A teenager I know is training for the Olympics - in 2024. He's one of the US's Top 100 swimmers. For inspiration he follows the best, Michael Phelps. We should follow the best, too, as we work to measure, manage, and ultimately conquer our Covid crisis.
It is unfortunate that our Main Stream Media (MSM) floods us daily with woeful headlines of Covid (CV) cases, hospitalizations, and deaths that people both in the US and abroad would be forgiven for thinking nothing is being done right in America, that there are no inspiring examples of excellence to be followed. But there are and you will learn how to find them. It's hard not to wonder what would happen if the MSM gave as much attention to our CV successes as given to our CV disappointments.
One way to identify CV excellence is to group our states into peer groups. I sort them into five Groups, based upon their population density, ie, the average number of people per square mile (PPSM). Why density? Because there is a general trend (with explainable exceptions) showing that as a state's Density increases so does its Covid Death Rate (ie, deaths as a percentage of its population.)
Our lowest density state, Alaska, for example, has 1 PPSM. And it's Death Rate (DR) during the 23 weeks to Aug 11 was of 0.0036%. In contrast, our highest density state, New Jersey, has 1,018 PPSM and a DR of 0.1796%. Translating these percentages into conversational English, for every 10,000 citizens in Alaska, Covid caused the death, on an annualized basis, of just less than one person. This compares to New Jersey's annualized rate of 36 deaths for its typical town of 10,000. Obviously, comparing dense New Jersey to sparse Alaska with vastly different death rates is of little value. Better to compare states of similar density and death rates - and see which states are best and worst on different measures and then discover why.
If you look at Table 1, following, you'll see Table 1A is for the 17 weeks, March 3 - June 30; Table 1B for the 6 weeks, July 1 - August 11; and Table 1C combines both tables giving the results for all 23 weeks.
March 3 was chosen as the first day of these CV tables for two reasons: Wikipedia tells us it was just a few days earlier, on February 29, that the US suffered its first CV death (near Seattle, Washington); second, Super Tuesday was on March 3, the day when 14 states, including the two most populous, turned out to vote in the Democratic Party Primaries. It was the nation's last nationwide "turnout" of people before restrictions (CV lockdowns, etc) started appearing.
Table B is included to let us see what's happened since July 1 when some states like Texas (see #29) and Florida (#43) had big "flare-up CV hot spots" (cols. I and M are indicators) while, at the same time, other states like New York (#44) and New Jersey (#50) moved past peak CV activity which resulted in their infected citizens recovering in such high numbers that col. M's Active Cases/Total Cases ratio turned negative (see Table 5B).
That last statement may be a bit tricky to understand so let's look at the data for two very different states, Florida and New York. In the 6-week period FL had a huge increase in cases while NY had a huge reduction. So FL's Active Cases/Total Cases % increased further while NY's went negative. This is seen in the table below, in the average-per-week section for NY. In the 17 weeks to June 30, NY was averaging 24,575 new cases per week. This dropped dramatically to 5,541 per week after June 30. New people getting infected dropped dramatically while, at the same time, people getting cured from the virus skyrocketed. Combined that meant the number of NY's Active Cases dropped heavily in the 6 weeks beginning July 1.
Green and Purple Shadings
To help you quickly identify who did well in the 17 weeks to June 30 and/or the following 6 weeks to August 11 and/or over the whole 23 weeks, look at the states with green-shaded numbers. Green signifies they were the best in their Group in one (or more) of four critical performance measures:
- Col. J - Deaths/Cases %. This tells us what percentage of cases (ie, citizens infected) later died from the virus. Obviously, the lower the number the better. Currently, we don't know with certainty why some states have lower numbers than others. Is it because a state identifies infected people earlier through a larger proportion of testing or offers greater encouragement to come forward and be tested? Is it because of better health in the state (lower levels of obesity, etc)? Or is it because of better care and guidance when someone is found to be infected? Or better hospital and ICU treatment?
- Col. K - Deaths/Population % (aka Death Rate). This is the ONE critical comparative number. It's each state's bottom line showing the percentage of citizens who have died of CV.
- Col. L - Cases/Population %. This number tells us what percentage of the state's population has been infected by Covid-19. It's a reading that tells us the strength of the state's "front line" of defence.
- Col. M - Information. This is a bonus indicator. A high number suggests a high inflow of new cases and, therefore, a possible increased need for hospital beds.
Each green shading indicates the Group's best in each of those four areas of excellence. Often it is because of great leadership. But sometimes a state has been dealt a good hand. Hawaii (#31), for example, is an island state and was able to impose an effective quarantine against visitors, which lowered virus infections dramatically. States with results close to the 'green belt' achievers are also worthy of study. My observation is that there are about three "star" states in each Group.
Conversely, the purple shadings are the lowest performance scores in each of the four categories mentioned. We can learn from them what practices to avoid.
A detailed explanatory discussion on individual green and purple states appears in an earlier article titled Covid 19: Why Some States Have a Lower Death Rate. It is easily accessed at http://www.brianwoolf.com under the Covid tab.
An Additional Way to Find Excellence
Apart from shrieking MSM headlines, the primary publicly-available, updated-daily, Covid data feeds available are Year-to-Date (YTD) tables. They provide wonderful big-picture information but it doesn't tell us what's been happening lately. Louisiana (#24), for example, was hit badly with Mardi Gras CV cases and deaths at the start of the crisis. That one-time hit has impacted its YTD numbers ever since. To find out how it has fared in recent times we need a separate table showing results for, say, the past 6 weeks.
One goal of this article's 3-part tables is to see if the additional information does help us know which states are faring better or worse currently. Such knowledge leads, of course, to better decision making.
And, yes, the results of the most recent six weeks (Jul 1 - Aug 11), compared to the prior 17 weeks of the crisis (Mar 3 - Jun 30), does show, for example, the magnitude of various CV changes. Eight states recorded average weekly case additions of at least 500% (5-times) more in the recent 6 weeks over the prior 17 weeks while, on the other hand, 8 other states emerged from peak virus conditions with reductions of 31-77% in their average weekly case total...
The Following Pages
On the following pages are Tables covering the other 40 US states' Covid results.
Reviewing all five tables, you will find great diversity in the various state results. And in their practices if you care to separately research how different states are tackling the Covid challenge. It's my hope that the best practices of each state will be copied by others to help minimize the overall hurt to our health, society, and economy that Covid-19 brings.
For the record, CV's scorecard through August 11, 2020: across the 50 states' 1.6% of the population has been infected ('cases'). 3.2% of that 1.6% ended in death, meaning a CV death rate of 0.05% (5 one-hundredths of 1%) of the population.
The intent of this article was to:
· Design a report that captures key Covid metrics in a simple-to-read format
· Include a section covering recent results (like a monthly P&L)
· Incorporate an easy visual method of highlighting good and bad results
I hope those goals, in your mind, were achieved.
Besides a full business life in retailing, and later, loyalty marketing, the other part of Brian Woolf's life has been filled with diverse interests: particularly speaking (including Toastmasters), travel (including all seven continents), and reading (including history). And he has written seven books sharing what he has learned along the journey. Ask him, two favorite trips? Antarctica and the Nile. Ask him, two favorite books? The Lessons of History (Will & Ariel Durant) and Over the Edge of the World (Laurence Bergreen). He loves learning and sharing.