Two New Earth-Like Planets Discovered

While the rest of the world was sleeping, I have been building a space ship. This autonomous ship has recently explored space near our Sun at a distance of Earth’s orbit. This exploration has been a remarkable success.

The ship has discovered two new planets! These planets orbit our Sun in exactly Earth’s orbit. And, it appears they can host life.

You may be skeptical of these claims. That’s understandable. But, pictures don’t lie. I dub the first planet discovered, Oceania. Behold:

The newly discovered planet, Oceania.

Oceania is an ideal planet for fish.

I dub the other newly discovered planet, Landia. Terrestrial animals and plants will find Landia an ideal planet, as you can see clearly:

The newly discovered planet, Landia.

These discoveries promise to change the course of not only history, but of life on Earth, itself.

Covid 19 Does Not Specially Target Old Folks

In my mind’s eye, I thought of Covid 19 as specially targeting the old.

We all know stories of nursing homes packed with sick old folks. But nursing home residents succumb to any sickness more easily than, say, high school kids.

So, is Covid 19 going after old folks in particular, or is Covid 19 just one more way to be killed?

Let’s find out.

The CDC has data for all deaths in the US by week and by age groups. Let’s graph that data in a stacked area graph.

This stacked area graph shows horizontal bands, one for each of several age groups. Thick bands have higher numbers of deaths, thin, lower.

If Covid 19 specially affects old folks, then the bands for older age groups should get thicker during March and April when most US Covid 19 deaths happened.

Note: The unlabeled age groups in this graph are:

    45-54 years
    35-44 years
    25-34 years
    15-24 years
    5-14 years
    1-4 years
    Under 1 year.

A consistent quarter of all deaths are people 85 and up. A little less are in the 75 to 85 age group. And so on.

WARNING! THE SLOPE ON THE RIGHT IS CREATED FROM MISSING DATA. In the US, it takes time for notifications of deaths to get to the CDC. Raw numbers for recent weeks are always low. One thing this graph shows, therefore, is that death reports for 85+ people get to the CDC faster than others.

This graph makes it appear that Covid 19 really has no differential affect on different age groups.

But, is it misleading?

Have you noticed graphs that purport to show information about Covid 19 almost always cut off shortly before Covid 19 was a factor in the US? This one does, too. Uh, huh, Robinson, remove the baseline context from your graph, you sneaky devil, you. Well, is that a problem here?

Well, the CDC also has death-by-age information going back to the beginning of 2015. Slightly different age groups as in the graph above, but good enough for a picture. Let’s look:

The unlabeled age groups are:

    25-44 years
    Under 25 years.

Judging by this graph, if any age group has been hit harder by Covid 19, it’s the 45-64 year group! But look at the first, zoomed-in graph before jumping to that conclusion.

If you squint, you can see that 85+ people tend to die slightly more often in the winter than in the summer. There tends to be a time – anywhere from October out to March – when 85+ people are hit the hardest. That’s likely to be the various flu seasons doing what they do. They come early. They come late. They come once or twice or not at all. They vary.

I do not know what’s going on with the ramp-up on the left – early 2015. Looks like a particularly bad season to be 85+. But, it’s on the edge of the graph, so …?

OK. That’s about it. I was thinking wrong. There’s nothing special about how Covid 19 affects old folks.


JSON data behind the graphs (Do not hit these URLs unless you know what you are doing. Your browser may not handle them well.):

JSON 2020 Feb thru mid Aug weekly US deaths by age group

JSON 2015-2020 weekly US death counts – by state and age group – 60 megabytes

A new Kinsa fever movie

Here is another movie generated by my Kinsa fever data display program.

This video uses color to show which US counties have similar Kinsa fever thermometer statistics. This particular video colors counties with recent (previous 15 days) higher-than-other-county-fever-percentages in red tones, less recent high percentages in green tones, and high percentages older than 45 days in blue.

Watch red to see how fever moves around the country over time. Watch for blue to see counties that have had fever, but not for a while. Green counties were feverish around a month before the end date.

During the video, the end date runs from mid-March to August 7th.

Where Kinsa says it’s getting hot

Here is the current US map with green showing the highest percentage of feverish people in March and April, blue showing May, and red showing June so far, to theĀ 8th.

Notice California (e.g. Alameda County)

and Florida (e.g. Pasco County)

seem to be heating up in May/June (red and blue – orange), and western Utah (e.g. Beaver County)

has come alive in the last, red week – June, that is. I wonder if anything is going on in these places.

By way of contrast, consider most of Texas (e.g. Nolan County)

which was particularly feverish (compared to other places in the US) only in May. That drop-off in Nolan County, Texas is peculiar, too. Neighboring counties have a similar, but not so dramatic drop-off. Probably some peculiarity of data processing. … Or is it? Dum, dum, dum, dummm. Suspense!

I learned something about keyboards

As it turns out, the empty space between the Alt and Ctrl keys seems to be there for a reason:

The side of the hand needs room here.

On a real keyboard, one with real key travel, if you don’t put your hands on some kind of padding, the meaty side of your right hand will fit in this space!

I learned this when, on a whim and with only one usable keyboard left (a sad Model M) I picked up a little 82-key AJazz AK33 board. This board sports the modern world’s unserious attempt at approximating buckling keys – Cherry Blue-esquers. But, hey, it’s small. Or something. And lighted. And has some key roll-over.

Ajazz AK33 with no slot for the right hand to fit in to.

But it didn’t work for me. Turns out, my right hand would push the left arrow key at random – usually when hitting a burst of keys.

Luck was good, though. I got one of my ~30 year old NMB buckling spring boards working again. They aren’t the best keyboard ever made (i.e. an IBM AT board), but they are up there.

Culture Change on Display

Culture-tied snippets from old media are fun and informative.

Consider the ad in which “nine out of ten doctors prefer Camels.”

Most old movies are filled with such oddities from customs of the day.

For real fun, look for pairs of things that only make sense paired together in today’s culture. Tomorrow, culture will change. But laugh today.

That all said, I give you a recent screen capture from the south east corner of an Ars Technica page:

Science on display

A New Heartbeat Picture

Over the last few months I’ve had reason to watch my heartbeat using information from a CMS-50D Pulse Oxi device. When all is normal, a display from a web app I’ve written to show the Pulse Oxi’s output shows something like this:

Normal heartbeat 12/29/2017 13:51:55

All well and good.

But, currently, if I put my heart under a bit of load, it doesn’t just speed up as it should. Instead, it does something like this:

Loaded heartbeat 12/29/2017 13:46:18

OK. What is “this”?

“This” is the pattern of 1 over-sized beat followed by 3 to 5 fast, weak beats followed by a delay of a beat of so. This is an unpleasant, very breathless pattern.

The thing is, this pattern isn’t all that’s going on. And, it raises the question, “What about a longer view?” The waveform display shows about 10 seconds of heartbeat. That’s great for general use, but you need to watch the display carefully to see trends and changes that happen over 10’s of seconds, let alone minutes.

A normal waveform display is also unsatisfactory in another way: The beats-per-minute number lags. In practice, BPM displays tend to be averages over 15 or more seconds. Fine for when all is well, but easily misleading when not.

So, examine the smaller graph in each of those two screen shots.

It shows a histogram of the last 120 peak-to-peak durations in beats per minute. Both the low and high peaks are counted, so this histogram shows the last 60 heart beats. The first graph above shows each half-beat has been plus or minus 5 or so BPM of the average heart rate. Normal.

Histogram details:

Tall lines show where many peak-to-peak durations have been at a single heart rate.

Short lines show heart beats of rarely seen durations.

Each peak-to-peak half-beat in the histogram is given a short line segment. Fully colored segments are recent beats. Faded segments are older beats. Segments are stacked in each beat-per-minute “bin”, oldest first, to make the histogram.

Ticks are painted at 10 BPM intervals along the bottom.

The yellow line marks the current, average heart rate. It is usually close to the tallest area of the histogram.

The second graph above shows when the heart does not beat consistently – misses or adds beats.

This histogram says, “Look carefully at this heart.”

Over time, you can watch a heart go in and out of sync. And it’s fun to watch the heart speed up and slow down, what with the columns marching back and forth on the histogram.

Here is a screen shot of when the heart beat was bad, but is now looking good:

Improving heartbeat 12/29/2017 13:47:08

Anyway, it’s been amusing to play with this stuff. And such a FIFO histogram could certainly be used in other applications.

A real-time display of either my current heart beat or a random, historical recording is at Alex’s Pulse – if the special, heartbeat server is running … which it rarely is, given the bandwidth this server consumes.