THIS PAGE REPRESENTS A FRESH APPROACH TO OF COVID-19 DATA.
IT MAY BE EVOLVING INTO A BOOK OR A MASTERS THESIS; ONLY TIME WILL TELL.
I COLLECT DATA EVERY SATURDAY MORNING, AND MY USUAL SOURCE IS:
https://www.worldometers.info/coronavirus
PROCESSING AND UPLOADING THE DATA TAKES A FEW DAYS,
BUT I USUALLY COMPLETE IT BY THE FOLLOWING WEDNESDAY.
FOR NOW, THE MEDIA ARE CONTINUING TO MISS THE BLOWOUT OF CV-19 DEATHS THROUGHOUT EUROPE, AND FROM EAST TO WEST ACROSS THE UNITED STATES.
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 writers.
Best wishes to all, and I hope it helps.
Much of the United States is still mired in the First Wave of the Novel Coronavirus Pandemic of 2020. The disastrous effect of the First Wave varies throughout the United States - subsiding in some areas while reaching new peaks in others. But across our nation, the numbers of new Covid-19 deaths per week in the US has more than tripled since the first of November, indicating that the effect of the Pandemic continues to get much worse.
However, let's remember that there are no "national" Covid-19 cases, sicknesses, and deaths. There are only local cases, sicknesses, and deaths - those happening in our communities or in other communities like ours. And let's also remember that the right number of Covid-19 cases, sicknesses, and deaths in any of our communities is zero. So, let's be vigilant, and let's follow the instructions of our local health authorities to help our communities GET TO ZERO!
However, let's remember that there are no "national" Covid-19 cases, sicknesses, and deaths. There are only local cases, sicknesses, and deaths - those happening in our communities or in other communities like ours. And let's also remember that the right number of Covid-19 cases, sicknesses, and deaths in any of our communities is zero. So, let's be vigilant, and let's follow the instructions of our local health authorities to help our communities GET TO ZERO!
TOP TOPICS FOR THE WEEK OF JANUARY 9, 2021
Note: All my documents and maps have been uploaded from my 1/9/21 data run,
but text changes will be complete when this note disappears.
FIRST THE GOOD NEWS
I. 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 5 DPMs. For purposes of comparison and remembering that DPMs are automatically adjusted for population size, that means that the US total of 1,074 DPMs as of last week was 214.8 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 129,551 more CV-19 deaths from then till my last data run on 1/2/21. 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/
II. EXEMPLARY NEW YORK
New York was one of the states in the US Northeast - along with New Jersey, Massachusetts, Connecticut, Rhode Island, and Pennsylvania - that apparently got hit - hard and early - by the highly lethal CV-19 variant that initially impacted Italy. So, the deaths per million people statistics for all of those states have remained highly elevated as a result, and that points out one of the few problems with DPM statistics.
DPMs are cumulative, so if a jurisdiction has had one death, that death affects its DPM count for the rest of the Pandemic. Now, that doesn't matter over the short haul or if heightened DPMs in a jurisdiction reflect a current problem - like the numbers now coming out of states like Wyoming, Vermont, West Virginia, South Dakota, Kansas, Alaska, and Maine. But, as our Pandemic has lingered past six months, DPM statistics heavily based on a huge increase in CV-19 deaths in the First Surge began to lose relevance. They just didn't reflect the current reality.
So, here's a new weekly map. It shows the percentage increase in deaths per million people for each of the 50 states and the District of Columbia over the last six weeks - which also equals the percentage increase in deaths. And, sorry, you'll probably have to download and rotate it to get a clear view.
Note: All my documents and maps have been uploaded from my 1/9/21 data run,
but text changes will be complete when this note disappears.
FIRST THE GOOD NEWS
I. 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 5 DPMs. For purposes of comparison and remembering that DPMs are automatically adjusted for population size, that means that the US total of 1,074 DPMs as of last week was 214.8 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 129,551 more CV-19 deaths from then till my last data run on 1/2/21. 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/
II. EXEMPLARY NEW YORK
New York was one of the states in the US Northeast - along with New Jersey, Massachusetts, Connecticut, Rhode Island, and Pennsylvania - that apparently got hit - hard and early - by the highly lethal CV-19 variant that initially impacted Italy. So, the deaths per million people statistics for all of those states have remained highly elevated as a result, and that points out one of the few problems with DPM statistics.
DPMs are cumulative, so if a jurisdiction has had one death, that death affects its DPM count for the rest of the Pandemic. Now, that doesn't matter over the short haul or if heightened DPMs in a jurisdiction reflect a current problem - like the numbers now coming out of states like Wyoming, Vermont, West Virginia, South Dakota, Kansas, Alaska, and Maine. But, as our Pandemic has lingered past six months, DPM statistics heavily based on a huge increase in CV-19 deaths in the First Surge began to lose relevance. They just didn't reflect the current reality.
So, here's a new weekly map. It shows the percentage increase in deaths per million people for each of the 50 states and the District of Columbia over the last six weeks - which also equals the percentage increase in deaths. And, sorry, you'll probably have to download and rotate it to get a clear view.
| map_of_us_6-week_deaths_1-9-21.pdf |
Now, the worst numbers on that map speak for themselves, but let's focus on the best one. New York, which has had the second highest number of deaths per million residents of any US state thus far in the Pandemic, has had the lowest increase in actual Covid-19 deaths over the past six weeks nationwide. Wonderful!
But what about the last week?
New York State's CV-19 deaths increased by just 2.74%. Highly effective!
And what about New York City? Here, let's remember that the cumulative DPM statistics for the City have been absolutely huge - in the running for the world's worst - something you can check on Page 1 of each of my 32 Weekly Reports listed below.
The percentage increase in CV-19 deaths last week for New York City was even better than for New York State as a whole. The City's increase was a really good 1.13%, which was only about 41% of the statewide increase, and here's how it broke out by Boroughs:
Manhattan = +1.55%
Brooklyn = +1.06%
Queens = +1.03%
Bronx = +0.59%
Staten Island = +3.29%
So, congratulations to New York, and your people should realize hat they're within reach of getting to zero. In fact, if you students, teachers, and journalists in New York get to work on it, I bet you'll find plenty of counties in New York State that have already gotten to zero, and those counties need to be recognized.
One last thing: US Covid-19 statistics are generally shown on websites by counties, rather than cities. So, for instance, New York City does not have a separate listing in:
www.worldometers.info/coronavirus/usa/new-york/
But what about the last week?
New York State's CV-19 deaths increased by just 2.74%. Highly effective!
And what about New York City? Here, let's remember that the cumulative DPM statistics for the City have been absolutely huge - in the running for the world's worst - something you can check on Page 1 of each of my 32 Weekly Reports listed below.
The percentage increase in CV-19 deaths last week for New York City was even better than for New York State as a whole. The City's increase was a really good 1.13%, which was only about 41% of the statewide increase, and here's how it broke out by Boroughs:
Manhattan = +1.55%
Brooklyn = +1.06%
Queens = +1.03%
Bronx = +0.59%
Staten Island = +3.29%
So, congratulations to New York, and your people should realize hat they're within reach of getting to zero. In fact, if you students, teachers, and journalists in New York get to work on it, I bet you'll find plenty of counties in New York State that have already gotten to zero, and those counties need to be recognized.
One last thing: US Covid-19 statistics are generally shown on websites by counties, rather than cities. So, for instance, New York City does not have a separate listing in:
www.worldometers.info/coronavirus/usa/new-york/
Instead, the five Boroughs that make up New York City are listed individually by their county names. The county names of the Boroughs match their common names except for Brooklyn, "Kings County", and Staten Island, "Richmond County." And you'll have to add the five county totals together to get the City total.
And, while we're at it, "deaths per million" is the standard population-adjusted statistic, but I've never seen DPMs calculated in list of counties. You'll face the same thing, so you'll need to know how to calculate them. There's probably an app, but I've found that doing them on my phone or calculator is easy as well as a good focusing tool.
You'll need to know two numbers for each jurisdiction: its total CV-19 deaths and its population. Start by rounding the population to the nearest thousand. Then divide total deaths plus three zeros by the population minus three zeros. Then round the result to the nearest whole number, and that's your DPM for that jurisdiction then.
With a little practice, it will become second-nature.
And, while we're at it, "deaths per million" is the standard population-adjusted statistic, but I've never seen DPMs calculated in list of counties. You'll face the same thing, so you'll need to know how to calculate them. There's probably an app, but I've found that doing them on my phone or calculator is easy as well as a good focusing tool.
You'll need to know two numbers for each jurisdiction: its total CV-19 deaths and its population. Start by rounding the population to the nearest thousand. Then divide total deaths plus three zeros by the population minus three zeros. Then round the result to the nearest whole number, and that's your DPM for that jurisdiction then.
With a little practice, it will become second-nature.
NOW THE BAD NEWS
I. US COVID-19 DEATHS CONTINUE TO SOAR
US Covid-19 deaths are continuing to increase dramatically, going from 260,628 to 356,616 - an increase of 95,988 in the six weeks ending on Saturday, January 2, 2021 - a 36.8% increase. Weekly CV-19 deaths have gone from 10,329 to 18,625 in just those six weeks, an increase of 80% in weekly deaths. Horrible!
And here's the US national data up to Saturday, 1/2/21:
US Covid-19 deaths are continuing to increase dramatically, going from 260,628 to 356,616 - an increase of 95,988 in the six weeks ending on Saturday, January 2, 2021 - a 36.8% increase. Weekly CV-19 deaths have gone from 10,329 to 18,625 in just those six weeks, an increase of 80% in weekly deaths. Horrible!
And here's the US national data up to Saturday, 1/2/21:
| delta_deaths_usa_1-9-2021.pdf |
And here's my current map of US deaths per million people by states, and you'll see it includes some ugly numbers. Again, you'll probably have to download and rotate it to see it clearly.:
| map_of_us_dpms_by_states_1-9-21.pdf |
For the most current data, or to zero-in on a particular state, check:
https://www.worldometers.info/coronavirus/country/us/
https://www.worldometers.info/coronavirus/country/us/
II. COVID-19 DEATH RATES BLOWING OUT IN EUROPE
The increase in European Covid-19 death statistics that I first noted nine weeks ago, and which had become more general two weeks later, is continuing and its effects are visible from the Atlantic through Russia. Three maps are provided below that illustrate the problem in the affected area. The first two maps show cumulative DPM data for the weeks ending 11/21/2020 and 1/2/2021, the last map shows the change in CV-19 deaths in percent in those jurisdictions for the six-week period from 11/21 till 1/2.
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 several nations experiencing increases over 300% during that period. As an example, the total deaths per million people for Greece went from 136 on November 21th to 473 on January 2nd. That's a stunning increase 248% over those six weeks.
I should note that I've been reviewing Covid-19 death statistics and doing weekly reports on them for the last thirty-three weeks, and all my weekly reports are included below. 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 outlier 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 - usually in the two-digits.
That situation was stable 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.
Here are the two cumulative DPM maps:
The increase in European Covid-19 death statistics that I first noted nine weeks ago, and which had become more general two weeks later, is continuing and its effects are visible from the Atlantic through Russia. Three maps are provided below that illustrate the problem in the affected area. The first two maps show cumulative DPM data for the weeks ending 11/21/2020 and 1/2/2021, the last map shows the change in CV-19 deaths in percent in those jurisdictions for the six-week period from 11/21 till 1/2.
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 several nations experiencing increases over 300% during that period. As an example, the total deaths per million people for Greece went from 136 on November 21th to 473 on January 2nd. That's a stunning increase 248% over those six weeks.
I should note that I've been reviewing Covid-19 death statistics and doing weekly reports on them for the last thirty-three weeks, and all my weekly reports are included below. 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 outlier 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 - usually in the two-digits.
That situation was stable 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.
Here are the two cumulative DPM maps:
| map_of_european_dpms_11-28-2020.pdf |
| map_of_european_dpms_1-9-21.pdf |
And here's the map showing the percentage change in single-jurisdiction CV-19 deaths in Europe over the six weeks from 11/21/20 till 1/2/2021.
| map_of_european_6-week_death_increases_1-9-21.pdf |
Now, 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, open this page:
www.worldometers.info/coronavirus/
Then scroll down to the list of countries and right-click on Ireland and open 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 and early by Covid-19, but the Irish fought the 7-day moving average of their daily CV-19 deaths down to single digits by last summer, and they've kept it there since - an excellent 8 last Saturday, meaning that Ireland can definitely get to zero. And the success of Ireland did not result from magic, blarney, or luck. It resulted from the hard work of the Irish people. Admirable!
Now do the same thing for the other nations hit hard and early by the Italian Variant of Covid-19 - 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 fought the 7-day moving average of their daily CV-19 death counts down the single digits by last August, with some even getting down to zero. But then they quit. Figuratively, each of them took their foot off the brake pedal, and you can see the results. Now their latest daily death counts - as shown by their 7-day moving averages - have ballooned from zero or near zero to as many as 561 DEATHS PER DAY, and here are the individual totals for each nation as of 1/2/2021:
Belgium 70, Netherlands 86, Spain 135, France 299, Italy 466, and the United Kingdom 561. Now, let's account for the population differentials this way: The UK has about fourteen times more people than Ireland. But, if you multiply Ireland's 8 by 14 to make the comparison fair, Ireland still has just 112 daily deaths compared to the UK's 561, a difference of just over 5 times.
The takeaway is obvious. Ireland kept the brakes on, but the other six 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. That was because European travel restrictions ended last summer too, allowing the more lethal Italian Variant of CV-19 to spread from the heavily affected countries throughout the continent.
But I think this unfolding situation can still be stopped 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. DISAPPOINTING NEWS OUT OF CANADA
Sadly, the 14-to-1 East-to-West DPM ratio in Canada reflected in the table below has compressed to 6-to-1 over the last two months, matching the drift-down of that ratio in the United States. The main cause is more deaths in the West where, for example, British Columbia's CV-19 deaths per million people have increase from 49 to 151 in just two months.
IV. TWO UNFORTUNATE MONTHS 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 and in every nation, by people who are absolutely devoted to getting their villages, towns and cities to zero. Zero covid cases maybe, but especially zero covid deaths.
Internationally, I've found that zero is a rare number that's tough to achieve, but two nations stood out in my research, hyper-populous China with its 1.4 billion people and little, lovable San Marino with its 34 thousand. China has not had a single additional CV-19 death for seven months, and San Marino hadn't had one since May 23rd - until mid-November.
San Marino has suffered fourteen new losses since November 18, 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 and open 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 and early by Covid-19, but the Irish fought the 7-day moving average of their daily CV-19 deaths down to single digits by last summer, and they've kept it there since - an excellent 8 last Saturday, meaning that Ireland can definitely get to zero. And the success of Ireland did not result from magic, blarney, or luck. It resulted from the hard work of the Irish people. Admirable!
Now do the same thing for the other nations hit hard and early by the Italian Variant of Covid-19 - 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 fought the 7-day moving average of their daily CV-19 death counts down the single digits by last August, with some even getting down to zero. But then they quit. Figuratively, each of them took their foot off the brake pedal, and you can see the results. Now their latest daily death counts - as shown by their 7-day moving averages - have ballooned from zero or near zero to as many as 561 DEATHS PER DAY, and here are the individual totals for each nation as of 1/2/2021:
Belgium 70, Netherlands 86, Spain 135, France 299, Italy 466, and the United Kingdom 561. Now, let's account for the population differentials this way: The UK has about fourteen times more people than Ireland. But, if you multiply Ireland's 8 by 14 to make the comparison fair, Ireland still has just 112 daily deaths compared to the UK's 561, a difference of just over 5 times.
The takeaway is obvious. Ireland kept the brakes on, but the other six 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. That was because European travel restrictions ended last summer too, allowing the more lethal Italian Variant of CV-19 to spread from the heavily affected countries throughout the continent.
But I think this unfolding situation can still be stopped 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. DISAPPOINTING NEWS OUT OF CANADA
Sadly, the 14-to-1 East-to-West DPM ratio in Canada reflected in the table below has compressed to 6-to-1 over the last two months, matching the drift-down of that ratio in the United States. The main cause is more deaths in the West where, for example, British Columbia's CV-19 deaths per million people have increase from 49 to 151 in just two months.
IV. TWO UNFORTUNATE MONTHS 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 and in every nation, by people who are absolutely devoted to getting their villages, towns and cities to zero. Zero covid cases maybe, but especially zero covid deaths.
Internationally, I've found that zero is a rare number that's tough to achieve, but two nations stood out in my research, hyper-populous China with its 1.4 billion people and little, lovable San Marino with its 34 thousand. China has not had a single additional CV-19 death for seven months, and San Marino hadn't had one since May 23rd - until mid-November.
San Marino has suffered fourteen new losses since November 18, 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
THIS PANDEMIC CAN ONLY BE BEATEN LOCALLY,
SO LET'S GET TO ZERO HERE AND NOW!
SO LET'S GET TO ZERO HERE AND NOW!
INTRODUCTION TO THIS WEBPAGE
I. MY PURPOSE AND MY BACKGROUND
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 - and how well or poorly local jurisdictions responded to it - will be based on the number who didn't get better, the number who passed away.
I present the data I collect in two ways, first with reference to a commonly known American city/county as a gauge - the City and County of Philadelphia, Pennsylvania on the American East Coast and in Europe and the City and County of San Francisco, California 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. And I'm even capable of occasionally complimenting Socialists.
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 will only have buckets of doubt.
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 - and how well or poorly local jurisdictions responded to it - will be based on the number who didn't get better, the number who passed away.
I present the data I collect in two ways, first with reference to a commonly known American city/county as a gauge - the City and County of Philadelphia, Pennsylvania on the American East Coast and in Europe and the City and County of San Francisco, California 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. And I'm even capable of occasionally complimenting Socialists.
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 will only have buckets of doubt.
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 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.
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 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.
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 |
NOTE: I renewed my Canadian data on 12/23, and the news wasn't good. Deaths per million people in Quebec Province had increased from 704 to 909 in the two months since 10/18/2020. That was an increase of 29%, but it still left Quebec a little better than Pennsylvania, the state with the lowest DPMs in the hard-hit American Northeast.
More interesting were the DPM increases in British Columbia and the Prairie Provinces. British Columbia's DPMs had increased 208%, Alberta's 203%, Saskatchewan's 405%, and Manitoba's 1376%. Meanwhile the DPM ratio between Quebec and British Columbia had dropped a lot, from 14-to-1 to 6-to-1. That correlation is now in line with the current American Northeast to West Coast correlations which you'll see below. Obviously, this is not good news for Canada, and I'll update the next text section along with its table soon.
More interesting were the DPM increases in British Columbia and the Prairie Provinces. British Columbia's DPMs had increased 208%, Alberta's 203%, Saskatchewan's 405%, and Manitoba's 1376%. Meanwhile the DPM ratio between Quebec and British Columbia had dropped a lot, from 14-to-1 to 6-to-1. That correlation is now in line with the current American Northeast to West Coast correlations which you'll see below. Obviously, this is not good news for Canada, and I'll update the next text section along with its table soon.
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 the west on the left and the east on the right.
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 the west on the left and the east on the right.
| 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 1,862 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 weren't generalized throughout the state, they were 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 those 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 1,862 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 weren't generalized throughout the state, they were 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 those 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 January 2, 2021, while comparing it with the 26 previous weeks. You'll see that, as of 1/2, the US had sustained 356,616 CV-19 deaths, an increase of 18,625 deaths from the previous week. US total deaths per million residents as of 1/2 were 1,074 DPMs, an increase of 55 from the previous week.
For purposes of comparison, China had sustained total of 4,634 Covid-19 deaths as of 1/2, and China had not sustained an additional CV-19 death for the previous seven months. Additionally, China's total deaths per million residents as of 1/2/21 were 3, roughly comparable to Singapore's and New Zealand's 5 DPMs each. China's DPMs were also somewhat better than South Korea (18), Japan (28), and Australia (35) while being somewhat worse than Vietnam's spectacular .4 DPMs.
I should mention here that I only started recording national CV-19 death statistics for the United States in the seventh week of this project for a good reason. Up to that point US death data looked like the graph of Canadian data above, with a big lump in the Northeast and a much smaller lump on the West Coast - 3,000 miles away. So, averaging those lumps had no analytical value.
But all American states had relaxed their CV-19 restrictions by the last week of May, 2020. Cases surged, and deaths surged about six weeks later, around the 4th of July. Essentially, the catastrophe became national then, and national data became relevant.
By the way, that point-of-inflection around the Fourth of July - where the US Northeast vs West Coast distinction began to compress - is obvious in the four "versus tables" below.
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 January 2, 2021, while comparing it with the 26 previous weeks. You'll see that, as of 1/2, the US had sustained 356,616 CV-19 deaths, an increase of 18,625 deaths from the previous week. US total deaths per million residents as of 1/2 were 1,074 DPMs, an increase of 55 from the previous week.
For purposes of comparison, China had sustained total of 4,634 Covid-19 deaths as of 1/2, and China had not sustained an additional CV-19 death for the previous seven months. Additionally, China's total deaths per million residents as of 1/2/21 were 3, roughly comparable to Singapore's and New Zealand's 5 DPMs each. China's DPMs were also somewhat better than South Korea (18), Japan (28), and Australia (35) while being somewhat worse than Vietnam's spectacular .4 DPMs.
I should mention here that I only started recording national CV-19 death statistics for the United States in the seventh week of this project for a good reason. Up to that point US death data looked like the graph of Canadian data above, with a big lump in the Northeast and a much smaller lump on the West Coast - 3,000 miles away. So, averaging those lumps had no analytical value.
But all American states had relaxed their CV-19 restrictions by the last week of May, 2020. Cases surged, and deaths surged about six weeks later, around the 4th of July. Essentially, the catastrophe became national then, and national data became relevant.
By the way, that point-of-inflection around the Fourth of July - where the US Northeast vs West Coast distinction began to compress - is obvious in the four "versus tables" below.
| delta_deaths_usa_1-9-2021.pdf |
B. Tracking the "Covid Gorilla" in the United States (More text coming soon.)
| philadelphia_vs_san_francisco_1-9-2021.pdf |
| pennsylvania_vs_oregon_1-9-2021.pdf |
| new_jersey_vs_california_1-9-2021.pdf |
| new_york_vs_california_1-9-2021.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
| 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_11-21-20.pdf |
| map_of_european_dpms_11-28-2020.pdf |
| map_of_european_dpms_12-5-20.pdf |
| map_of_european_dpms_12-12-20.pdf |
| map_of_european_dpms_12-19-20.pdf |
| map_of_european_dpms_12-26-20.pdf |
| map_of_european_dpms_1-2-21.pdf |
| map_of_european_dpms_1-9-21.pdf |
C. European 6-Week Death Increase Maps
| map_of_european_6-week_death_increases_12-5-20.pdf |
| map_of_european_6-week_death_increases_12-12-20.pdf |
| map_of_european_6-week_death_increases_12-19-20.pdf |
| map_of_european_6-week_death_increases_12-26-20.pdf |
| map_of_european_6-week_death_increases_1-2-21.pdf |
| map_of_european_6-week_death_increases_1-9-21.pdf |
D. 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 |
| map_of_us_dpms_by_states_11-28-20.pdf |
| map_of_us_dpms_by_states_12-5-20.pdf |
| map_of_us_dpms_by_states_12-12-20.pdf |
| map_of_us_dpms_by_states_12-19-20.pdf |
| map_of_us_dpms_by_states_12-26-20.pdf |
| map_of_us_dpms_by_states_1-2-21.pdf |
| map_of_us_dpms_by_states_1-9-21.pdf |
E. US 6-Week Death Increase Maps
| map_of_us_6-week_deaths_12-5-20.pdf |
| map_of_us_6-week_deaths_12-12-20.pdf |
| map_of_us_6-week_deaths_12-19-20.pdf |
| map_of_us_6-week_deaths_12-26-20.pdf |
| map_of_us_6-week_deaths_1-2-21.pdf |
| map_of_us_6-week_deaths_1-9-21.pdf |
F. 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 |
| washington_county_dpms_on_11-28-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_11-28-20.pdf |
| washington_county_dpms_on_12-5-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_12-5-20.pdf |
| washington_county_dpms_on_12-12-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_12-12-20.pdf |
| washington_county_dpms_on_12-19-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_12-19-20.pdf |
| washington_county_dpms_on_12-26-2020_-_alpha_list.pdf |
| map_of_washington_county_dpms_12-26-20.pdf |
| washington_county_dpms_on_1-2-2021_-_alpha_list.pdf |
| map_of_washington_county_dpms_1-2-21.pdf |
| washington_county_dpms_on_1-9-2021_-_alpha_list.pdf |
| map_of_washington_county_dpms_1-9-21.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 34: Data collected on Saturday, 9 January 2021
| analysis_of_covid-19_deaths_-_9_january_2021.pdf |
Week 33: Data collected on Saturday, 2 January 2021
| analysis_of_covid-19_deaths_-_2_january_2021.pdf |
Week 32: Data collected on Saturday, 26 December 2020
| analysis_of_covid-19_deaths_-_26_december_2020.pdf |
Week 31: Data collected on Saturday, 19 December 2020
| analysis_of_covid-19_deaths_-_19_december_2020.pdf |
Week 30: Data collected on Saturday, 12 December 2020
| analysis_of_covid-19_deaths_-_12_december_2020.pdf |
Week 29: Data collected on Saturday, 5 December 2020
| analysis_of_covid-19_deaths_-_5_december_2020.pdf |
Week 28: Data collected on Saturday, 28 November 2020
| analysis_of_covid-19_deaths_-_28_november_2020.pdf |
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 2021, 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 |