differences around me, I know how to count, and I can present numerical differences in an understandable way for the benefit of my clients. As you've already seen in the rest of this site, when I see a need that can be met by research - as in developing a method for helping students like you and your parents find really affordable colleges - I dive right in and do the research necessary to get it right.
So, there's a new approach to CV-19 with a lot of data on this page, but I think we should start with some basics.
II. LET'S BEGIN WITH A DOSE OF HUMILITY
Before we're too harsh and judgmental with China, the origin of the 2020 Pandemic, let's take a brief look at the origins of the 1918 Pandemic.
It's called the "Spanish Flu" of 1918, but it would be more accurate to call it the "American Flu", because it didn't originate in Spain at all. It originated here in the USA, specifically on one farm in Haskell County, Kansas. It was only called the Spanish Flu because Spain was the only Western European country that was not a belligerent in World War I, so it was also the only country with a free press at that time, a press that fully covered the horrible effects of that disease in Spain. The belligerent nations in Europe - and in the US too, since America had just entered WWI and was gearing up its war effort - censored coverage of the growing pandemic, because the leaders of those nations believed that knowing its severity would be bad for public morale and for the overall war effort.
The disease at the center of the 1918 Pandemic was a "swine flu", a viral disease that initially sickened pigs, but mutated the capacity to also sicken humans. It was also not the last swine flu the world has seen, but it might have been the nastiest. It was especially lethal for young adults between the ages of twenty and forty, frequently taking its victims from their first symptoms to their deathbeds in a single day.
A local physician in Haskell County tried to make national health officials in America aware of the severity of the disease, but he got no response. So the disease spread to the new US Army training camps in that area and from there around America. That pandemic ultimately killed about 675,000 Americans, but it didn't stay in the US. It traveled with our troops overseas, and it ultimately killed between twenty and one hundred million additional people worldwide. And, to put that number in perspective, that's roughly the number of all the civilians killed in the Second World War.
The best recent treatise on that pandemic, The Great Influenza, was written by historian and author John Barry. His book is generally available, but there's probably a line to get it at your school or city library right now. In the meantime, here's a link to a 40 minute video on the 1918 Pandemic that's mainly based on Mr. Barry's research:
www.youtube.com/watch?v=UDY5COg2P2c&t=1443s
And here's the link to the Wikipedia article on Haskell County, Kansas, which includes a brief history of the beginning of the 1918 Pandemic with an important quote on its origin from Mr. Barry's book stating his thesis: that there were no tracks of the 1918 Pandemic leading toward Haskell County, only tracks leading away.
en.wikipedia.org/wiki/Haskell_County,_Kansas
Before we're too harsh and judgmental with China, the origin of the 2020 Pandemic, let's take a brief look at the origins of the 1918 Pandemic.
It's called the "Spanish Flu" of 1918, but it would be more accurate to call it the "American Flu", because it didn't originate in Spain at all. It originated here in the USA, specifically on one farm in Haskell County, Kansas. It was only called the Spanish Flu because Spain was the only Western European country that was not a belligerent in World War I, so it was also the only country with a free press at that time, a press that fully covered the horrible effects of that disease in Spain. The belligerent nations in Europe - and in the US too, since America had just entered WWI and was gearing up its war effort - censored coverage of the growing pandemic, because the leaders of those nations believed that knowing its severity would be bad for public morale and for the overall war effort.
The disease at the center of the 1918 Pandemic was a "swine flu", a viral disease that initially sickened pigs, but mutated the capacity to also sicken humans. It was also not the last swine flu the world has seen, but it might have been the nastiest. It was especially lethal for young adults between the ages of twenty and forty, frequently taking its victims from their first symptoms to their deathbeds in a single day.
A local physician in Haskell County tried to make national health officials in America aware of the severity of the disease, but he got no response. So the disease spread to the new US Army training camps in that area and from there around America. That pandemic ultimately killed about 675,000 Americans, but it didn't stay in the US. It traveled with our troops overseas, and it ultimately killed between twenty and one hundred million additional people worldwide. And, to put that number in perspective, that's roughly the number of all the civilians killed in the Second World War.
The best recent treatise on that pandemic, The Great Influenza, was written by historian and author John Barry. His book is generally available, but there's probably a line to get it at your school or city library right now. In the meantime, here's a link to a 40 minute video on the 1918 Pandemic that's mainly based on Mr. Barry's research:
www.youtube.com/watch?v=UDY5COg2P2c&t=1443s
And here's the link to the Wikipedia article on Haskell County, Kansas, which includes a brief history of the beginning of the 1918 Pandemic with an important quote on its origin from Mr. Barry's book stating his thesis: that there were no tracks of the 1918 Pandemic leading toward Haskell County, only tracks leading away.
en.wikipedia.org/wiki/Haskell_County,_Kansas
III. A NOTE ON MY APPROACH
I fully explain my method below, but it's based on an analysis of Covid-19 death statistics gathered each Saturday morning mainly from:
https://www.worldometers.info/coronavirus/
I focus on death statistics because that's how pandemics are "scored." Of course, knowing the number of new cases is essential for policy makers in determining where the next flareup may be within a jurisdiction - meaning in a city, county, state/province or nation - and where to devote the jurisdiction's resources. But relatively few of those cases will ever result in deaths, and it's the deaths the statisticians and historians count. Look at it this way: it's great when sick people get better, but the strength of the pandemic will be based on the number who didn't.
I present the data I collect in two ways, first with reference to a commonly known American city/county as a gauge - Philadelphia on the American East Coast and in Europe and San Francisco on the American West Coast and in Asia. And then I include a commonly used population-adjusted statistic for each jurisdiction, its deaths per million residents.
Additionally, I try to never engage in whatever the popular American prejudices might be that week, month, year, or presidential term. For instance, I consciously treat the Chinese, Russians, and Iranians just like other people.
Finally, I place full trust in the good faith of the people who collect the data I analyze until those people give me a verifiable reason to doubt their word. That's because I know that, if I assume bad faith in the people collecting the data I analyze, I will never know anything at all.
I fully explain my method below, but it's based on an analysis of Covid-19 death statistics gathered each Saturday morning mainly from:
https://www.worldometers.info/coronavirus/
I focus on death statistics because that's how pandemics are "scored." Of course, knowing the number of new cases is essential for policy makers in determining where the next flareup may be within a jurisdiction - meaning in a city, county, state/province or nation - and where to devote the jurisdiction's resources. But relatively few of those cases will ever result in deaths, and it's the deaths the statisticians and historians count. Look at it this way: it's great when sick people get better, but the strength of the pandemic will be based on the number who didn't.
I present the data I collect in two ways, first with reference to a commonly known American city/county as a gauge - Philadelphia on the American East Coast and in Europe and San Francisco on the American West Coast and in Asia. And then I include a commonly used population-adjusted statistic for each jurisdiction, its deaths per million residents.
Additionally, I try to never engage in whatever the popular American prejudices might be that week, month, year, or presidential term. For instance, I consciously treat the Chinese, Russians, and Iranians just like other people.
Finally, I place full trust in the good faith of the people who collect the data I analyze until those people give me a verifiable reason to doubt their word. That's because I know that, if I assume bad faith in the people collecting the data I analyze, I will never know anything at all.
IV. GENERAL THESIS
A. Method and Format
As the Covid-19 pandemic spread out of China in January and February this year, it was obvious to me that the disease being endured by Italy was a new and different variant of the CV-19 virus, that it was more than ten times more lethal than the original Wuhan Variant affecting south and east Asia, and that the reasons being given in the press for its more extreme lethality - like "It's just those kissy-huggy Italians" - seemed rather childish. And those anecdotal reasons seemed all the less scientifically valid as the Italian Variant made its way from Italy through Spain, France, Belgium, The Netherlands, and then to Great Britain.
"Those kissy-huggy Brits?" Yeah, right.
B. Tracking Gorillas
Here, I'll take a pause and mention that many of you will have careers that involve in numerical analysis, and throughout your careers you'll see plenty of "multiplier events", where the numerical difference from one event and the next is obviously in multiples - meaning several times higher or lower, and equaling several hundreds or even thousands of percent. In those cases, I think it's best to not strain to explain obvious multiplier events with "fractional factors" that may only explain ten, twenty, thirty, or forty percent of differences the extent of those differences. Instead, be open to the possibility that you're looking at something new.
As an example, let's say that you're a primate biologist and you live in a world where no primates have been observed that are larger than chimpanzees. But then you come across a footprint made by a large gorilla. Then you spot another footprint like the first, and then more and more of them, and all the footprints are in one forest with none in any other forest. Now, you've never heard the word "gorilla" before, and you've never actually seen these. But you've seen their footprints, so you know that gorillas exist, and you know that what differentiates their vastly increased size from the chimpanzees you've actually seen is not eating more pasta. They're just different animals. And you know how to recognize the presence of gorillas in other forests, because you know their footprints.
End of pause.
C. Back to Method and Format
Thankfully, the quality of publicly available data on Covid-19 were and are superb, so by the middle of May I had a good idea just how much more lethal the Italian Variant was when compared to the common Wuhan Variant. So, after some numerical experimentation, and based solely on its numeric footprint - the numbers of deaths per million residents it was leaving in its wake - I came to opinion that the multiplier was between ten and twenty times.
As the weeks passed, the Italian Variant spread in an arc - a "Swath" might be more descriptive - from Italy west and northwest through Spain, France, Belgium, the Netherlands, the UK, and Ireland and with some apparent effect on bordering nations - such as Portugal to the west and Switzerland, Luxembourg, Germany and Denmark to the north and east. But the effect of the Italian Variant of CV-19 was limited to just those nations, and it did not affect any other jurisdictions throughout the entirety of Eurasia then - including China itself. Here I'll note that Sweden, with its disastrous laissez-faire approach to the Pandemic, is a special case with elevated death totals as the result.
In the United States and Canada, the data were bifurcated. The disease affecting the hardest hit states in our Northeast - meaning New Jersey, New York, Massachusetts, Connecticut, Rhode Island, and Pennsylvania - along with the Province of Quebec - had the statistical signature of the Italian Variant. While the states on our West Coast, along with the Provinces of British Columbia and the Prairie Provinces (Alberta, Saskatchewan & Manitoba), had the statistical signature of the Wuhan Variant.
By the way, for my data analyses, I picked one top-performing jurisdiction on each coast of the United States as my gauge, my point of comparison for all the jurisdictions around it. On the West Coast I chose the City and County of San Francisco, and in the Northeast I chose the City and County of Philadelphia.
A. Method and Format
As the Covid-19 pandemic spread out of China in January and February this year, it was obvious to me that the disease being endured by Italy was a new and different variant of the CV-19 virus, that it was more than ten times more lethal than the original Wuhan Variant affecting south and east Asia, and that the reasons being given in the press for its more extreme lethality - like "It's just those kissy-huggy Italians" - seemed rather childish. And those anecdotal reasons seemed all the less scientifically valid as the Italian Variant made its way from Italy through Spain, France, Belgium, The Netherlands, and then to Great Britain.
"Those kissy-huggy Brits?" Yeah, right.
B. Tracking Gorillas
Here, I'll take a pause and mention that many of you will have careers that involve in numerical analysis, and throughout your careers you'll see plenty of "multiplier events", where the numerical difference from one event and the next is obviously in multiples - meaning several times higher or lower, and equaling several hundreds or even thousands of percent. In those cases, I think it's best to not strain to explain obvious multiplier events with "fractional factors" that may only explain ten, twenty, thirty, or forty percent of differences the extent of those differences. Instead, be open to the possibility that you're looking at something new.
As an example, let's say that you're a primate biologist and you live in a world where no primates have been observed that are larger than chimpanzees. But then you come across a footprint made by a large gorilla. Then you spot another footprint like the first, and then more and more of them, and all the footprints are in one forest with none in any other forest. Now, you've never heard the word "gorilla" before, and you've never actually seen these. But you've seen their footprints, so you know that gorillas exist, and you know that what differentiates their vastly increased size from the chimpanzees you've actually seen is not eating more pasta. They're just different animals. And you know how to recognize the presence of gorillas in other forests, because you know their footprints.
End of pause.
C. Back to Method and Format
Thankfully, the quality of publicly available data on Covid-19 were and are superb, so by the middle of May I had a good idea just how much more lethal the Italian Variant was when compared to the common Wuhan Variant. So, after some numerical experimentation, and based solely on its numeric footprint - the numbers of deaths per million residents it was leaving in its wake - I came to opinion that the multiplier was between ten and twenty times.
As the weeks passed, the Italian Variant spread in an arc - a "Swath" might be more descriptive - from Italy west and northwest through Spain, France, Belgium, the Netherlands, the UK, and Ireland and with some apparent effect on bordering nations - such as Portugal to the west and Switzerland, Luxembourg, Germany and Denmark to the north and east. But the effect of the Italian Variant of CV-19 was limited to just those nations, and it did not affect any other jurisdictions throughout the entirety of Eurasia then - including China itself. Here I'll note that Sweden, with its disastrous laissez-faire approach to the Pandemic, is a special case with elevated death totals as the result.
In the United States and Canada, the data were bifurcated. The disease affecting the hardest hit states in our Northeast - meaning New Jersey, New York, Massachusetts, Connecticut, Rhode Island, and Pennsylvania - along with the Province of Quebec - had the statistical signature of the Italian Variant. While the states on our West Coast, along with the Provinces of British Columbia and the Prairie Provinces (Alberta, Saskatchewan & Manitoba), had the statistical signature of the Wuhan Variant.
By the way, for my data analyses, I picked one top-performing jurisdiction on each coast of the United States as my gauge, my point of comparison for all the jurisdictions around it. On the West Coast I chose the City and County of San Francisco, and in the Northeast I chose the City and County of Philadelphia.
IV. GENERAL MATERIALS
A. When the Pandemic Began in the United States
Covid-19 arrived on both coasts of the United States at roughly the same time with the first CV-19 deaths on neither coast predating the other. Review the next graph - you'll probably have to rotate it - and also you'll see that, over just a three-week period beginning in the middle of March 2020, 49 states and the District of Columbia had suffered their first CV-19 deaths.
A. When the Pandemic Began in the United States
Covid-19 arrived on both coasts of the United States at roughly the same time with the first CV-19 deaths on neither coast predating the other. Review the next graph - you'll probably have to rotate it - and also you'll see that, over just a three-week period beginning in the middle of March 2020, 49 states and the District of Columbia had suffered their first CV-19 deaths.
| first_us_cv-19_deaths.pdf |
B. Tracking the "Covid Gorilla" in Canada
The differences between the effects of Italian and Wuhan Variants of Covid-19 are still most visible in Canada. Let's focus the western province of British Columbia, with its orientation toward Asia and especially China, and Quebec, with its orientation toward Europe and especially France. In line with the numeric footprints of the two variants that I mentioned earlier, you'll see in the next graph that the Province of Quebec's 704 deaths per million residents is more than 14 times higher than British Columbia's 49 DPMs. And remember that Canada has national healthcare, which damps out healthcare differences as a cause for that extreme differential. Again, you'll probably have to rotate this graph to see it properly, and the provinces are arrayed as you'd see them on a map, with west on the left.
The differences between the effects of Italian and Wuhan Variants of Covid-19 are still most visible in Canada. Let's focus the western province of British Columbia, with its orientation toward Asia and especially China, and Quebec, with its orientation toward Europe and especially France. In line with the numeric footprints of the two variants that I mentioned earlier, you'll see in the next graph that the Province of Quebec's 704 deaths per million residents is more than 14 times higher than British Columbia's 49 DPMs. And remember that Canada has national healthcare, which damps out healthcare differences as a cause for that extreme differential. Again, you'll probably have to rotate this graph to see it properly, and the provinces are arrayed as you'd see them on a map, with west on the left.
| canadian_dpms_by_province.pdf |
Right here is a good spot for bringing up the danger of over-generalizing your research in your term papers. For instance, it's true that Quebec's 704 DPMs is a huge number in the Canadian context, and it's also true that the Province of Quebec has 62% of Canada's CV-19 deaths while only having 22.5% of Canada's total population. But you'll see in my latest weekly summaries below that, for a jurisdiction hit hard by the Italian Variant, that 704 is about the best in all of North America and compares well with the seven hardest hit nations in Western Europe. Additionally, the DPM totals for the remaining Canadian Provinces vary between good, great, and amazing.
Also, the Province of Quebec - along with the other Provinces on Canada's southern flank - is an absolutely huge place with lots of cities, and you can bet that 704 is not a common DPM among those cities or throughout all of Quebec. So, if you're doing a paper on Quebec, you'll need to use available resources to dig deeper.
New York State provides a good example of this in America. You'll see below that New York State's current 1,745 DPMs is the second worst in its six-state area - and the second worst in the US too, by the way. But, if you substitute out the current total CV-19 deaths in New York City, and substitute in NYC's Philadelphia Equivalent, you can then recalculate New York State's DPMs with a dose of reality. Then you'll find that the New York State DPM total drops by about 700, taking the state from the second worst in its area to the second best. So, obviously New York State's CV-19 problems aren't generalized throughout the state, they're focused on the City, and that analytical approach changes the whole history of the Pandemic there.
Oops, back to Canada for one last comment. You can see from that bar graph that a national total for Canadian DPMs doesn't make much sense analytically. A national total acts like an average DPM value for the nation, and you can see that in a nation - like Canada - with what you could call a distinct viral dichotomy (good SAT word), you essentially have Quebec with one value and everybody else with another. So averaging the data is meaningless.
Also, the Province of Quebec - along with the other Provinces on Canada's southern flank - is an absolutely huge place with lots of cities, and you can bet that 704 is not a common DPM among those cities or throughout all of Quebec. So, if you're doing a paper on Quebec, you'll need to use available resources to dig deeper.
New York State provides a good example of this in America. You'll see below that New York State's current 1,745 DPMs is the second worst in its six-state area - and the second worst in the US too, by the way. But, if you substitute out the current total CV-19 deaths in New York City, and substitute in NYC's Philadelphia Equivalent, you can then recalculate New York State's DPMs with a dose of reality. Then you'll find that the New York State DPM total drops by about 700, taking the state from the second worst in its area to the second best. So, obviously New York State's CV-19 problems aren't generalized throughout the state, they're focused on the City, and that analytical approach changes the whole history of the Pandemic there.
Oops, back to Canada for one last comment. You can see from that bar graph that a national total for Canadian DPMs doesn't make much sense analytically. A national total acts like an average DPM value for the nation, and you can see that in a nation - like Canada - with what you could call a distinct viral dichotomy (good SAT word), you essentially have Quebec with one value and everybody else with another. So averaging the data is meaningless.
V. FILES UPDATED WEEKLY
A. Delta Deaths USA
In math and science, the Greek letter "delta" - the one that looks like a triangle - is used to indicate the change in a value, and this document records the numbers and the change in those numbers of US Covid-19 deaths and deaths per million residents (DPMs) for the week ending November 7, 2020, while comparing it with the 18 previous weeks. You'll see that, as of 11/7, the US had sustained 243,127 CV-19 deaths, an increase of 7,863 deaths from the previous week. US total deaths per million residents as of 11/7 were 733 DPMs, an increase of 24 from the previous week.
For purposes of comparison, China had sustained total of 4,634 Covid-19 deaths as of 11/14, and China had not sustained an additional CV-19 death for the previous six months. Additionally, China's total deaths per million residents as of 11/14 were 3, roughly comparable to Singapore's and New Zealand's 5 DPMs each. China's DPMs were also somewhat better than South Korea (9), Japan (14), and Australia (35) while being somewhat worse than Vietnam's spectacular .4 DPMs.
A. Delta Deaths USA
In math and science, the Greek letter "delta" - the one that looks like a triangle - is used to indicate the change in a value, and this document records the numbers and the change in those numbers of US Covid-19 deaths and deaths per million residents (DPMs) for the week ending November 7, 2020, while comparing it with the 18 previous weeks. You'll see that, as of 11/7, the US had sustained 243,127 CV-19 deaths, an increase of 7,863 deaths from the previous week. US total deaths per million residents as of 11/7 were 733 DPMs, an increase of 24 from the previous week.
For purposes of comparison, China had sustained total of 4,634 Covid-19 deaths as of 11/14, and China had not sustained an additional CV-19 death for the previous six months. Additionally, China's total deaths per million residents as of 11/14 were 3, roughly comparable to Singapore's and New Zealand's 5 DPMs each. China's DPMs were also somewhat better than South Korea (9), Japan (14), and Australia (35) while being somewhat worse than Vietnam's spectacular .4 DPMs.
| delta_deaths_usa_11-21-2020.pdf |
B. Tracking the "Covid Gorilla" in the United States (More text coming soon.)
| philadelphia_vs_san_francisco_11-21-2020.pdf |
| pennsylvania_vs_oregon_11-21-2020.pdf |
| new_jersey_vs_california_11-21-2020.pdf |
| new_york_vs_california_11-21-2020.pdf |
VI. DEATHS PER MILLION RESIDENTS (San Marino data will be updated soon.)
A. DPMs in General
1. Introduction.
If you look at the list of data by nations in the worldometers.info site I mentioned above,
https://www.worldometers.info/coronavirus/
you'll find my favorite column in that table, "Deaths/1M Pop." That stands for "deaths per million people", and it gives us an accurate, population-adjusted comparison statistic with no further effort. If you're looking at that site on a phone, you'll probably have to turn your phone to "landscape" and compress the see that column because it's on the right-hand side of the table.
I abbreviate "deaths per million" to DPM, and if you scroll down that column you find that the highest DPM belongs to little, lovable San Marino, a former Italian city-state on the top of a ridge between Florence and Rimini. San Marino has a tiny population of about 34,000 but it suffered 42 deaths in the initial surge of the First Wave, giving it a DPM of 1235 - the worst in the world on that list. However, as a point of reference, San Marino has not had a CV-19 death for five months - meaning it GOT TO ZERO! - so, it won't get pinged by me.
By the way, San Marino looks like a great place to visit when this thing lightens up, and here's a five-minute video by Rick Steves on that topic:
www.youtube.com/watch?v=eSqoEhWd9cM
2. Trailing Indicators
And San Marino's DPM total points out two interesting attributes about DPMs and all CV-19 death statistics, for that matter - they are all "trailing indicators", and they are all cumulative in nature. (More text coming soon.)
3. Finding And Calculating DPM's
On worldometers.info can click on any country and go to its own webpage. If you click on the United States, you'll find a table listing all the states with DPM calculations provided for each. And if you click on the individual states you will get a list of all the counties in that state, but that list will not include DPMs, so you'll have to calculate the relevant ones for yourself. You're going to make that calculation a lot, and there may be an app for it, but here's a simple method I use.
Find out the exact number of CV-19 deaths to date from the table and the population of the jurisdiction rounding that number to the nearest thousand. Then, on any calculator enter the number of deaths plus three zeros, and then divide that number by the rounded population minus the last three zeros of that number. The result will be a good approximation of that jurisdiction's death per million.
Using San Marino as an example: enter its CV-19 deaths (42) plus three zeros (42000), then divide it by its rounded population (34,000) minus three zeros (34), and the result will be its DPM (1235) plus or minus a couple of Ds. Easy.
4. Where the US Ranks
If you go back to the worldometers.info list of countries, and if you - I think, properly - ignore the entry for San Marino, you'll find only there is currently only one country in the world with a number of deaths per million people exceeding 1,000. That's Peru, with a DPM of 1,028.
However, if you click on the United States and review the state-by-state data, you''ll find seven states with DPMs higher than 1,000: New Jersey (1,847), New York (1,725), Massachusetts (1,426), Connecticut (1,284), Louisiana (1,252), Rhode Island (1,111), and Mississippi (1,094). That means that if our states were actually independent countries, we would have the seven worst DPMs in the world.
B. European DPM Maps
A. DPMs in General
1. Introduction.
If you look at the list of data by nations in the worldometers.info site I mentioned above,
https://www.worldometers.info/coronavirus/
you'll find my favorite column in that table, "Deaths/1M Pop." That stands for "deaths per million people", and it gives us an accurate, population-adjusted comparison statistic with no further effort. If you're looking at that site on a phone, you'll probably have to turn your phone to "landscape" and compress the see that column because it's on the right-hand side of the table.
I abbreviate "deaths per million" to DPM, and if you scroll down that column you find that the highest DPM belongs to little, lovable San Marino, a former Italian city-state on the top of a ridge between Florence and Rimini. San Marino has a tiny population of about 34,000 but it suffered 42 deaths in the initial surge of the First Wave, giving it a DPM of 1235 - the worst in the world on that list. However, as a point of reference, San Marino has not had a CV-19 death for five months - meaning it GOT TO ZERO! - so, it won't get pinged by me.
By the way, San Marino looks like a great place to visit when this thing lightens up, and here's a five-minute video by Rick Steves on that topic:
www.youtube.com/watch?v=eSqoEhWd9cM
2. Trailing Indicators
And San Marino's DPM total points out two interesting attributes about DPMs and all CV-19 death statistics, for that matter - they are all "trailing indicators", and they are all cumulative in nature. (More text coming soon.)
3. Finding And Calculating DPM's
On worldometers.info can click on any country and go to its own webpage. If you click on the United States, you'll find a table listing all the states with DPM calculations provided for each. And if you click on the individual states you will get a list of all the counties in that state, but that list will not include DPMs, so you'll have to calculate the relevant ones for yourself. You're going to make that calculation a lot, and there may be an app for it, but here's a simple method I use.
Find out the exact number of CV-19 deaths to date from the table and the population of the jurisdiction rounding that number to the nearest thousand. Then, on any calculator enter the number of deaths plus three zeros, and then divide that number by the rounded population minus the last three zeros of that number. The result will be a good approximation of that jurisdiction's death per million.
Using San Marino as an example: enter its CV-19 deaths (42) plus three zeros (42000), then divide it by its rounded population (34,000) minus three zeros (34), and the result will be its DPM (1235) plus or minus a couple of Ds. Easy.
4. Where the US Ranks
If you go back to the worldometers.info list of countries, and if you - I think, properly - ignore the entry for San Marino, you'll find only there is currently only one country in the world with a number of deaths per million people exceeding 1,000. That's Peru, with a DPM of 1,028.
However, if you click on the United States and review the state-by-state data, you''ll find seven states with DPMs higher than 1,000: New Jersey (1,847), New York (1,725), Massachusetts (1,426), Connecticut (1,284), Louisiana (1,252), Rhode Island (1,111), and Mississippi (1,094). That means that if our states were actually independent countries, we would have the seven worst DPMs in the world.
B. European DPM Maps
| map_of_european_dpms_10-24-2020.pdf |
| map_of_european_dpms_10-31-2020.pdf |
| map_of_european_dpms_11-7-2020.pdf |
| map_of_european_dpms_11-14-2020.pdf |
| map_of_european_dpms_11-21-2020.pdf |
C. US DPM Maps
| map_of_us_dpms_by_states_10-24-20.pdf |
| map_of_us_dpms_by_states_10-31-20.pdf |
| map_of_us_dpms_by_states_11-7-20.pdf |
| map_of_us_dpms_by_states_11-14-20.pdf |
| map_of_us_dpms_by_states_11-21-20.pdf |
D. Washington State DPM Lists and Maps
| washington_county_dpms_on_11-7-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_11-7-20.pdf |
| washington_county_dpms_on_11-14-2020_-_alpha_list.pdf |
| map_of_washington_county__dpms_11-14-20.pdf |
| washington_county_dpms_on_11-21-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_11-21-20.pdf |
IV. MY WEEKLY COVID-19 RESEARCH REPORTS
Below are my analyses of Covid-19 death statistics for the last twenty weeks. To be consistent, I collect data every Saturday morning using data published via https://www.worldometers.info/coronavirus/. Then I crunch the numbers over the weekend, and I'm ready to publish the summary on Monday. I picked the jurisdictions - cities, counties, states, and nations - based on their relevance and their illustrative properties. So, if your favorite jurisdiction isn't listed, please don't feel slighted. As I note below, great data are available worldwide, and you can find them all via websites like https://www.worldometers.info/coronavirus/
B. Reports
Note: In https://www.worldometers.info/coronavirus/usa/, data breakouts for the sub-jurisdictions of individual US states are by county, rather than by city. For example, in https://www.worldometers.info/coronavirus/usa/new-york/ there is no separate entry for New York City, but there are entries for each of the five boroughs (counties) that make up New York City. For clarity, I have listed the five boroughs individually by their common names along with combined totals for the entire City of New York. If you see those data listed elsewhere, remember that Brooklyn is actually Kings County and Staten Island is actually Richmond County.
Note: In https://www.worldometers.info/coronavirus/usa/, data breakouts for the sub-jurisdictions of individual US states are by county, rather than by city. For example, in https://www.worldometers.info/coronavirus/usa/new-york/ there is no separate entry for New York City, but there are entries for each of the five boroughs (counties) that make up New York City. For clarity, I have listed the five boroughs individually by their common names along with combined totals for the entire City of New York. If you see those data listed elsewhere, remember that Brooklyn is actually Kings County and Staten Island is actually Richmond County.
Week 27: Data collected on Saturday, 21 November 2020
| analysis_of_covid-19_deaths_-_21_november_2020.pdf |
Week 26: Data collected on Saturday, 14 November 2020
| analysis_of_covid-19_deaths_-_14_november_2020.pdf |
Week 25: Data collected on Saturday, 7 November 2020
| analysis_of_covid-19_deaths_-_7_november_2020.pdf |
Week 24: Data collected on Saturday, 31 October 2020
Note: This is the first week of a new format for my weekly summary. I think that you'll find a clearer
distinction between the Italian and Wuhan Variants in Europe and the Northeastern USA.
Note: This is the first week of a new format for my weekly summary. I think that you'll find a clearer
distinction between the Italian and Wuhan Variants in Europe and the Northeastern USA.
| analysis_of_covid-19_deaths_-_31_october_2020.pdf |
Week 23: Data collected on Saturday, 24 October 2020
| analysis_of_covid-19_deaths_-_24_october_2020.pdf |
Week 22: Data collected on Saturday, 17 October 2020
| analysis_of_covid-19_deaths_-_17_october_2020.pdf |
Week 21: Data collected on Saturday, 10 October 2020
| analysis_of_covid-19_deaths_-_10_october_2020.pdf |
Week 20: Data collected on Saturday, 3 October 2020
| analysis_of_covid-19_deaths_-_3_october_2020.pdf |
Week 19: Data collected on Saturday, 26 September 2020
| analysis_of_covid-19_deaths_-_26_september_2020.pdf |
Week 18: Data collected on Saturday, 19 September 2020
| analysis_of_covid-19_deaths_-_19_september_2020.pdf |
Week 17: Data collected on Saturday, 12 September 2020
| analysis_of_covid-19_deaths_-_12_september_2020.pdf |
Week 16: Data collected on Saturday, 5 September 2020
| analysis_of_covid-19_deaths_-_5_september_2020.pdf |
Week 15: Data collected on Saturday, 29 August 2020
| analysis_of_covid-19_deaths_-_29_august_2020.pdf |
Week 14: Data collected on Saturday, 22 August 2020
| analysis_of_covid-19_deaths_-_22_august_2020.pdf |
Week 13: Data collected on Saturday, 15 August 2020
| analysis_of_covid-19_deaths_-_15_august_2020.pdf |
Week 12: Data collected on Saturday, 8 August 2020
| analysis_of_covid-19_deaths_-_8_august_2020.pdf |
Week 11: Data collected on Saturday, 1 August 2020
| analysis_of_covid-19_deaths_-_1_august_2020.pdf |
Week 10: Data collected on Saturday, 25 July 2020
| analysis_of_covid-19_deaths_-_25_july_2020.pdf |
Week 9: Data collected on Saturday, 18 July 2020
| analysis_of_covid-19_deaths_-_18_july_2020.pdf |
Week 8: Data collected on Saturday, 11 July 2020
| analysis_of_covid-19_deaths_-_11_july_2020.pdf |
Week 7: Data collected on Saturday, 4 July 2020
| analysis_of_covid-19_deaths_-_4_july_2020.pdf |
Week 6: Data collected on Saturday, 27 June 2020
| analysis_of_covid-19_deaths_-_27_june_2020.pdf |
Week 5: Data collected on Saturday, 20 June 2020
| analysis_of_covid-19_deaths_-_20_june_2020.pdf |
Week 4: Data collected on Saturday, 13 June 2020
| analysis_of_covid-19_deaths_-_13_june_2020.pdf |
Week 3: Data collected on Saturday, 6 June 2020
| analysis_of_covid-19_deaths_-_6_june_2020.pdf |
Week 2: Data collected on Saturday, 30 May 2020
| analysis_of_covid-19_deaths_-_30_may_2020.pdf |
Week 1: Data collected on Saturday, 23 May 2020
| analysis_of_covid-19_deaths_-_23_may_2020.pdf |
Copyright 2020, Mark Warns, All Rights Reserved
As on every page of the ACG website, here are Word versions of my Apple-to-Apples templates for finding out - before you ever apply to any college anywhere - your expected college costs and loans. There's also a Word version of my blank data input pages that you can download for use by your own family. Follow the instructions on each form, and they're all you really need to find affordable colleges.
| acg_apples-to-apples_stabilized_net_cost_comparison.docx |
| acg_apples-to-apples_expected_annual_loan_template.docx |
| acg_2019-2020_family_npc_input_data.docx |