THIS PAGE IS STILL UNDER CONSTRUCTION, BUT NOW IT'S GETTING CLOSE.
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, 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 as it already has this year.
My primary purpose in publishing this page is to help students get an early 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 the pandemic through a different 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 have 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 and parents find really affordable colleges - I dive right in and do the research necessary to get it right.
So, there's 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, 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 as it already has this year.
My primary purpose in publishing this page is to help students get an early 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 the pandemic through a different 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 have 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 and parents find really affordable colleges - I dive right in and do the research necessary to get it right.
So, there's a lot of data on this page, but I think we should start with some basics.
II. LET'S BEGIN WITH HUMILITY
A. The So-Called "Spanish Flu" of 1918
It's called the "Spanish Flu" of 1918, but it would have been 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 covered the horrible effect of the 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 believing that knowing its severity would be bad for public morale and 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.
That pandemic ultimately killed about 675,000 Americans, but it didn't stay in the US. It accompanied our troops overseas, and it ultimately killed between twenty and one hundred MILLION additional people worldwide.
The best recent treatise on that pandemic, The Great Influenza, was written by historian and author John Barry. That book is generally available, but there's probably a line to get it at your school's or city's library. 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:
en.wikipedia.org/wiki/Haskell_County,_Kansas
B. The 2020 Pandemic in China
C. The 2020 Pandemic in Eurasia and Oceania
D.
A. The So-Called "Spanish Flu" of 1918
It's called the "Spanish Flu" of 1918, but it would have been 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 covered the horrible effect of the 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 believing that knowing its severity would be bad for public morale and 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.
That pandemic ultimately killed about 675,000 Americans, but it didn't stay in the US. It accompanied our troops overseas, and it ultimately killed between twenty and one hundred MILLION additional people worldwide.
The best recent treatise on that pandemic, The Great Influenza, was written by historian and author John Barry. That book is generally available, but there's probably a line to get it at your school's or city's library. 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:
en.wikipedia.org/wiki/Haskell_County,_Kansas
B. The 2020 Pandemic in China
C. The 2020 Pandemic in Eurasia and Oceania
D.
III. MY WEEKLY COVID-19 RESEARCH REPORTS
A. Explanation of Method and Format
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/
As the Covid-19 pandemic spread out of China last January and February, 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 media for its 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.
Here, I'll take a pause and mention that throughout your careers you'll see plenty of "multiplier events" where the numerical difference from one event to the next is obviously in multiples - meaning several times and equaling several hundreds of percent. In those cases, I think it's best to not strain to explain multiplier events with "fractional factors" that may only explain twenty, thirty, or forty percent of a thousand percent difference. Instead, be open to the idea that you're seeing something new.
As an example, let's say that you're a primate biologist, that you live in a world where no primates have been observed that are larger than chimpanzees, and that 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, and you've never actually seen this one. But you've seen its footprint, so you know it exists, and you know that what differentiates its vastly increased size from the chimpanzees you've actually seen is not eating more pasta. It's just a different animal. And it needs a name. So, you name it after the forest where you first spotted the footprint. End of pause.
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. After some numerical experimentation, and based solely on the numbers of deaths per million residents it was leaving in its wake, I came to opinion that the multiplier was about twelve 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 north, south and east of those nations throughout the entirety of Eurasia, nearby islands, and even Oceania - with the exception of Sweden with its disastrous laissez faire approach to the Pandemic - was relatively low, and it has remained relatively low to this day. Here, I'll note that there is no statistical evidence that the Italian Variant has affected any of these nations including China itself.
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 Alberta, had the statistical signature of the Wuhan Variant.
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. And, if you use twelve as your variant multiplier or divisor, and then adjust for population differences, you'll find that Philly and Frisco tend to have pretty close numbers. This means that, if you adjust San Francisco's total number of CV-19 deaths to the increased population of Philadelphia and then multiply that number by twelve the result will approximate Philadelphia's current CV-19 death total, or if you adjust Philadelphia's total number of CV-19 deaths to the decreased population of San Francisco and then divide that number by twelve the result will approximate San Francisco's current CV-19 death total. The multiplier/divisor will actually vary between ten and fourteen week to week, but you get the point.
By the way, you'll see that the top performing states - meaning those with the lowest Covid-19 deaths to date - are Pennsylvania to the east and Oregon to the west. You'll also notice the huge variation between and even within states. Here, I concentrated on Washington, my home state. Those numbers are on Page 3, and the news can be very good or it can be awful. For instance, I lumped all the communities on the Kitsap and Olympic Peninsulas of Washington State - the area where I live - as the "Peninsula Counties." So that includes the communities of Gig Harbor and the Key Peninsula in Pierce County along with six full counties: Kitsap, Clallam, Jefferson, Grays Harbor, Mason, and Thurston. That area has a population of about 900,000 and it had a total of only 55 CV-19 deaths as of last Saturday. That gave them a CV-19 death risk so far that's 49% UNDER San Francisco's. But on the downside, Yakima County in central Washington which has a population of 251,000 has had 263 CV-19 deaths. Adjusting for population, that made Yakima County's death risk 777% HIGHER than San Francisco's so far.
As the Covid-19 pandemic spread out of China last January and February, 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 media for its 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.
Here, I'll take a pause and mention that throughout your careers you'll see plenty of "multiplier events" where the numerical difference from one event to the next is obviously in multiples - meaning several times and equaling several hundreds of percent. In those cases, I think it's best to not strain to explain multiplier events with "fractional factors" that may only explain twenty, thirty, or forty percent of a thousand percent difference. Instead, be open to the idea that you're seeing something new.
As an example, let's say that you're a primate biologist, that you live in a world where no primates have been observed that are larger than chimpanzees, and that 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, and you've never actually seen this one. But you've seen its footprint, so you know it exists, and you know that what differentiates its vastly increased size from the chimpanzees you've actually seen is not eating more pasta. It's just a different animal. And it needs a name. So, you name it after the forest where you first spotted the footprint. End of pause.
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. After some numerical experimentation, and based solely on the numbers of deaths per million residents it was leaving in its wake, I came to opinion that the multiplier was about twelve 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 north, south and east of those nations throughout the entirety of Eurasia, nearby islands, and even Oceania - with the exception of Sweden with its disastrous laissez faire approach to the Pandemic - was relatively low, and it has remained relatively low to this day. Here, I'll note that there is no statistical evidence that the Italian Variant has affected any of these nations including China itself.
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 Alberta, had the statistical signature of the Wuhan Variant.
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. And, if you use twelve as your variant multiplier or divisor, and then adjust for population differences, you'll find that Philly and Frisco tend to have pretty close numbers. This means that, if you adjust San Francisco's total number of CV-19 deaths to the increased population of Philadelphia and then multiply that number by twelve the result will approximate Philadelphia's current CV-19 death total, or if you adjust Philadelphia's total number of CV-19 deaths to the decreased population of San Francisco and then divide that number by twelve the result will approximate San Francisco's current CV-19 death total. The multiplier/divisor will actually vary between ten and fourteen week to week, but you get the point.
By the way, you'll see that the top performing states - meaning those with the lowest Covid-19 deaths to date - are Pennsylvania to the east and Oregon to the west. You'll also notice the huge variation between and even within states. Here, I concentrated on Washington, my home state. Those numbers are on Page 3, and the news can be very good or it can be awful. For instance, I lumped all the communities on the Kitsap and Olympic Peninsulas of Washington State - the area where I live - as the "Peninsula Counties." So that includes the communities of Gig Harbor and the Key Peninsula in Pierce County along with six full counties: Kitsap, Clallam, Jefferson, Grays Harbor, Mason, and Thurston. That area has a population of about 900,000 and it had a total of only 55 CV-19 deaths as of last Saturday. That gave them a CV-19 death risk so far that's 49% UNDER San Francisco's. But on the downside, Yakima County in central Washington which has a population of 251,000 has had 263 CV-19 deaths. Adjusting for population, that made Yakima County's death risk 777% HIGHER than San Francisco's so far.
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 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 Richmond County.
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 always on the ACG website, here are Word versions of my Apple-to-Apples templates for your own college costs and expected loans along with 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 |