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:
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:
These discoveries promise to change the course of not only history, but of life on Earth, itself.
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:
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:
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.):
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.
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 the 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!
This Corona Virus thing is, ignoring dead people, a lot of fun.
Well, because it’s so interesting. The progression of the disease is interesting, the reactions to the disease are interesting, and speculations about the post-virus future are interesting.
One interesting thing is the quantity of blather from the babble-world. Where are exceptions to misstatements, lies, confusion, and overall silliness?
One exception seems to be a company named Kinsa. They sell an Internet connected fever thermometer. $30 and $50. Currently sold out.
But, talk about perfect timing: Kinsa has data for much of the US showing when people were, and are, running fevers. Their data correlates pretty closely to flu season.
So, come Covid19, they moved fast and created https://healthweather.us. This web page shows in color and graphically which counties in the US have been affected by fevers and when, post February 16th.
The good, the bad, the ugly:
The Good: A couple minutes in the Firefox Web Developer says the data underneath the web page is remarkably clean and accessible. (OK, they’ve made breaking changes to the data a couple times in the last few days, but life is tough. Boo. Hoo.)
The Bad: Starting Feb 16? Why not Nov 1, 2019? I know why. But why?
The Ugly: Sorry, Tuco. You’re written out of this script. It’s a pretty web page.
Can the web page and data reveal outbreaks of fever in near real time? Kinsa sure hopes so.
As it turns out, the famously big US Covid19 outbreak (NY city) does show up in Kinsa’s data.
But, it remains to be seen what happens over the next few weeks as people wander out of stay-at-home. Thermometers don’t inherently pay attention to political spin, and don’t inherently serve to confuse. So, I’m rooting for Kinsa.
Now, this is all very nice, but what does it lead to?
Well, look at the orange/red line in the NY County image above. Notice its shape – its profile. Call that shape the “fever profile”.
I was clicking around some counties on the web page and noticed an odd thing: Most counties had a fever profile from February to May that looked like neighboring counties. Like, say, county A had a spike of fevers around March 17th, and so did bordering counties B and C. Not counties two states over, though.
But, sometimes there seemed to be sharp transitions between one county and the next with respect to their curves. Maybe my imagination. Maybe not.
I wanted to see the whole country’s county-time fever profile similarities at a glance. If two counties had a similar fever profile/curve from February through today, the two counties should look similar in a picture. And if their fever profiles were different, they should look different.
So, I whipped up a program to color counties based on the total fever-percentage-of-people numbers in 3 bands of time. E.g. Feb 16 to the end of February. The first half of March. And the third band for mid-March to the present. Then the larger the totals a county has in fever percentages in each band, the brighter a color is. The first band is red. The second, green. And the last, blue.
And, here is a picture of the US with counties colored based on fever percentage profiles as of today:
It looks like if you really didn’t want a fever, you should have been in a dark area – Arizona, New Mexico, or the Knoxville Tennessee area. That latter area is quite the surprise.
And if you like Covid19, you wanted to be in the bright blue (mid-March and later) Florida or the New York City areas. Don’t forget to be old!
If you want the flu (in red February), go north-central (ND, WI, MN, northern MI, … or … Canada?).
If you do like your body hot, go to where the colors are bright: downstate Illinois, Indiana, western Kentucky, and Missouri. Maybe Ohio. Or maybe California! Though in California a hot body could be taken two ways.
Here is a picture based on the fever percentages minus what Kinsaexpected them to be given historical trends:
Bright New York is pretty clearly where the unusual fever has been. And I love the West Virginia hole in the picture. Examining the (Fever – Expected) profile for a county there:
shows they dodged the flu.
Another bright spot I didn’t expect was downstate Illinois. The bright purple says they probably had a bit more flu and Covid19 than Kinsa‘s expectations. Or something.
You want movies? You got ’em:
Percentages of people running a fever stepping “today” from February through May:
All in all, it’s been a fun program to write. It shows the fever profile of the county your mouse hovers over so you can quickly see the profiles of lots of counties in a geographical area. I’ve found that handy and kinda informative.
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:
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:
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.
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:
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.
Have you ever wondered what driving in Lesotho is like?
But now I can say, without having been there, it’s a lot like the altiplano.
You know the altiplano. Well above tree level and not a thing to see after the first glance. But curiously entrancing. People live at this altitude? Well, with lots of thick wool and really red blood … apparently.
Or go a little north to Botswana. Mad Max country. The Australian out-outback. English on the wrong side of the road and miles a miles of … miles and miles.
Dropped to these places by spaceship without knowing where you are? How do you find yourself?
In Holland, such questions are answered immediately. The Dutch apparently even label their bus stops with unique names, available for decoding 24×7 through Google’s super-computer. As are the names of little Estonian villages, in case the spaceship dropped you there.
Click on “Options” and check “Stealth”. Then make sure all the countries are either highlighted or none of them are. Toggle “Options” off. Punch “Go”. Welcome to somewhere in the world’s farmland as shown in Google’s Street View.
Right now, click maps.google.com and click on the street view thingee down in the lower right. It toggles showing where street view covers. Zoom a bit in and you’ll see where the real coverage is. Germany, Austria, India, and China have only isolated 360° images. They are not street-viewed.
Apparently, a popular MapCrunch challenge is to get from your drop point to an airport for a flight home. No outside help. No Googling. No other Internet tools. Just drive and look. I can advise you not to accept this challenge. Airports are few and far between and driving even 30 miles is really, really slow and RSI inducing (make the window small for fast refreshes). Maybe it would be better to just drive to somewhere you can get some food. Restaurant or store or whatever.
I binged for several days on an alternate game: Figure out exactly where you are using outside help. This game can still be slow. You can open the real Google Maps Street View in another window and find the location you’re at by matching the cloud pattern in the sky. Desperate times call for desperate measures. When you’re cruising south on some road in scrub-land Bolivia, not a town in sight, you gotta do something.
You’ll want Flag Finder for when you just arrive and see a rural police station’s flag.
Which way to go at the start? Down hill.
Is the sun north or south? You’re below or above the equator. If it’s to the west, consider driving the same direction as the Google car. He’s headed home for the day.
Satellite dishes point to the sky above the equator.
Driving on the left narrows things down.
The script in signs can peg the country. Korean is easy to spot, for instance.
Signs may be in indecipherable script, but URLs are on those signs. URLs have country codes.
You may even find LAT/LON values on signs. My little LAT/LON interpreter is handy to get to those locations.
Mileposts are give-aways. They have the highway number and, usually, a kilometer-to-some-town/bridge/landmark on them. Go to the direction of the lowest kilometer number when first orienting.
After a while, some things stand out:
New roads. New houses. New factories (P&G in countryside Romania). All over the world.
Cell phone towers everywhere.
Contrasts: Bleak, bedraggled Bulgaria and Kyrgyzstan. Sunny Albania. Thriving Slovakia and Serbia.
Deep red dirt of Uganda.
Frosted Polish trees.
Rebar sticking out of Latin American buildings.
Concrete of the tropics.
Pristine Scandinavian buildings. Red.
Road signs with weirdly chosen destinations. Why is that town on this sign?
Highway numbers that make little sense. But are still able to be found in a Maps window overlooking the country.
Many, many one lane roads, indistinguishable from American bike trails.
If your body has some miles on the odometer, play this game: Erase cell phones and scale the car quality back. What year in America are you looking at? Countryside Panama in 2010 looked like the ’50’s to me.
Ignore country-specific things like languages, zoning laws and building style. Notice the big differences you see between places in the world are rural/urban, not country/country. Cities look the same everywhere. Same stores. Same brands. Same way things are built. Houses are built for the climate and culture. American houses are surrounded by toy farms, for instance. We call them “lawns”. Other places have their oddities. Walls around the house, for instance, are common.
Notice you can’t peg a location by the highway, itself. Yes, guard rails and such do vary, but the road engineers of the world seem to be in close contact. New roads are especially generic. I was sure I’d hit Nevada when I dropped in on a desert highway in Botswana. It took a couple trucks going by (“Anderson Trucking”) before I noticed they were on the wrong side of the road. … The road, whose lines and signs were clearly ‘merican.
In case you think it’s just a couple of odd, Brit holdovers who drive on the left, what with #1 and #3, China and US being right-siders, consider #2 and #4, India and Indonesia on the left.
Final advice: Don’t binge on this thing. After a 34 hour session, I slept fitfully with more than a little pain and porcelain throne facing for 22 hours. And then wrote this advice.