Castles in the Clouds, weather division: In today’s weather forecast, the computer-generated model shows a giant storm off Japan – shaped like Japan:
Why, yes, I am easily amused.
Recently read Cecil Chesterton’s short biography of his big brother Gilbert Keith. It’s good – Cecil published it anonymously, but he didn’t fool anyone. Both brothers – here, Cecil, and in G. K.’s autobiography – admit they spent their childhood arguing, interrupted only by silly little things like sleep and meals. Man, to be a fly n the wall for those arguments. It is to be noted that they both understood ‘argument’ to be, in the words of Monte Python, a structured series of statements meant to establish a proposition. They both frown mightily on mere disputation and gainsaying.
Cecil died right at the end of WWI, after thrice being sent home wounded, and thrice returning to battle. Thus, G.K. was 45 and Cecil 40 when Cecil died. The love between the Chesterton Brothers is palpable.
Cecil says what one must suspect, just looking at the shelf space the complete works of G. K. Chesterton would occupy: G. K. wrote almost constantly. Yet he never seemed hurried, and would more than gladly while away an afternoon drinking with his buddies or talking with whoever he happened to bump into. He was what previous ages referred to as a man of energy – nuclear-level, in G.K.’s case.
Finally, can’t remember the exact quote or even the author, but here’s something to consider in these days of decline and ruin (and growth and opportunity): Try to change yourself. Then you will understand how hard it is to change anyone else.
Let’s take ourselves on wings of nostalgia as it were and try to help ourselves forget, perhaps, for a while, our drab wretched lives: Let us return to a subject written about here before the world lost its mind. All 12 longtime readers might recall my neurotic obsession interest in California weather. My interest was at first piqued by the incessant harping on and doomsday predictions over what, when looked at objectively, was just typical California weather. Namely: precipitation varies a lot from year to year here in the Golden State. Most years, we get less than average rainfall. Some years, we get a lot more than average rainfall. That’s the pattern evident in the data since there has data to look at.
So, a few years in a row of below average rainfall is not a drought. In any decade, you might get 5, 6, 7 years of below average rainfall, sometimes in a row. Such a pattern seems to simply be the way weather works here on the West Coast, at least since the last glacial maximum ended 10,000 years ago. The existence of California’s extensive system of reservoirs and canals testifies that at some point, some Californians understood that this is the pattern – and built a lot of reservoirs in an attempt to even it out a bit. That these reservoirs are sometimes near empty is a feature, not a bug. If they were always full, that would mean that precipitation around the state was always orderly and consistent. If they were always full, we wouldn’t need them.
Similarly, the three major rivers in the L.A. basin have been turned into concrete lined storm channels. 100 years ago, Angelinos got tired of having their city washed away about every decade, and so made sure the water from the occasional epic storm had somewhere to go. Most years, there will be more skateboarders than water in those channels. But once in a while…
Calling ‘average’ ‘normal’, so that mundane variation become, not ‘below average’, but ‘abnormal’ simply adds to the atmosphere of panic.
So: for the last year, we’ve been hearing about how California had sunk into an unprecedented drought since the epic rain year of 2016/2017 when, you may recall, 200%+ of average rainfall and snowpack nearly washed out the Oroville Dam. the state’s largest reservoir. That ended the then current unprecedented ‘drought’. Before that, the 2005/2006 epic rain year ended another unprecedented drought. And so on, back through the decades. As one remarkably sane meteorologist put it. there are only a few storms between drought and plenty in California.
How are we doing this year? Glad you asked. According to my crazy spread sheet*:
The real accuracy here is probably more in the range of 10 percentage points, rather than the displayed 1/100th of a percentage point -but where’s the fun in that? So, despite the faux accuracy above, we’re really more like something between 70 and 80% of the season average as of today.
Any still here and not drifting into a coma may be interested in the overall pattern of rainfall over time in Contra Costa County, which I’ve determined from other datasets:
Again, while it would be easy (I do it all the time) to come up with a bunch of reasons why it’s wrong to do the math this way, and wrong to mix data from different sets, and so on, it’s also reciprocally hard to come up with any reasons the number would be very off – a bunch of different people calculating rainfall over many years and over a fairly contained and consistent area are not likely to get significantly different results.
The rain season here stretches from July through the following June. The seasonal pattern is something like this: On average, about 16% of total rainfall falls from July through November; about 10% falls in April, May, and June. The other 74% falls in December, January, February, and March.
Using the above as a baseline, as of the end of December, we get on average about 35% of our season total rainfall. This year, we’re at over 200% of expected average rainfall to date so far, and about 75% of the average seasonal total – with the bulk of the rainy season still to come. The Sierra snowpack, the melting of which following summer replenishes many reservoirs, is in a similar state: about 150% of average to date, about 50% of seasonal average.
So, we can stop worrying about the drought for now? Well – no. Unfortunately, it’s not uncommon for the rain and snow to just – stop. A near or completely dry month or two or three, even the peak months, happens regularly. It would be a little unusual if, after a very rainy first half of the season, we got a very dry second half – but hardly unprecedented.
Isn’t this all fascinating? No?
The table is set for a nice 200% year, which would shut up the drought doomsayers for a while, at least. Yet, alas, even only 100% isn’t a sure bet at this point. I’ll keep y’all posted.
*The Contra Costa County Flood Control District maintains a set of 32 rain gages spread across the county. These gages are meant to track current rainfall against a set of “critical antecedent conditions” so as to allow predictions of flooding. The tables on the web page are automatically updated every 15 minutes, allowing the obsessive attentive observer to watch the rainfall spread out across the county in almost real time. These gages are situated at various altitudes and terrain, so that the experts at the CCC Flood Control District can see where the water is piling up and where it will go. I misuse these gages to measure broad rainfall totals, doing a series of logically and mathematically dubious sums and calculations in order to arrive at the magic number you see above – EXACTLY 76.93% of expected seasonal rainfall has, well, fallen so far. Riiiight. Summing up rainfall and averages across a range of gages and then dividing to get percentages – not strictly scientific. I also do averages of averages, which also has its shortcomings. BUT – I tell myself – the situation is such that these iffy methods are probably roughly right. I’m not applying for grant money are trying to whip up some panic here – I just like taking a stab at a broader measure of rainfall.
Everyone is gone except for my mother-in-law, the cat, and me, the spouse and Caboose are out performing various duties and acts of mercy. Mother-in-law is ensconced in her recliner, the cat asleep on her lap. I’m running a old John Wayne movie marathon for her – True Grit just came up – over YouTube. Thus, I am free to ponder this period between storms.
Right now, it’s puffy clouds and a light breeze. Over the last couple days, it rained an inch here – don’t laugh, that’s a pretty good storm by NoCal standards. Approaching from the north east, due in this part of California starting around 7 this evening as it works its way down the coast from Oregon and Washington, is an ‘atmospheric river‘ storm, or a ‘pineapple express‘. We are told to expect 3 to 5 inches of rain overnight, across Sunday, and into Monday.
By Bay Area standards, that’s a LOT of rain. We usually have a season total rainfall through October of about an inch – this one storm is supposed to be several times that. Our ‘drought’ – a couple of below average rainfall years, such as we have had EVERY FEW YEARS THROUGHOUT THE RECORDED HISTORY OF CALIFORNIA was due to global warming climate change, our betters assure us. I’m sure this unusually large, but by no means unprecedented, storm ‘ending’ the ‘drought’ will also be caused by global warming climate change. There’s nothing that thing can’t do!
More seriously, because of decades of mismanagement, we have had a lot of wildfires over the last couple years. A whole bunch of rain all at once is going to cause mudslides. This could get ugly.
California gets about 75% of its rainfall over December, January, and February. If this storm comes through as advertised, we’ll have 1/3 of our seasonal average before the rainy season really gets going. Could be interesting.
And there’s always the possibility of an ARkstorm. We’re high enough up that flooding would not be an immediate problem. It’s just the utter destruction of California’s entire drinking water infrastructure and a good chunk of the power grid, while major parts of the highway system are under water, that could be – inconvenient.
Many other distracting thing are happening at the moment. Further details as events warrant.
In any triangle, if one of the sides is produced, then the exterior angle is greater than either of the interior and opposite angles.
Euclid is a language unto itself. The words are part of the language, but few people, it seems, can understand the words without the diagram, even if they only picture the diagram in their heads. I know I can’t – I immediately construct the picture in my mind, at the very least. Once you’ve got the picture, then the words help you walk through the proof.
But the picture itself doesn’t tell you what you are to prove from it. Those 2 dozen words in italics that describe what the picture is for do that. This picture might be worth a thousand words, but those thousand words don’t include what the picture means.
I’ve mentioned before how I was epically terrible at Greek, back in collage, yet epically great at Euclid. It’s just a knack, and I’ve done little with it, but I was that annoying kid who could just read the proposition, look at the diagram, and, 9 times out of 10, produce the proof without having to look at the text. Other people could glance at the rules for forming verbs in Greek, and just get them, while they were a plate of spaghetti to me. Just one of those things.
I watched the other students struggle their way through Euclid. I never had that experience, the glory, even, that some people had when the brilliant truth of Euclid’s modest claims broke through – but it was beautiful. Some kids had very limited ideas of what was true, and seeing how the logic of a Euclidian proof compels agreement was the dawn of a new world to them. I think I had a similar experience in 4th grade, when I first understood how the hard sciences can prove something true. Given a set of assumptions and definitions and the rules of logic, a really well-constructed experiment can really prove something, within, of course, the limitations of the observations and definitions.
But I digress. The point here: diagrams don’t speak for themselves You have to speak their language to understand them, and sometimes need many additional words of explanation. One more point: practice makes perfect. If you, like a St. John’s freshman, are working through Euclid pretty much every day, you start to get the hang of how he works, so that each successive proposition tends to make sense more quickly and easily than the last. (This is offset by the generally increasing complexity of the propositions, but you get the drift.)
I write all this to explain to myself how it is that diagrams such as the one below don’t seem to impress people:
So let’s spell it out:
This chart displays deaths by age band by week per 100,000 people in that age group.
The order of the lines on the graph are the inverse of the order of the ages in the list to the right. That is, the bottom group in the list is the top line in the chart, and visa versa.
The y-axis scale peaks at 50 deaths per week, which the line for weekly deaths in the 75+ Years group slightly exceeds at a couple of points. This means that a little more than 50 age 75+ people per 100,000 died over a week a couple of times.
Conversely, at no point are any deaths per 100,000 of those under 40 evident. Given the scale, where 1 death per week is noticeable as a slight bump, this means that, at most, something well under 1 death per week per 100,000 occurred for those under 40.
For those under 50, the peak weeks might be as high as 1 per 100,000 at a couple of points. Since the under 40 are invisible at this scale, if you add them all together to get a weekly deaths per 100,000 for all those under 50, your total weekly deaths per 100,000 over the 7 age groupings added together reaches a max of about 1 at two points over the last 18 months.
But what does this all mean? It means, first, deaths among the elderly have been high, and deaths among those under 50 have been low, with deaths among those between 50 and 75 being measurable but much lower than those 75 and over. For those under 40, deaths per week doesn’t even register at this scale.
One more piece of information not presented here is the age distribution across the population. That’s not the point of this diagram, which is expressly concerned with deaths per 100,000. But to get your arms around what this means in terms of total deaths, you’d also need to consider how many people fall into the various categories.
(aside: I can never seem to find population distributions by age expressed with the same age banding that the CDC uses. I’ve wasted time backing into the numbers, but it just seems odd that the data is most generally presented with wide age bands that one cannot easily change. So this is going to be sloppier than I’d like, but I think the point will still be clear. End gripe.)
The US population is estimated at about 332M. Almost a quarter of that population, or about 78 million people, are under age 18. Last I checked, about a week ago, 380 Americans under 18 – children – had deaths ‘involving’ COVID (that’s the CDC’s language, not mine). As Briggs points out, that’s less than half as many children as died of pneumonia over the same period. And before you go there, recall that pneumonia also can have lingering or permanent effects on those who survive it.
On the other end, 16.5% are over 65, or about 55 million Americans. Backing into the numbers on the chart above, at the two peaks in April 2020 and January 2021, it looks like as many as 75 people 65 and older died per week. Multiplying that per 100,000 number by the 550 units of 100,000 in 55 million, you get peak weekly deaths in the 65+ age groups of about 41,000 deaths. Peak weekly deaths ‘involving’ COVID for people 65 and over were about 100 times the TOTAL deaths of children over the entire 18 months of the pandemic.
Therefore, taking these CDC numbers at face value, COVID is a threat to the elderly, and not a threat to children. The overall risk of death for children is not significantly increased by the presence of COVID in the environment. Indeed, the overall risk of death for those under 50 is not meaningfully increased by the presence of COVID in the environment.
This situation was evident, as in screaming from the page, with the very first Imperial College report back in March of 2020. But do you hear about it on the news? No?
Was abusing our children’s patience by walking through the 3-step process by which science is reported in popular media, as follows:
Somebody does a study.
Study author gets interviewed by a journalist, who writes something up.
Editors and headlines writers take a crack at it.
What could possibly go wrong? Let me count the ways:
Study is a piece of crap. My own private unpublished but peer-reviewed (I asked my son about it) study says: 95% of all studies you hear about on the news are crap. Prove me wrong.
You’d need a microscope see the chances the ‘journalist’ understands enough science to hold a printed copy of the study right side up. He couldn’t identify scientific evidence if it climbed into his lap and kissed him square on the lips. Basically, he got the gig because he was less terrified (or a more dogmatic SJW) than the rest of the reporting pool.
If, by some slim chance, anything accurate and useful made it into the article, an editor, who understand less science than even the reporter, is virtually guaranteed to screw it up. He’ll punch it up by making the opening paragraph as scary as possible, regardless of anything so mundane and boring as evidence.
The headline writer knows even less science than the clown parade that produced and edited the report he’s now skimming. He slaps an apocalyptical headline on it designed to get clicks. Thus, every warm day becomes global warming doom, every plastic straw is causing the extinction of cute little turtles, every remote possibility something might go wrong becomes the end of the world, even if the headline writer has to make it up.
Your average reader, if he even gets past the headline, stops after the first paragraph, where the editor did his job ‘punching it up’ by featuring gloom and doom.
And that’s if the people involved are trying, somewhat, to play it straight. If they have an agenda – I slay me! – it’s much worse.
Moral: if you heard about some science-y sounding thing on the news, it’s wrong. Just assume it’s wrong – the times you’ll be mistaken are negligible, and the disgrace you’ll suffer minimized.
(Aside: this is why Sagan was so popular: most scientists are (or at least were) very careful, and really hoped they could explain what they were up to. How boring is that? Plus, it makes you feel stupid. Sagan flattered the idiots, explained nothing, but put on quite the show. He knew the people publishing articles and producing TV shows didn’t want to feel stupid, were not going to put much effort into understanding anyway, so he just used his stolen glory as a scientist to sell them Scientism. This has born fruit, in the form of millions of Americans thinking they ‘believe the science’ whenever they fall in line with whatever the Officially Approved Authority Figure (an AAUF, pronounced ‘oof’ ) is saying at the moment.)
As I long promised, I’m plowing through the 2020 CDC death data to determine, as much as possible, how many people the d*mn virus can plausibly be said to have killed.
As predicted, the data is now still harder to find – the All Deaths data, which used to be a couple clicks in from the CDC front page, is now very hard to find – at least, it’s not obvious. After scanning the pages and searching using the CDC’s search box for the very specific title of the page, which I knew because I have that YouTube video by a Dr. Briand from John Hopkins wherein she analyzes the CDC numbers, I ended up snipping a tiny corner of her PowerPoint, expanding it, then manually typing in the wee fuzzy web address – and it came up. So, the data is still there as of today, it’s just not easy (possible?) to find from the CDC front page. I’m grabbing/downloading and backing up everythng.
About that main page – sheer, poisonous fear-mongering. They show 350K COVID death, just stated as a fact on Page 1 – their data, once I found it, showed 300K deaths involving COVID. So, where do those extra 50K dead people come from, if they’re not, you know, there in the CDC’s own numbers? No explanation is offered. Just shut up and be very afraid. Then, they have warnings about the danger to young people and children, how we need to be very afraid – while their numbers show – hell, take a look:
There were 149,198,677 Americans 34 and under, of whom 118,384 died in 2020. Of those poor souls, the deaths of 2,672 ‘involved’ COVID, meaning, if you were under 35, you stood a 0.00179% chance of dying of COVID last year. Slightly worse than your risk of dying of a shark attack or lightning strike. If you were one of the 103,258,356 Americans under 25, a sad 54,830 of your age cohort died last year, a whopping 585 of whose deaths ‘involved’ COVID. If you’re under 25, your chances of dying from COVID last year were 0.00057%. Slightly worse than getting hit by meteorite – but not by enough to worry about.
Before you dare mention lung damage, or lingering problems, or any other reason young people should be masked & locked up, one rule: show me the damn data. I don’t want to hear about some anecdote you heard on CNN – show. Me. The. Data.
I note one last thing from the CDC data now, with more to come when I’ve gotten a chance to digest the data: the overall death rate in the US in 2020, pending, of course, updates that should roll in over the next couple weeks and push it ever so slightly up, was 0.884. That’s 884 deaths per 100,000 Americans. Back in May, I looked up the UN’s 2020 projected death rate prior to COVID, and it was 0.888. This number just comes from extrapolating from long-term tends, nothing special, but, barring a deadly pandemic or other disaster, such a method should produce a pretty accurate forecast. So it looks like the US, despite a raging pandemic that’s killed, they say, 350K people, will have had pretty much the same number of dead people in 2020 as projected before the pandemic. Huh.
By now, I’ve despaired of convincing anyone who can open their own eyes and look around and is yet not convinced that the COVID 19 panic is and has been from the beginning a fraud. If you can’t see that, I don’t know what I could say to convince you. But, for my own satisfaction:
Way, way back on April 3, using then-available number, basic logic and a little math, I came up with an infection fatality rate (IFR) of around 0.25%, and said that was still probably quite a bit high. About 6 weeks later, the CDC published their “COVID-19 Pandemic Planning Scenarios“. Digging around a bit, their most likely scenario used an IFR of. 0.15 to 0.26%. That means the CDC expects about 15 to 26 out of every 10,000 infected people to die.
Imagine. And they evidently based this on their very ‘generous’ counting of COVID deaths. Now, I’m not some genius sleuth or anything, just pointing out that the data needed to reach these very-much-not-worth-panicking-over-numbers were right there all along, so that even I could reach them. And I’d still bet the CDC IRF is high by a factor of maybe 4. Just a hunch.
Thus: even accepting a world where we all are encouraged to imagine ourselves under dire threat from a disease where 95% of the attributed deaths are among very sick, often very elderly, people with multiple health problems and short, as in months, life expectancies, that risk is still TINY according to the CDC driving the panic. If you’re not in a nursing home or otherwise under palliative care, you are literally under more risk crossing the street than from catching this virus.
Those watching Our Betters decide that rioting over the approved issues immunizes people, while golf or church or a visit to a restaurant is literally courting DEATH for MILLIONS, all while even the ridiculously ‘generous’ death counts plummet, and STILL think the lockup was a good idea and people who don’t wear masks are evil, are not going to be convinced otherwise by math and facts. But I tried.
Charts, because we haven’t done those since they got boring:
In the US, boy, are they trying to make it seem bad despite all the evidence to the contrary. In a country with 330M people, where close to 8,000 people die on an average day, we’re supposed to cower like rabbits because, with ‘generous’ counting, because around 500 people (and falling), almost all of whom were very sick and most near death before they (may or may not have) gotten infected, are dying while infected per day.
No deaths at all since May 26. Note the hilarious “correction” on May 25, where they can’t say: “we overcounted deaths by a couple thousand, which is about 10% of the total,” Because that would be too easy. Instead, they said:
“Spain: On May 25th, the government decreased the number of total cases by 372 and the number of deaths to 26837. The discrepancy is the result of the validation of the same data by the autonomous communities and the transition to a new surveillance strategy. Discrepancies could persist for several days. We’ve adjusted our figures to reflect the new numbers [source] [source] [source]”
Over. Was over once a) it had ripped through the nursing homes; and b) spring weather arrived.
Stick a fork in it.
Turned the corner yet? Hard to say. Southern hemisphere, but the population is mostly in tropical and subtropical climates – usually hard on airborne viruses. Not sure what’s happening, but remember: 220M people, many living in very poor conditions – kind of like Wuhan tenements. This level of deaths, while certainly tragic on a personal level, is not something to panic over. Will keep an eye on this.
Mexico (pop: 129M, or twice Italy or France) is approaching the Top 6 in deaths (above). Will keep an eye on our neighbor to the south.
Also also: William Briggs took the data in the CDC report linked to above, and produced this chart, showing graphically the about 62M infections generating those 1.7M cases we’ve heard about. Again, it’s that whole functionally numerate thing: if this doesn’t make you guffaw, maybe numbers aren’t your thing?
And Dr. Briggs’ analysis:
As of Wednesday night, and using our standard sources (which exaggerate death counts), there were 1,689,630 reported “cases” (positive tests) and 94,352 reported deaths. The crude CFR was 94,352/1,689,630 = 5.6%. Again, this bug is not killing 5.6% of those with symptoms. The RFR was 0.03%.
The number of estimated actual cases are anywhere from 8 to 30 million Americans. That is, about 2.4% to 9.1% of the US’s population had symptoms or were otherwise cases.
The number of estimated actual infections are anywhere from 37 to 62 million people. That is, about 11% to 19% of the US’s population are already infected.
If actual deaths are lower, then all these numbers will be too high.
The point of all this: to find more cases, all you’d have to do is run more tests – the infection is out there in millions of (asymptomatic or mildly symptomatic) people. Panicking over increased cases is idiotic. Or, to be more generous, shows a lack of understanding of the data.
The top 6 countries by COVID 19 deaths are shown below. These 6 countries barely represent 10% of the world’s population yet account for 56% of all cases and 72% of all deaths:
A political observation, something all these countries share:
USA – election of a ‘right wing’ administration revealed the vulnerability of the culturally and politically dominant Left, which, predictably, completely lost its mind. Now, under the COVID noise, indictments are coming down against the 3rd tier of Leftist operatives.
UK – contrary to the wishes of the politically dominant Left, the nation voted not only to reject globalism and leave the EU, but then enabled the despised non-Leftist Boris Johnson to become Prime Minister and form a government.
Italy – The anti-globalist 5 Star Party came to power in 2018.
France – Macron, a lifetime Socialist Party member, has been under siege by the Yellow Vests for over a year. The Yellow Vests are a mixed bag, politically – hey, it’s France – but include ‘populist’ anti-government factions. While Macron is often portrayed as pro-business, a cursory look at what he’s up to shows he’s pro giant global business, and pursues pretty much textbook Gramsciite social destruction policies.
Spain – a history of conflict, often violent, between Communists (under whatever name) and more conservative elements. ‘Right wing’parties have been making gains in recent years.
Brazil – Jair Bolsonaro’s election in 2019 put in power pretty much the Left’s nightmare candidate. As Wikipedia sums up: “He is a vocal opponent of same-sex marriage and homosexuality, abortion, affirmative action, drug liberalization and secularism. In foreign policy, he has advocated closer relations to the United States and Israel. During the 2018 presidential campaign, he started to advocate for economic liberal and pro-market policies.”
Each of these nations has a Left that’s having its power threatened. Somehow, that correlates remarkably to more COVID 19 deaths…
Now for some charts. Have COVID 19 daily deaths counts fallen off a cliff? Why, yes, yes they have: (All charts from Worldometers)
This is probably as low as Italy will fall, as far as daily death counts go, as they count COVID 19 deaths very liberally, and, with an older population, they will not lack from nursing home patients with the sniffles checking out. But, on a populations 60M+, we’ve reached the statistical noise level of deaths.
Ditto. Also note that the declines started in early April – with the arrival of spring, which is what any sane person would have predicted.
French data is extremely noisy – again, hey, they’re French – but the overall decline is still there. Those spikes are all related to reporting lumpiness. Tiny numbers.
So here’s the one country, of the six ‘leading’ country, where the decline is not evident from the graph. Brazil has over 200M people; 750 deaths are, as always, personal tragedies, but, statistically? Barely registers.
I’ll keep more of an eye on Brazil.
Even the birth palce of COVID Panic Porn is clearly on the way out. This data shows an odd weekly cyclicality: down, down, down, off a cliff, down, way up, repeat. Reporting quirk? I’d assume so.
As noted previously here, England has implemented a policy of listing COVID 19 on the mandatory reporting list, along with the Plague, Mad Cow Disease, anthrax – because a flu-like infection fatal well under 1% of the time is just like those things. The net result: as the infection inevitably spreads, more and more people who test positive will show up in the counts regardless of COVID 19 actually contributing to their deaths, or, indeed, despite showing no symptoms at all.
So, realistically, we’ve reached bottom unless the UK changes its reporting rules. It may even go up.
Finally, the US:
Same pattern, same issues as with the UK data. Given the political investment in keeping the lockdown and fear going by political conmen like the reptilian Newsom, I’d guess this is about as low as the daily counts will be allowed to go.
If you’re going to put Grandma or Grandpa in a nursing home — don’t put off making a visit. That’s the upshot of a U.C. San Francisco study published this week, which reveals elders often don’t last very long in care facilities.
Of its sample of nursing home patients who died between 1992 and 2006, a full 80 percent were dead within one year, claims the study, which appears in the current edition of the Journal of the American Geriatrics Society.
(UPDATE: Looked around for more data on nursing home mortality – it is made somewhat confusing, in that the definitions are not clear. What, exactly, is a nursing home versus Long Term Care? What kind of patient is in what kind of home? The most favorable study showed an expected annual mortality rate of 31.8% and an average stay of 2.2 years. Others were in between. Perhaps the 80% annual death rate is for a particular kind of nursing home? Alzheimer’s and dementias seem to take 5 or more years to kill their victims once they’ve been put in a home – perhaps the homes in the first study excluded such patients? Yet, even the low end annual mortality rate of 31.8% requires that nursing homes are home to a lot of people who are very near death.)
In the body of the report, it explains that numbers are not available for the US, Italy, Spain and some other places. Too bad. In the US, with only 35 states reporting, the report notes that there have been at least 10,000 nursing home deaths.
If we assume, not much of a reach, that the US is most likely more like the western European countries (+ Canada) on the right than the mostly smaller countries on the left of this chart, then the US rate might be 50% or more. We’ll have to wait and see if those numbers ever get reported. (I’m not holding my breath.) Also note that these numbers presumably do not include those cared for at home who might otherwise be in a nursing home, or hospital patients – also likely very sick people with a very short life expectancy.
(Aside: we also should not assume homogeneity: some people in nursing homes are a lot closer to the end than others – no reason to suppose COVID 19 isn’t killing proportionately more of the very ill. In other words, it would be simple-minded to assume 20% of the nursing home deaths attributed to COVID 19 were of people who otherwise would have survived the year. I bet close to 100% would have died this year– something like 95%.)
The bottom line here: my confidence that the total US deaths this year will not be much higher than the UN pre-COVID projection of about 2.903M is solidified by just about all the data that comes out. If 200K deaths are assigned to COVID 19 – unlikely, if they’re playing fair (Ha! I slay me!), then the total would go up by maybe 100K (a bad flu season)- IF it’s only the nursing home deaths that are padding the totals. I’m thinking, based on the ongoing collapse of US death counts (6 straight days of declining daily totals – Spring is here!), 200K is well out of reach.
When it is said, as it has been, that 95% of the COVID deaths in the US are of people with one or more “comorbidity”, that is identifying a population that is already a lot nearer to dying than the typical healthy person. To put it another way, 5% of the people who die of COVID 19 were otherwise healthy – for 70K deaths, that 3,500 otherwise healthy people who died of COVID 19. Not to be gruesome, but some percentage, probably a very large percentage, of the remaining 66.5K dead were going to die this year anyway, and thus were presumably included in the original death projections, and will thus not add to the number of overall deaths.
As I get older, I get a glimmer into how odd I really am. Not that I think I’m all that different than anybody else in this regard, we’re each weird in our own way. For today’s post, I’m trying to remember that I simply process information in what is evidently an unusual way.
Example I’ve mentioned before: when we studied Euclid in freshman year lo these many decades ago, 9 times out of 10 I would look at the drawing, read the proposition – done. Working under stated premises and the rules of logic, you assert, for example, that the angles opposite equal sides of a triangle are equal? Sure. Prove it? Sure – hand me the chalk. It took me a good while to understand that the other students, generally very intelligent people, didn’t work this way, that the truth of the propositions was something they got to by working through the proofs step by step, and that they had little idea how to proceed with the proofs without first working through them.
Now, I’m an idiot when it comes to language and many other things – can’t seem to get even the basics, and forget them faster than I can learn them. But logic and Euclid? Evidently, I’m some sort of idiot savant.
I say this because of my growing frustration, where arguments over the virus seem to circle around and around the same irrelevancies and claims, while ignoring what, to me, are the glaringly obvious points and the inevitable conclusions to be drawn from them. Then I remember: I’m odd, perhaps these points are not obvious to others? Maybe I need to lay them out in some sort of rational order, and not skip any steps? So, today, for what seems to me to be the umpteenth time, let’s go over the basic issues.
1. Messy data, and what it tells you
Way, way back in January and February, we started getting information out of China about an epidemic. Over time, but before mid-March when the shutdown was imposed here, we got data showing case fatality rates from various areas in China. Most of the action was in Wuhan, but it had spread to other areas as well. The CFR for various regions were quite different, ranging from, if I recall, 4.5% in Wuhan proper to well under 1% in outlying areas.
The very first thing these numbers tell you: the data is very messy. These various outcomes CANNOT be caused by the virus alone. There must be – MUST BE – other factors at play. The next thing they tell you: if these numbers mean anything, they mean anyone’s chances of dying from the virus is heavily dependant on where they live.
I evidently need to harp on this: these CFR numbers, in themselves, don’t actually tell us how deadly COVID 19 is. To get to that point, you would need a whole bunch of additional information – information, it turns out, nobody has. I’ve harped on that in previous posts. But they do tell you that there is no one number that represents how deadly this virus is, that even so simple-minded a number as CFR varies enormously from place to place.
From the beginning to this day, the claim has been made that ‘we were acting on the best data available.’ I am here to say:
NO, ‘WE’ WERE NOT.
The best we knew, the clearest information we can get and could ever get from the early data or any of the subsequent data: the seriousness of the virus depends on where you live. There is no one CFR that expresses the seriousness of this infection.
The ‘information’ we acted upon, the evident cause of our panicked overreaction, was: dead Italians! 7.7% CFR! Ferguson’s model! Millions dead! If, instead, we had said: the data is very messy so it’s impossible to conclude much of anything from it, but, if it does mean anything, it means the CFR depends enormously on where you are when you get infected, we would then have asked different questions and proceeded differently.
But ‘we’ didn’t. We ignored the actual evidence in favor of wild, worse-case assumptions, that we then plugged into models, which, dutifully, produced worse-case numbers.
Garbage in, garbage out.
Model output in not data or evidence. Acting on model output is not acting on the evidence.
(This is why, by the way, I focus almost exclusively on deaths, and belabor how deaths are counted, and dismiss case counts and CFR as misleading – deaths are the ONLY way to meaningfully gauge the seriousness of the outbreak. And not that many people had died when our overreaction was inflicted. Some states were shutting down before a single death had happened within their borders.)
I’m putting a recap/expanded summary of what I mean by messy data and what that means to the numbers being tossed around in a footnote. The messiness of the data generally means one would need to be careful using any of them and caveat the hell out of any claims. In this case, with few exceptions, the messiness of the data tend strongly toward overstating the seriousness of the pandemic. I’ve been harping on this in previous posts, which I why I merely footnoting it here.
2. There is nothing novel about novel viruses
Whenever it is pointed out that, you know, people and viruses have evolved together for millenia, and that we need new flu shots every year because every year we have to deal with new versions of flu viruses, we are told “COVID 19 is a novel virus! It’s not the flu! Nothing that has happened with flu viruses has any bearing on COVID 19 (and: you are an idiot to propose it does – I’ve been told this).
But wait – every new flu bug is a novel virus by definition. And, while, according to the current classification system, coronaviruses are not flu viruses, there are and have been plenty of coronaviruses floating around as long as people have been alive. Some common cold viruses are coronaviruses, for example.
Just because COVID 19 is caused by a novel virus doesn’t mean it is any more scary than next year’s flu bug, which will also be a novel virus, or the next cold you catch, which could very well also be a novel virus, and even a novel coronavirus.
Novel doesn’t equal ‘super-scary’
If we want to look for a recent, comparable virus, how about SARS in 2002? SARS is a closely related coronavirus, much harder to catch but more dangerous if caught. It died out once the weather got nice. It made a brief reappearance the next year, and then died out for good.
The claim that COVID 19 requires extraordinary, job-destroying precautions simply because it’s novel is absurd. Humanity has endured novel viruses for millenia, year after year after year. There is no evidence to support the idea this virus is going to be particularly worse than any of the others.
Buuut – if you mistake model output for evidence, and ignore what the actual evidence is, then ‘novel’ means this virus is way different than the usual. As more evidence rolls in, it becomes clearer and clearer that COVID 19 has been wildly and irresponsibly overhyped. ‘It’s just the flu’ looks more and more accurate as each day passes.
3. (Recklessly) Assumed Homogeneity
Assume a spherical cow of uniform density in a friction free environment. A blog post from Sarah Hoyt reminded me of this old joke, how complex situations must be simplified to be modeled – and thus, why models so frequently give gibberish answers.
The models necessarily assume a particular spherical cow of uniform density: that there is *one* rate of infection and *one* fatality rate, and one value for any of the other variables. (You apply the calculus after you’ve entered the values.) True, you could build multiple models representing multiple populations – the Imperial College model did two famous ones- one for Britain, one for the US – but that should engender an uncomfortable discussion of which I have heard nothing: how small do we slice it?
The logical answer, based on the very earliest evidence out of China, and reinforced with every new piece of information, is: very, very small. Not a country, not a state, not a city. How about a cruise ship, or a nursing home, or a particular Wuhan tenement? Or a California suburb, a grocery store, or a school? Does anybody pretend to believe you are getting remotely the same CFR in an Italian nursing home and a Southern California grammar school? Or from one nursing home or school and the next (unless it’s zero)?
I can hear the ‘buts’ – but people interact! But people don’t stay in their school or suburb! So? What does the model do? Assumes *another* spherical cow of uniform density: that everybody interacts the same way and amount, that some ‘average’ rate of transmission represents what’s really going on, that human interactions can magically be reduced to some (assumed ) number.
That is again, stupid, and *assumed to be false* by the very mitigation efforts currently being imposed on us: our faith in the belief that quarantines and various isolations and restrictions can stop the virus means we accept the notion that how people interact is different and effects how the virus spreads.
We could, therefore, have focused on exactly which interactions between exactly which people are the likely vectors, and striven to control them. We tragicomically did not: in our fine state, churches and garden centers are shut down; homeless encampments and public transit are not. People shopping outside in fine weather or sitting for an hour in a church – too dangerous. People riding around in subway cars and buses, or camped out in their own feces – acceptable risk. The homeless, in particular, then go mingle with social services personnel and the people they panhandle from. Yet homeless encampments were explicitly exempted from the rules. Sound prudent to you? Either California wants the homeless to die and doesn’t care how many other people they take with them – not politically viable – or they think this lockdown is a joke. Or? Similar insane steps seem to have been taken elsewhere. New York, last I heard, had not even yet shut down subways and elevators!
Another spherical cow, a somewhat more subtle one: that grouping people by age or any other characteristic makes all those in the class effectively the same. The class of, say, people 75 to 84 years old is somehow homogenous, thus any person in that class stands, according the CFR for that age cohort, a 10.32% (or whatever) chance of dying if they catch COVID 19.
Nonsense. Within that group are some older, some younger, and, much more important, some healthier and some sicker individuals. Some already have breathing and heart troubles, some don’t, and all this is a matter of degree. Lumping them together by age fatally muddies the answer to the underlying question: how likely is COVID 19 to kill them?
One subset is very likely to die, much more than any other. People are abandoned to die in nursing homes by the millions every year, with the people doing the abandoning feeling more or less bad about it. Some even visit. I guess all that daycay when we were kids gave us a high tolerance for other people’s misery. Be that as it may, it is BLINDLY OBVIOUS that the populations in nursing homes are way more likely to be seriously ill and ARE WAY MORE LIKELY TO DIE than their age cohorts in the outside world.
I asked a relative who worked for years in a nursing home. The staff knows that, likely as not, some little thing – a cold, a stomach flu, an infected scratch – will kill those people sooner or later. Probably sooner, if they are very weakened; maybe later, if they are stronger.
Dying after catching a viral infection is an extremely common way people in nursing homes to eventually check out. The virus would not kill them if they were not already old, sick, and weak.
Reports are that about 20-25% of all the COVID 19 deaths are people in nursing homes. This may – may – be understated, as it’s possible the decedent got sick and was shipped off to a hospital to die. Also – and I don’t know, but it seems very likely – some of those who die at home very well might be under hospice care – another (better) way we treat those who we expect to die soon.
So ‘where you are’ must also contain caveats about where you are healthwise. This information, which was available at the time of the US lockdowns, was effectively ignored. What happened in Italy, a country with a history of ‘excess deaths’ for even just seasonal flu, is that COVID 19 ripped through some nursing homes, then through some hospitals, and then started to peter out as soon as it had claimed, not random 81 year olds, but very sick 81 year olds and other people sick enough to be packed into a nursing home or hospital.
The point of all this: Now, it’s common among the educated to claim that our lockdown – and our sheep-like surrender of our constitutionally guaranteed right to free assembly – was merely prudent, based on what was known at the time, and that caution should be exercised in lifting restrictions. That’s a far as you can go and stay in the Kool Kids Klub. Further, other considerations, ones that were not available at the time the initial decisions were made, don’t retroactively make the initial decisions made without them somehow more reasonable. Excess deaths in (tightly-packed, #WohanStrong, subway & elevator ridden) New York and its suburbs NOW does not contradict or stand against what was know back in March: that packed conditions where people jostle about and breath the same recycled air in closed buildings, elevators and subways is *pretty probably a high-risk area* and that prudent steps should be taken there.
But NYC ain’t Laramie, WY or suburban California. A subway isn’t a suburban park. An elevator isn’t a garden supply store. Yet we model and set policy as if they are, not in accord with, but in defiance of, what was known at the time.
“Where is Every Body”: Fermi’s Ghost in China
Take a look at this picture, and note the date:
China started its by now famously effective and credulity-straining lock down on January 23, but didn’t get around to a general travel ban until January 30. The virus had spread all over China, presumably before January 30, such that cases were found everywhere. (Note that the size of the hexagons reflects cases, but more obviously, reflect local population densities: The huge one is of course Wuhan; the tiny ones are where few people live all spread out; the big ones where many people live in large cities. The 1.3 billion Chinese in China are not evenly distributed, and do not live in one uniform fashion or under one uniform climate or geography, etc.)
By the January 30 lockdown, COVID 19 had had about 2 months to spread out from Wuhan – and spread it did, as you can see by the map above.
The first US case was reported on January 20. The first furtive steps at control took place with travel restrictions in January, but full on, government-led efforts were not taken until March 16.
So: China and the US both had about 2 month of spread before strong steps were taken. China, with a more densely packed population in many places, poorer sanitation and personal hygene practices, and an outbreak in the dead of winter, might – might – be supposed to be a more favorable environment in which the virus could spread. Be that as it may, on March 31:
WASHINGTON (AP) — President Donald Trump on Tuesday warned Americans to brace for a “hell of a bad two weeks” ahead as the White House projected there could be 100,000 to 240,000 deaths in the U.S. from the coronavirus pandemic even if current social distancing guidelines are maintained.
When you build models, one thing you routinely do is a reality check: is my output reasonable? Are there any existing cases against which I can try my model to see if it makes sense, given what actually happened? This is simple prudence regardless of the type of model. When I used to use financial pricing models, I knew or could easily find out what was happening in the market – what people were charging for equipment financing, and what kind of yields the finance companies were getting (and cost and tax assumptions, expense allocations, cash flow timings and all sorts of related trivia). So, if I modeled a case where the output was wildly different to what was happening in the real world, I’d look into it hard. No way am I just going to use output that contradict reality. I’d get fired.
So, when his team told Trump a bit over 2 months into this, that between 100K and 240K Americans were going to die, even with all the restrictions in place, I have to ask: where is every body? where are all the Chinese dead?
Because China was 5 months into it by this point, had similarly had 2 months for the virus to spread unchecked, had if anything more favorable conditions for its spread – and yet, as of today, is reporting under 5,000 deaths. Would not logic dictate that, since there are 4 times the number of Chinese as Americans, and, as you can see from the map above, COVID 19 was just about everywhere in China, that something like 400,000 to a million dead bodies should be piling up by now? Under the assumptions used to predict 100K to 240K dead Americans?
So, this forcast fails the sniff test rather badly. Of course, I think the Chinese communists lie, and think they could probably lie their way around an order of magnitude more deaths – 40K, say. But 400K? A million? Possible – these are the people who offed something like 65M in the Great Leap Forward, after all – but that took years, before spies, satellite surveillance and a semi-open country. Seems kind of hard…
Or, perhaps they are not lying, and instead are only counting people where COVID 19 clearly killed them? As Neil Ferguson, the guy behind the model, recently said about deaths being attributed to COVID 19 actually being from other causes:
It might be as much as half or two thirds of the deaths we see, because these are people at the end of their lives or have underlying conditions so these are considerations.
I don’t know, but either their numbers or ours are bogus, or both are and something in between is happening: the Chinese are hiding some deaths, and we are wildly overstating ours.
More to the point: at the time when this “100K – 240K” claim was made, it could not have been based on ‘what was known at the time’. It was not based on science or even a good-faith effort. All the supposed data available makes it nonsensical. Instead, like all the doom and gloom projections so far, it seems based on wildly pessimistic assumptions that nobody sniff-tested. SOMEBODY needed to ask: what about China? And get a straight answer. Trump’s got the surveillance satellites and spies, after all.
Springtime for COVID!
Finally, one last thing that keeps popping up: there are some viruses that seem to survive the spring and summer months better than others. Not very many that anybody has heard of. In fact, the only ones I’ve heard of are the Spanish Flu, which survived for about 2 years in conditions – WWI, primitive medical care – that could hardly be farther from what we have now, and the 1957 Asian flu, which lasted about a year and a half. Each of these flus somehow survived the disinfectant effect of sunshine, and so came back for an encore once the weather got cold again.
But, we are constantly reminded, COVID 19 isn’t the flu! It’s a *novel* coronavirus. Way different! So, do we have any coronaviruses to compare it to? Why yes, yes, we do: the SARS outbreak of 2002-2003. That SARS virus is closely related to our current bug. Infections broke out in November 2002; the WHO declared it over in July of 2003. Then, for unclear reasons 251 cases were identified in Toronto in 2003-2004. That micro-outbreak ended in June, 2004, with an appalling 43 deaths, or 6% of all the deaths.
So, again, based on the evidence we actually have and not on worse-case assumptions about what *might* happen, the logical thing to assume is that what seems most definitely to be happening – nice weather is killing off the virus – is, in fact, what IS happening, and that, based on what happened with the closely-related SARS virus, little or no recurrence is likely to happen in the fall.
Again: evidence. If you want to claim that we need to cower in fear that this virus will, like the dream of postbellum South, rise again, then point to a similar virus that did so. No fair harping on how COVID 19 isn’t the flu, then turning around and using *the flu* as your example of Undead Viruses.
Conclusion: I’m appalled, as any reader of this blog knows, by misuses of the term science in connection with every hair-brained bit of panic-mongering that crosses the illustrious pages of our esteemed media. In a remotely just world, this pandemic will be remembered as a cautionary tale, and go down as yet another abuse of science from what future historians (one hopes, not in charcoal scratching on a cave wall) will call the Age of the Great Scientific Frauds.
The data is messy. Let me count the ways:
Some of the more important ways, at any rate.
The definition of a ‘case’ is not clear or consistent from place to place, and changes over time. Cases are not reported in an orderly or consistent manner. Cases may or may not include those diagnosed from symptoms alone without a confirming test. Cases are unlikely to include very many asymptomatic people. Cases are also dependent somewhat on how much testing is being done. What this means: case counts across time and space give us only a vague idea of the virus’s spread.
The definition of a ‘death’ is not clear or consistent from place to place, and changes over time, for many of the reasons given above. Filling out a death certificate is not simple. Often, the immediate cause of death is not clear. Further, nowhere, with the possible exception of China, is death by COVID 19 counted in the manne any reasonable person would consider fair, namely: did COVID 19 kill this person? Would he have lived if he hadn’t caught it? Estimate of how many deaths classified as caused by COVID 19 that could pass this common sense definition range from half – Ferguson’s high end estimate – to 12% or less by some Italian doctors. The US, Britain, Italy and France explicitly encourage or insist upon COVID 19 being listed as a cause of death if the decedent tested positive or could reasonably be supposed to have COVID 19, even if it is at most a minor cause. E.g., Ventura County reported 2 COVID 19 deaths a couple days ago: a 99 year old man (life expectancy in years = 0) and a 37 year old man, who died of a drug overdose but had tested positive for the virus. No reasonable person would count those as COVID 19 deaths. What this means: Death counts are not a fair representation of the number of people who were killed by COVID 19. It seems likely to be too high by 50% or more.
Cases are not infections. Nobody knows, and nobody ever will know, how many infections there are or were. Cases will always understate infections, often severely, unless the disease is near 100% fatal or near 100% generates unique, serious symptoms, or we accurately test everybody in the world. Otherwise, mild or asymptomatic infections will constitute a probably large number of infections that do not become cases. Early own, using the then-available information, I estimated that infections outnumbered cases by at least 400%. Since then, wider testing in, for example, New York and Miami, suggest at least 15 times as many infections as cases. What this means: The virus is much more widely spread, and therefore much less dangerous, than would be suggested by the number of cases.
Case Fatality Rate – CFR – is a) not the real fatality rate even in theory, b) can never be established with any confidence, given the uncertainty in the case and death counts, and c) needs to be measured over a more or less homogeneous population to mean much of anything. What this means: For the reasons above, the CFR may be 30 times or more higher than the true fatality rate: e.g., half as many deaths divided by 15 times the number of cases = 1/30 the CFR. if the number of infections is 15 times higher than the number of cases.
In conclusion: none of this is particularly hard to figure out, it should be obvious to any competent epidemiologist or model builder.
Been a week or so, so let’s look at some graphs, from Worldometers, as usual. Again, I focus on deaths, because however iffy the classification of deaths as caused by COVID 19, at least – sorry to be morbid here – somebody died and so there’s a body to count. Infections are unknown, and cases are a function of testing and changing definitions and instructions, and so can and do fluctuate unpredictably. I don’t know what to make of total case numbers, and I suspect neither does anyone else.
That’s not exponential growth, or growth of any kind. As the Philosopher pointed out 2300 years ago, what is not growing is dying. The curious thing: one would expect a decline at roughly the same rate as the rise. This seems to be falling more slowly than it rose. One reason might be that, with widespread infection and more broad testing, the listing of every death where COVID 19 appears on the death cert as a COVID 19 death would, over time, tend to cause the COVID 19 case death rate to converge with the overall death rate from all causes. In the hypothetical extreme, where everyone has been infected, every death will be attributed to COVID 19. This extreme is not going to happen in reality, but the principle applies: if, say, 30% of the population is determined to have or have had COVID 19 (so that they test positive), and that 30% dies at something like the normal rate, then 30% of all deaths for whatever reason would, under current practice, be classified as COVID 19 deaths.
The call for universal testing is a call, intentional or not, to inflate the number of COVID 19 deaths. If someone, say an older, weaker person, gets the flu, can’t fight it off, and it progresses to pneumonia and kills him – a very common way old people die – but tests positive for COVID 19, that is gong to be classified as a COVID 19 death just about everywhere in the West, certainly in the US, Italy and England. But – here’s the point – however classified, such a tragic death won’t push the annual numbers up. That death would have taken place anyway, so it doesn’t add to the annual total.
A plague worthy of the name adds to the total number of dead over its duration. So far , COVID 19 isn’t doing that, and there’s no reason to imagine it will going forward.
The UN projected that about 1.0658% of Italians would die this year, very slightly more than last year and in line with a decade long graduale climb as the population ages. Italy is home to about 60.5M people, so about 645K Italians were projected to die this year in the normal course of things.
So: will more than 645K Italians die this year? If a plague is killing a bunch of people, one might suspect so. But if people who, sadly, were going to shuffle off this mortal coil this year anyway, as people in nursing homes and hospitals frequently do, then COVID 19 will have little or no effect on overall deaths.
I bet there’s no increase, that right around 645K Italians die this year from all causes, just as if a no plague had taken place. Because (whisper) no plague took place. But because of the reporting requirements, virtually all deaths where COVID 19 can plausibly be claimed to be present in the deceased will be counted as COVID 19 deaths. Thus, unless they go with double counting, deaths otherwise attributable to heart failure and cancer and the afflictions of old age will drop, as will deaths from the flu, pneumonia, and any respirtory problems.
This switcheroo will only show up in the totals, or rather, will not show up in the totals.
Another thing – here’s the weather in Milan, the capital of Lombardy, over the last couple of months:
A bit cold and nasty-looking (to a Californian) until the second week of April – at least, cold nights and quite a few cooler days. Now, the weather is getting pretty nice, that lovely Mediterranean climate Italians and Californians love – and in which air-borne viruses quickly die out.
Same story. Here’s France:
French reporting has been very inconsistent, as this graph shows, but the trend, if any, seems downward. I’m reminded of a story I heard about the song April in Paris: Yip Harburg, the lyricist, was asked how he could write
April in Paris, chestnuts in blossom Holiday tables under the trees April in Paris, this is a feeling No one can ever reprise
…when every Frenchman knew April in Paris is cold, wet and nasty. He replied: May didn’t fit the rhythm. So the positive effects of Spring sunshine won’t likely be seen for another couple weeks.
Now, on to America:
What is happening here? We get repeated lesser daily counts for a day or two, followed by new highs – three or maybe four times so far.
Following new CDC guidelines: “As of April 14, 2020, CDC case counts and death counts include both confirmed and probable cases and deaths. This change was made to reflect an interim COVID-19 position statement issued by the Council for State and Territorial Epidemiologists on April 5, 2020. The position statement included a case definition and made COVID-19 a nationally notifiable disease.
A confirmed case or death is defined by meeting confirmatory laboratory evidence for COVID-19. A probable case or death is defined by i) meeting clinical criteria AND epidemiologic evidence with no confirmatory laboratory testing performed for COVID-19; or ii) meeting presumptive laboratory evidence AND either clinical criteria OR epidemiologic evidence; or iii) meeting vital records criteria with no confirmatory laboratory testing performed for COVID19″ [source]
Since over half the deaths have taken place in the NYC metro area, one would look first there.
And, on that same page, are updates on how, over time, other states have changed their reporting practices to be more generous and inclusive – in other words, to include more deaths under the COVID 19 heading, following revised CDC guidelines. Whether these changes are warranted or not, they skew the results in one way only: more deaths reported as due to COVID 19.
So, are we in the same situation as Italy, where I predict no significant uptick in total annual deaths? I say yes.
I had hoped to be able to say that the numbers clearly show the US is well past its peak; but at face value, that’s not quite possible, given the upward spikes in deaths. On a local basis, the New York City/Newark area, unlike the rest of the country, has seen overall ‘excess’ deaths over what historical trends would find reasonable. This is real, and cause for concern. On the other hand, I have seen no reports to suggest the profile of the people dying has changed – it is still 80% people over 65, and 95% people who are sick, elderly, or both. In other words, at most 5% of the victims are younger and healthy. I say at most, because the prudent thing to wonder is if those younger, healthy victims did not, in fact, have underlying health issues that were undetected – maybe, maybe not, but the thought all but suggests itself.
Finally, one more set of pictures: Weather in New York City
Again, from a Californian’s perspective (I’m sitting out on the patio typing this, 80F, light breeze, beautiful) that’s some nasty weather, mostly cold and damp, and erratic. Maybe if Spring finally arrives in the northeast, we can put a stake in this thing.