An Interesting Look at COVID-19 Data

If you don’t know, and who couldn’t possibly know, there are sites all over the web showing us the latest data on the corona virus, in all different ways using raw figures, estimates, and graphic presentations.

I decided to take a different kind of look at the data and I’ve found some interesting points.

First, let’s point out all the locations you can find coronavirus data.

This first one is a lot of fun. It’s a COVID-19 Event Risk Assessment Planning Tool. If you are planning on visiting some place, or attending a wedding, going to a movie, or just a family gathering, you can use this tool to determine the risk (from 0 to 100%) that someone in your gathering will have the virus.

For example, I chose my county, Isanti in Minnesota, and then I plugged in a group of 15 people. Then you get to choose the fudge factor, either 5 or 10. You see, because of how we test and who gets tested, there can be 5 times more active cases (even without symptoms) than actually ascertained. And there could be 10 times more cases. So, you get to choose which, 5 or 10.

Once you’ve plugged all that in, just click your county. I got this:

So, it’s about a 50/50 chance that someone in the crowd of 15 will have the bug. This is why social distancing and masks are recommended. There’s a chance, and you don’t want to be giving it to grandma.

The next place is quite simple. Simply google “covid-19 deaths” and you’re there. You can change the different variables and see the counts.

For COVID-19 Projections, go here: https://covid19.healthdata.org/united-states-of-america?view=total-deaths&tab=trend

You can plug in any country, click on a date in the future (I’m sure many have chosen January 20th as their specific date), and at your cursor will appear three numbers.

• Mandates Easing, in red.
• Current Projection, in brown.
• Universal Masks, in green.

As you can see, statistics show that masks work, even when doctors and so-called experts are all over YouTube telling you they don’t. Yes, the bug can go thru a mask, but still we must trust the statistics, which show masks work. End of story.

Next is Tracking Our Covid-19 Response at: https://www.covidexitstrategy.org/

Currently, only two states are “Trending Poorly.” Is this a good thing? Not really. Not when the rest of the states have “Uncontrolled Spread.”

And finally, there is the Worldometer. You can start with worldwide stats by country, and sort on any of the headings, such as Deaths Per 1 Million, or you could click on a country and view stats by state, and again, sort on any of the headings. You can plug in any country and view their statistics, and then the states or provinces within them. Just keep clicking and exploring. Amazing stuff.

However, it was from these stats that I decided to explore an aspect that has personally interested me for some time.

Male vs Female Run Countries

Or, men vs women.

I first copied all the data into an Excel spread sheet and then separated out all the countries run by women. Then I started adding up the averages.

Additionally, I had to do something with Switzerland. It was Sweden that was bound and determined to build Herd Immunity and when it wasn’t working very well, medical experts (whackos) bought onto this idea in Switzerland, but luckily not for very long. But then they screwed up by allowing unlimited visitors into the country. They’ve really messed up their numbers.

Then there is Belgium, and their numbers are out of sight too. So here’s what we did. We first compared averages between women run countries and countries run by men, but the average in the most important figures, cases per million and deaths per million.

Averages	Cases /1 M	Deaths /1 M
Women	12,748	207
Men	11,678	218

Then we did something that is often done by statisticians, and that is eliminate the top and bottom. We eliminated the top two from each group, and then eliminated the bottom 1 from the female group, and the bottom 10 from the male run countries because there were 15 times more male run countries than female. And you can see the numbers changed rather drastically.

Averages	Cases /1 M	Deaths /1 M
Women	9,285	119
Men	12,371	233

To be fair, let’s add in the top ten best and see how much better male run countries are doing.

Averages	Cases /1 M	Deaths /1 M
Women	9,285	119
Men	12,371	218

So you can see, even with those 10 countries with under 5 deaths per million added back into our spreadsheet, the difference in deaths per million between female run countries and male run countries is still quite drastic.

I think you can see what we’re getting at here.

This pandemic is being handled better by women leaders than by men leaders.

And if you compare their economies, you’ll find the same darn thing.

I guess the Dalai Lama was right.