The Site that Asks: Is the "dictator" justifying intervention by killing innocents? Or is someone else doing it for him? (previously The Libyan Civil War: Critical Views)
August 31, 2020
Comments are re-enabled at this blog. Apologies to anyone who's tried to post a legitimate comment here lately. I decided a persistent captcha-evading spammer needed a change of routine, and might be aware I'll zap all their future efforts quicker yet, now that the option is back.
August 31, 2020
update Sept. 3
After recent revisions, I come back to the United Kingdom's disputed death toll from the SARS 2 / Covid-19 pandemic. It's a bit of a shell game to compare data, considering mixed standards and definitions between regions that change over time, adding up differently at different dates. I can only clarify some points and offer informed guesses for other parts in an effort to offer a concrete estimate of some value.
An Error Corrected?
I'll start with the heralded revision of August 11, lowering the UK death toll by 5,377 in what was taken as an admission of undue inflation by deceptive health authorities. Peter Hitchens, Daily Mail: "the London Government was last week forced to admit that for some time its official death figures have been a wild overstatement of the facts." He called the revision "a huge admission" of "an error" he suspects was no accident. As Hitchens explains:
"They were forced into this by the brilliant forensic work of Professor Carl Heneghan and his brave colleagues at Oxford’s Centre for Evidence-Based Medicine, which showed that the previous figures were so loose that they could have included car-crash victims who once tested positive for Covid."
Similarly, Kit Knightly noted at Off-Guardian: "Actually, over the last week the UK’s covid death count has reduced by over 5000, thanks to a review which removed duplicates and mistakes (which OffG predicted would happen months ago). The case count is bloated by at least 30,000 duplicates too."
I guess the case count WAS bloated - 30,000 cases were removed on July 2. Bing tracker, still seeming stumped how to plot the revised death toll.
As for "duplicates and mistakes," Knightly meant about what Hitchens, Heneghan, et al. suggest, but cites a BBC article mentioning neither: "Previously, people in England who died at any point following a positive test, regardless of cause, were counted in the figures," the article says, laying out two different issues: how and when people died. "But there will now be a cut-off of 28 days, providing a more accurate picture of the epidemic," it continues. That does suggest the prior view was less accurate, as "regardless of cause" would suggest, but that's actually debatable and/or might be left the same. Mistaken vs. inaccurate vs. debatably accurate ... And there's no hint I've seen, here or anywhere, of the death toll including duplicates.
Nor is there any sign of a change to the cause aspect Hitchens noted, just a timeline change to 28-days after infection, as already done in the rest of the UK, and broadly elsewhere. Currently, PHE explains that even within their revised, inside-28-days death count for Covid-19 patients: "The actual cause of death may not be COVID-19 in all cases."
But they must be hit by a bus, etc. WITHIN 28 days of a CONFIRMED infection? I have a query in about this point - just what types of deaths do they count - literally all, or just ones that at least might relate, or maybe something even more stringent? If I get any answer, I'll add it here.
Update 9/3: I did. query to coronavirus-tracker@phe.gov.uk:
Greetings. I'm Adam Larson, a researcher in the United States. I'm trying to follow the Covid-19 pandemic and sort out issues of confusion. One of these is the recent revision of PHE England tally of related fatalities. Some recent commentaries made it sound like a bunch of unrelated deaths - "even if they had a heart attack or were run over by a bus three months later" - were scrapped. But official sources and explanatory articles don't mention any change to the cause aspect, just a timeline change to 28-days after infection, as already done in the rest of the UK. Currently the website explains "The actual cause of death may not be COVID-19 in all cases" even inside that 28 days. Statistically, that should be the main killer in such a short span, but ... I didn't find a fuller explanation of what kinds of deaths are included. Is it, as understood, all causes, including car crash, etc.? Or does it only consider likely-related deaths like pulmonary complications, and maybe things like heart attack?
answered:
It’s all deaths of people who have tested positive within the previous 28 days. So, yes, it does include people who died in a car crash but no Covid-victims who were diagnosed 29 days or longer ago.
I took a look at this "brilliant forensic work" Hitchens cited.
Carl Heneghan and Yoon Loke, Center for Evidence-Based Medicine, July 16
"...it seems that (Public Health England) regularly looks for people on the NHS database who have ever tested positive, and simply checks to see if they are still alive or not. PHE does not appear to consider how long ago the COVID test result was, nor whether the person has been successfully treated in hospital and discharged to the community. Anyone who has tested COVID positive but subsequently died at a later date of any cause will be included on the PHE COVID death figures."
From the Quoted official explanation ("when a patient dies, the NHS central register of patients is notified (this is not limited to deaths in hospitals). The list of all lab-confirmed cases is checked against the NHS central register each day, to check if any of the patients have died.”) that might seem like a stretch. But it seems to be the case, as far as I can tell. They admit "The actual cause of death may not be COVID-19 in all cases."
The article muses "By this PHE definition, no one with COVID in England is allowed to ever recover from their illness." In fact the UK has always listed 0 people as recovered - I suppose it's some kind of definition issue, related to England's long-term view at the time.
"A patient who has tested positive, but successfully treated and discharged from hospital, will still be counted as a COVID death even if they had a heart attack or were run over by a bus three months later."
One of those causes is worth keeping track of - people who "recover" from covid, or get it with no one knowing, seem to die from strokes, heart attacks, diabetes, etc. at alarming rates within a short span of getting the virus. This might be why 'recovered' survivors are dying at 8x the usual rate.
"One reasonable approach would be to define community COVID-related deaths as those that occurred within 21 days of a COVID positive test result."
I'd suggest the 28-day cutoff they use is already too short to catch the full picture. What would be best is to filter by cause to eliminate unrelated deaths from possibly and clearly related ones, with a longer time limit, if any.
"...there is a persistent pattern where ‘out of hospital’ data are contributing hundreds of additional deaths to the daily figures, and this shows no signs of letting up. Indeed, there were >100 non-hospital daily deaths recorded on 14 occasions over the last 30 days."
This isn't just a problem causing high numbers to appear - it also means many people are dying not long after their supposed recovery-discharge point.
Data Review
What, when, how, and where do they count?
What: a death following a confirmed Covid-19 infection.
When: within 28 days, leaving covid survivor a pretty high-risk distinction.
How (cause of death): pending clarity, this includes any kind, including totally unrelated. By the scale of things, the bulk of later deaths seem related, but not all of them. Clarity would be good.
Where: at one point, just patients dying in NHS hospitals were counted. In late April, a huge number of deaths in care homes were included, and new ones were counted from there. Two other categories: "home" and "other" were tallied by the Office for National Statistics, but less so by the regional Public Health services. It seems that currently in Wales, "hospitalised patients in Welsh Hospitals or care home residents" are counted, but not ones at home or other. In England and Scotland, deaths "in any setting" are counted. In Northern Ireland: unclear.)
Graphic: ONS count including all settings, as PHE and PHS claim to, had a count of 51,710 "deaths involving COVID-19" just in England and Wales, up to 31 July vs. 46,193 for all UK on 7/31 (basic PH number as listed on the Bing tracker). Diff = 5,517, plus Scotland and Northern Ireland difference. I'm still not sure what that reflects.
Official tally revised to 28-day cutoff, redone in shades of red (source: https://coronavirus.data.gov.uk/deaths)
Now that overlaid on the ONS graph ... and it does seem the BBC plotting has the zero line drawn at more like 10 - it needed to be shifted up like that in order to fit, and the last tolls here are more like 10 than zero (shortly after this dropping to about 6 or 7 most days). This is a bit confusing, specially with two different but similar-looking 4-bar systems at work.
A simpler view with takeaway (and note the pink difference might also include some other difference, like location of death):
The lower and near-zero official death rate is lauded as a sign Covid-19 is no longer harmful, or that the UK is now "herd immune," or at least that they have the tricks down of "protecting the vulnerable." It's the latter, except it means vulnerable to dying VERY QUICKLY; a death 29 days or more after infection just doesn't count. But as we can see, the current bag of tricks is not doing as good a job of protecting those vulnerable to dying from it a bit slower.
Now for excess deaths - what may be quite a bit of the same thing, absent the verified infection. This issue is widely noted in the mainstream media, professional circles, and some governments as an indicator of the true death toll. Wherever Covid19 strikes, there's an unexplained surge of above-average deaths (causes often unclear but likely related when known). Some places like Belgium and New York seem to have a fuller (if more speculative) counts that include likely/or presumed Covid deaths, while other places (especially densely-populated and poor cities) can't keep up with cases or deaths, and have several times what's suggested by their massive and still-incomplete excess fatalities - if they compile them at all. My main points and work on the subject.
The UK is not unusual in having something like 50% more than predicted emerge on analysis (I had found around 70,000 total was likely, based on a graph at the Economist with a crude baseline adjustment (explained there, but even better below), compared to a smaller tally than I'm using now, to get about the same proportion anyway.) That issue is likely playing out in every hard-hit country, but maybe to a lesser degree in most places. An analysis by the Guardian found the UK had the biggest surge in excess deaths of 11 nations examined (10 European nations including Italy and Spain, and the United States) The UK surge was barely worse than Spain's AND included the unusual England count now under review, so that whole contest could be re-called. How DO Italy and Spain do their counts?
Consider this chart of 5-year fatality rates in UK (as seen in the weekly reports found here).
The downward revision let me see an interesting pattern: covid survivors (past 28 days) have been dying at about 8x the usual rates for England. That highly suggests most of these past-28 deaths are from its damage, and supports the possibility of the same happening, at a lower rate, among the vast number of unconfirmed infections out there.
To establish the average death rate on the smallest scale possible, I found this government site allowed that. I grabbed the weekly reports for 2020 (which include evolving versions of that 5-year graphic), and for the same weeks from the previous three years for comparison (previous years are linked at the bottom).
https://www.gov.uk/government/statistics/weekly-all-cause-mortality-surveillance-2019-to-2020
As an example of the given wording, the final week (Aug. 10-16) says "In week 33 2020, an estimated 9,392 all-cause deaths were registered in England and Wales," plus other details. For each week I compare that (notedly provisional) number, vs. a 3-year average for number of deaths in the same week, 2017-2019. For week 33, it's 9,074 (week 33 2019: 9,093 - 2018: 8,830 - 2017: 9,299). Comparing the expected average to what happened gives an excess of + 318. (then a daily portion, for plotting - total divided by 7 = +45). This is the first week in a while to be above average - likely due to a heat wave, but as I'll show, It might be a bit deadlier than the same heat wave if it had come last year, before Covid-19 and all the human wreckage it's caused.
My own three year average is probably a bit higher than a longer average would be, including the bad flu year 2017-18, with the year before also looking a bit high, the one after normal-low - see chart above. But for some weeks it may be low (2018 has some weeks missing, so it's a 2-year average there, skewed less deadly. In retrospect, a 5-year average would be better, but this is it for me for now).
Quickly, all my weekly excess vs. average findings, with yearly samples in just some cases (tedium), starting at week 8:
week 08 to 2/23: an estimated 10,841 all-cause deaths vs. 11,744 3 yr. av. (2019: 11,295 - 2018: 12,142 - 2017: 11,794) Excess: -1,263. Daily share: -180
week 9 (to 3/1): 10,816 vs. 10,985 3ya. Excess: -169 daily: -24
week 10 (to 3/8): 10,895 vs. 11,657 3ya. Excess: -762 daily: -109
week 11 to 3/15: 11,019 vs. 11,351 3ya. Excess: -332. Daily: -47
week 12 to 3/23: 10,645 all-cause deaths vs. 10364 TWO year average (2018 missing). Excess: -190. Daily: -27
week 13: 11,141 vs. 10,243 2ya (2018 missing). Excess: +898. Daily: +128
week 14 (4/5): 16,387 vs. 10,033 2ya (2018 missing). Excess: + 6,137. Daily: 877
week 15 (4/12): 18,516 vs. 9,392 2ya (a diverse 10,291 in 2019 vs. 8,493 in week 15 2017) or better, 10,250 from another try with monthly totals 5 years back - April divided into 7/31). Excess: +8,838 (using the latter number). Daily: +1,263
week 16 (4/13-19): 22,351 vs. 9,640 2ya (2018 missing) excess: +12,711. Daily: +1,816
week 17 (to 4/26): 21,997 vs. 10,424 3ya (2019: 10,059 - 2018: 10,306 - 2017: 10,908) Excess: +11,573. Daily: +1,653
week 18 (5/3): 17,953 vs. 10,141 3ya. Excess: +7,812. Daily: +1,116
week 19: 12,657 vs. 9,457 3ya (2019: 9,055 - 2018: 8,624 - 2017: 10,693). Excess: +3,200 - daily: +457
week 20 (to 5/17): 14,573 vs. 10,234 3ya. Excess: +4,339 - daily average: +620
week 21: 12,288 vs. 9,987 3ya. Excess: +2,301 - daily: +329
week 22: (5/30): 9,824 vs. 3ya 8,246 (8,260 in week 22 2019 - 8,147 in 2018 - 8,332 in 2017.) Excess: +1,578 - daily: +225
week 23: 10,709 vs. 9,952 3ya. Excess: +757 - daily: +108
week 24 (6/14): 10,709 vs. 9,385 3ya. Excess: +1,324 - daily: +189
week 25 (6/21): 9,339 vs. 9,447 3ya. Excess: -108 - daily: -15
week 26 to 6/28, 8,979 vs. 9,352 3ya. Excess: -373 - daily: -53
week 27 to 7/5: 9,140 in vs. 9,194 3ya (2019: 9,062 - 2018: 9,258 - 2017, an estimated 9,263). Excess: -54. Daily: -8
week 28 to 7/13, 8,690 vs. 9,283 3ya - Excess: -104 - daily: -15
week 29 to 7/20: 8,823 vs. 9,107 3ya (2019: 9,080 - 2018: 9,127 - 2017: 9,113) Excess: -284. Daily: -40
week 30: 8,891 vs. 9,045 3ya. Excess: -154. Daily: -22
week 31 to (to 8/2) 8,946 vs. 9,124 3ya Excess: -178. Daily: -25
week 32 (to 8/9), an estimated 8,945 vs. 9,160 3ya. Excess: -215. Daily: -31
I plotted these in lavender-blue behind the same confirmed curves and to the same scale. That's based on daily tallies, so I take the weekly excess divided by 7 and projected evenly over a 7-day block for comparison. And in the backdrop I show confirmed cases (orange) and possible unconfirmed (gold). Pale, blurred, and not to vertical scale, these are just for a basic idea on the timeline why there might be unknown covid deaths this far back - starting from two confirmed cases on 2 February that had been listed at the Bing tracker 'til mid-April, but don't don't appear now. We'll come back to this shortly.
These weekly totals can't be read too literally, especially with unexplained dramatic variance, they may reflect lags in reporting where one week is short and the next catches up. Especially at the peak, where the system would be overwhelmed, the greater number probably died in week 15, but far more come out under week 16. But there is also a rise of excess deaths then, and the past-28 day covid deaths are proportionally higher in that same time, the week or so after the main hospital-based peak. So maybe it did add up to more dead in week 16. Unless week 15 carried over to 17 as well, it seems there was a lot of ongoing death in all three weeks, much of it not captured in the official toll.
Again, these totals refer just to England (pop. 55.9m) and Wales (pop. 3.23m), so missing a similar portion for the smaller populaces in Scotland (5.5m) and Northern Ireland (1.908m). I find Scotland has 9% of the combined E+W pop. and NI has 3% E+W. That's 12% between them, but both likely have lower rates of this problem than either England or Wales, so I'll say add a combined 10% addition is a fair estimate, wherever that comes up.
All UK est. excess. from 2/24 (all + added and - subtracted) = 58,863 (E+W) + 10% (est. S+NI) = 64,749 above-average deaths, likely vast-majority covid-caused. This basic number has been widely reached and reported on as the best benchmark for how many the virus has actually killed in the UK. Excess deaths past 65,000 was in fact widely reported on June 23, while I only get that as of August 16. I think they start counting later, include less of that pre-infection low I include, since I'm also considering a "baseline adjustment" that plays into.
Baseline Adjustment: Some have noted a likely or actual added drop in non-covid deaths, and I think this is worth considering - the applicable baseline for non-covid deaths isn't just average but apparently well below that. See UK 5-years mortality graph above, where the past 4 years had NO Covid-19 deaths, as it didn't exist. Note the huge spike this year is preceded and followed by dips below average, the former being quite sharp, and the latter managing this despite the ongoing covid deaths, so all other causes combined must be well below the 3-year norm. (The same dips can be seen in more detail in my graphic.) Consider the yearly hump in winter is mainly down to influenza, etc. and these deaths tend to fall as soon as people start distancing (+ usual infection-death time lag). Deaths from traffic accidents and violence will be down when people stay home. Some other classes will fall and others will rise, on balance coming out below average.
So if the applicable baseline is below average/zero, just counting the deaths above that line will miss the others between it and the actual baseline.
The exact "actual baseline" can't be told without more detailed data, but I tried a reasonable guess, connecting the lows before and after the spike with a little inference. The early dip in late January may be related; it seems like flu deaths were slightly above average, but just as news of the spreading virus in China came in at least, may have the most vulnerable groups being extra careful and already preventing many flu deaths. A sharp drop is noted the week Jan 20-26, just as Chinese authorities admitted what many suspected - Covid-19 was communicable and might have spread anywhere by now. Actual cases soon appear in the UK (the redacted ones of Feb. 2 would be reported - looking it up, on January 31 in a mother and son from China were confirmed and noted on January 31, and a woman in Nottingham "may have caught it as early as February 9" (community transmission) (DailyMail) A Wikipedia article notes earlier likely appearances in mid-December (via China) and mid-January (via Austria).
This might be enough news to sharpen the same point and explain a month of lows with a big dip in week 8 (Feb. 18-23) to more than 1,200 deaths below average, quite possibly despite a few early covid deaths. It's even possible these were happening earlier yet, in that apparent spike of flu deaths already going as the year started. But seeing it dip even lower when thing WERE heating up, then seeing it quickly pushed up from week 9 forward, as if in an immediate prelude to the coming mega-spike - well, that seems like a good spot to start counting likely covid deaths above that low baseline, which I set by roughly averaging weeks 7 and 8 (noting that weekly variance).
The pink baseline I drew in here may start too low, but upon lockdown declared on March 23, following distancing advice by March 16 and general warning preceding that, deaths should reach a low similar to what's shown - around 160/day less than average. In many places, the shock of the orders makes for wide compliance for about a week. From there it would creep up as people realize the world wasn't obviously ending and start moving about more. Some get the idea and find a safe balance, while others increasingly question, protest, and defy mitigation efforts. Some of them will die, but at a much, much lower rate than they were about to.
Also in play are the negative effects of lockdown - lost wages, suicides and overdoses, canceled medical care, etc. will have death tolls that variously rise and fall over time. But considering how it all comes out below average so far, these don't seem to add up to very much yet. I don't know how much to add or where, except mainly it's not much, and takes a while to emerge - "lockdown" won't explain the sudden and massive spikes of death just as the virus was taking off and only starting to come under control. That will be the virus, people.
Anyway, after drawing in the new guessed baseline, I would draw a light blue box just a bit smaller than the distance to the 0 line or below-average total. Since most tallying here is done above zero/average, I add these to the top of the blue bar to clarify it's a proposed addition. In context, the additions seem reasonable -we already have so many deaths above average, it shouldn't be too surprising there are even more. I wouldn't be surprised if my adjustments were too big, or too small. But until such knowledge exists, this seems like a good guess, and either way, the bulk of it, some 65,000 deaths above regular average, still have no better explanation.
In essence, just as the confirmed cases yield these past-28 days fast-dying survivors, and the bulk of other excess deaths are the equivalent from the vast number unconfirmed cases. Those will tend to be milder cases than the known ones (largely found in hospital upon admission for extreme symptoms). But it's only natural the same kind of damage occurs with the unknown cases, probably in a much lower proportion, but one that still adds up.
Estimate: ~80,000 Total Deaths
Adding all the plusses and subtracting the minuses for weekly deaths:
All UK est. excess. from 2/24 to 8/16 = 58,863 (E+W) + 10% (S+NI) = 64,749 most of them likely caused by Covid-19.
Baseline adjustment all UK, 2/24-8/16: 18,249 (E+W) + 10% (S+NI) = 20,074 total deaths *below the zero line*, most of them likely caused by Covid-19.
Total est. excess, all UK 2/24-8/16: (64,749+20,074) = 84,823
Besides the actual baseline, the actual Covid-19 portion of that huge total is harder to call. I mean "most," and I'm including deaths the virus only had a direct hand in. I'd say a conservative minimum is 80%, and I suspect 90% is more like it, and will give a broad range of 80-96%, personally favoring the upper half of this range:
if 80% = 67,858
if 90% = 76,341
if 96% = 81,430
This allows me to offer a rough estimate of around 80,000 people actually killed by the virus - one way or another, with or without the help of CO-morbidities (which had been just conditions until the virus transformed them). That's across the U.K. and all settings, but only so far. That's at least 64% and as much as 97% above what's now counted (41,410 at 8/16). It comes to at least 1,005 dead per one million populace (or 0.1% dead) and as much as 1,185/m, which would be one of the highest national death rates in the world.
... "sorry, but just a few percent more"
August 24, 2020
edits/adds 8/26, 31
There are two ways Covid-19 will leave a place, even temporarily - the exit it came in by (China and New Zealand have done this well, battling it down to effective zero, not that stays right there on its own) - and the exit that runs through us, the "herd" - we live with it up-close and even invite infection until we achieve an overall balance of natural immunity. This is the approach favored by experts and others who favor freedom, are long on distrust of authorities, and/or don't grasp just how harmful this virus can be.
Some serious work went into this study, for example, to conclude from "unfolding epidemics" and careful application of mathematical formulas, "our inferences result in herd immunity thresholds around 10-20%" as Dr. Eli David quotes it, noting as usual how the utopia of Sweden is believed to have proven it.
The basic idea of how "herd immunity" works - normally, and as I gather - is after the virus hits more brick walls of immunity more than open paths, and that dynamic has a little time to play out. At the start, with everyone susceptible (perhaps with some exceptions), and assuming a reproduction rate R between 2 and 3, which it often is (it varies with all kinds of details) - as they say you infect 2-3 people, who each infect 2-3 on average, and so on. At the HI threshold, you most likely infect no one, because a solid majority of those you bump into are immune (be it exposed, vaccinated, or maybe cross-immune from something else). I can see how under some circumstances and with some contagions, the slowed rate required might come about with less than 50% immunity, but as we'll see, that doesn't seem to be the case with this virus.
Whatever the exact level, the one that works is where it too few places left to go. Less infected, less susceptible, and more immune between them is the formula. It still allows for some transmision, but probably just to one person who has the same issue, passing it to maybe one person or no one, who (if one) in turn passes it to maybe one or zero, and again as needed until it's just zero. At this point, cases will resolve faster than new ones are made, and eventually a last case will recover without infecting anyone. And ideally something like 25-35% of the populace was able to avoid being infected.
There might be some validity to the new science suggesting it can be done with as little as 10-20% infected, 80-90% not infected. All such plans seem to involve some special shielding of "the vulnerable" (hopefully defined well, and hopefully the shielding ... you know, happens), and maybe some other special tricks about mixing up immunological homogeneity, and getting the pace right to avoid flooding the ICUs again. But we'll need more than some validity, some calculations, some ifs and maybes we likely can't guarantee.
We don't want to expose a solid majority of people to this. And the herd immunity promoters insist we probably won't have to. Increasingly, it seems, people are agreeing these 10-20% figures seem credible. But we'll need evidence it might work out this way before we agree to toss that many under the bus, we want a clear-eyed view of what's ahead, how many more will have to get off the bus to get us more traction of a slope that's steeper and muddier than was calculated.
They provide evidence - just look at Sweden, usually as some other Herd Immunity supporter understood it to be back in April, all safe and sound at the edge of herd-vana with 15-30 infected. Sweden's policy planners had bragged of high infection around 30%, low mortality rates considering, and impending herd immunity almost from the start - adoring fans never double-checked or followed up. They just kept repeating the urban legend of the amazing Swedish success story. But the continuing data is clear they haven't reached herd immunity - that's measured in LESS INFECTIONS, not in less-fatal ones. many still hold out hope they'll achieve it after hitting 20% infected, which may finally be close, but it seems the Swedish herd has lost its enthusiasm for the project. And good thing - they might have only reached 20-30% of the infections they'd need.
Another example: Nicholas Lewis back on May 10 favored a level between two models giving 7% and 24% - pointing to Sweden as an emergent example thought close to that level. It was soon shown 7.3% didn't do it. They may be close to 24 by now, and we'll see.
Maybe they need to reconsider some variables and re-do the calculations - maybe it's 30-33% we need - keep going - more fatal 3rd wave brings it to 37% and it only slows when they make it slow, yet again, to ask the pandemic managers what gives? Okay, carrying the one and re-checking some factors - sorry, it might actually take a near majority - 44-48% FOR SURE guys, now keep that economy moving full tilt, and keep that virus moving, not so slow but not too fast. ICUs need to keep up. A few months later they hit 52% and AGAIN it's not slowing on its own. As they wait for the next math fix, they might notice thousands more have died, covid confirmed. Thousands more unusual mystery deaths have also piled up, and cardiovascular and pulmonary ailments and deaths have risen to puzzling levels. Even the dumbest populace would refuse the march by this point, happy enough to have never found that tipping point, only wishing they'd never been fooled into trying. Some things are best left undiscovered.
The pandemic managers behind that might reflect, as they dangle from light poles, how they could have just learned from the following examples available by now, if not at the time of the rosy predictions of April and May people are still recycling:
(note: antibodies known to lapse over time to some degree, so the actual numbers infected should be somewhat higher than indicated in these cases. )
* Diamond Princess cruise ship: 3,711 passengers and crew - 712 cases (19.2%) - 13 or 14 deaths. Cabin lockdown seems to have halted transmission at under 20%. This has been taken as showing herd immunity, presuming the ventilation system would keep blowing the virus to everyone, yet infections barely rose. But this has no logic - the virus won't refuse to infect a person in one cabin just because 20% of people in other cabins have been infected. The simple answer is that it's not airborne like that, as we already knew. It was the isolation of people that kept it spreading further, and kept it from a good test of how high it can go, except to say it doesn't automatically stop much short of 20% (seriously questioning 10-20% predictions that top out where reality seems to bottom out).
* May 14, 2020 · "Over 25% of the UK likely to have had COVID-19 already" - probably not, but ...
* "Antibody prevalence in New York City, for example, jumped from 6.9 percent in late March to more than 23 percent in May" CDC round 2 found 23.2%. Jun 30, 2020 · "Around 25% of people in the New York City area have probably been infected with the coronavirus by now." It had slowed down, stayed slow, but never did go away - control measures were serious here, hard to discount as the major cause. But maybe they did happen to hit herd immunity around then? Maybe others would do the same?
* Aircraft carrier USS Theodore Roosevelt: as of last news on 5 May, at least 1,156 cases out of ~4,500 crew = at least ~25.7%. This doesn't push our envelope very far. Some might quit following here and run off the celebrate the news that Covid19 fizzles out at about 25%.
* New Delhi, India, August 20: "Nearly 30% of residents in India’s capital New Delhi have had the coronavirus, according to a seroprevalence" 29.1%. Was 23.48% on July 10. Maybe it's 25-30% needed then?
* Trousdale Turner Correctional Center, Tennessee: prison capacity: 2,500. Inmates tested: 2,444 (sounds like all of them). Positive: 1,299 (53.15%) + 281 Staff tested, 50 positive. Of course this is a prison, not a populace under lockdown in private homes. It's not a place with millions - like India.
* Pune, India, Aug. 17: "seropresence tests show 51.5% of Puneites have developed antibodies" It can keep going til it hits more than half the populace? That wasn't predicted. That could be a disaster. Did they do that survey right?
* Bergamo, Italy: "Of 9,965 people in Bergamo who had their blood samples collected between April 23 and June 3, 56.9 percent had antibodies against the virus" Maybe they do something differently in Bergamo - it does seem especially hard-hit, even by the standards of Italy, or of the Lombardy region it's in. Unlike those places in India (as far as we know), a lot died here - some 0.5% of the population, at least. And at leas 57%, likely over 60% before antibody lapse, managed to get infected, with no herd immunity emerging to prevent it.
* Mumbai, India: July 29: "COVID-19 antibodies found in 57% of Mumbai slum residents in limited serosurvey by NITI Aayog, BMC, TIFR" (but just 16% in other areas). Okay, maybe Bergamo's an outlier, and there's something odd with Indians and their susceptibility. If so, too bad - there's a lot of them.
* Guayas/Guayaquil, Ecuador: I didn't find anything to say what level of seroprevalance there was in this very hard-hit area of Ecuador (its temporary capitol after recent troubles). I would guess it's about on par with Bergamo and perhaps worse, considering the enormous excess deaths they suffered just then. There was a study into it I found, said to be complete, but with no findings presented. I'll keep an eye out.
* Life Care center, Kirkland, 120 residents, 25 of them died from a Covid19 outbreak in early March. They didn't stop at 20% infected but at 20% dead. "A March 18 report from the Centers for Disease Control and Prevention found 129 cases of COVID-19 were associated with Life Care Center of Kirkland. This included at the time 81 residents (67.5%), 34 staffers and 14 visitors." But this is a bit like prison, full of frail people in the worst age-bracket and mostly with prior conditions. As such, they might also be especially susceptible to infection.
* Federal Corrections Center Lompoc, in California "which houses 1,162 low-security inmates." "Nearly 70% of the inmates at Federal Correctional Institution Lompoc have tested positive, exploding by more than 300 in recent days" to a total of 792. 792/1162 = 68.16% Again, that's a prison. Let's hope it's just old people, northern Italians, poor Indians, maybe Ecuadorians, and prisoners who have these unusually high ability to keep on getting infected.
* May 5: Elayn Hunt Correctional Center in St. Gabriel Louisiana: "155 women without symptoms were tested after 39 became ill" - "192 inmates had tested positive, including 66 who had symptoms" "according to Department of Correction statistics. The unit has about 195 inmates, though the number fluctuates, Pastorick said Monday." 192/~195 = ~98.5% infected here, or the number had fluctuated higher.
That last one might be too high; there might've been more than 195 tested (though it's not likely to be much higher), and the results could include false positives. Any of these might be, I suppose. But AFAIK there's no rule saying infection must stop at the HI level - which cannot be this high. In a prison, maybe it just doesn't slow down the same way it would in a spread-out populace - it can bouncing off the same limited surfaces from prisoner to prisoner until it hits everyone.
There are a lot of factors and variable going into saying just what level would work for nations and cities that mostly go over my head. I'm happy with that, especially as I see experts SEE EXPERTS USE THEIR EXPERTISE TO predict outcomes far off from the reality. Just from what I've read, followed, and reasoned, I'd say the HI threshold is well above the 50-60% levels found in parts of Italy and India - probably well below the near 100% it can get to - it will vary on the population and just how they go about their business, but the expert views predicting the usual ranges around 60-70% and above always made the most sense, and better predicted the kind of levels emerging, and that we'll be seeing and more of.
One more thing I'll say about the HI level for Covid19: I don't want to see anyone find out what it is by a real-world trial. If it happens by accident somewhere, let's learn from it. But no one should be aiming to make this happen.
8/26: Update: Hard-hit Bergamo enjoyed, on the 22nd, its first day since the start with zero new confirmed cases. They had a few per day before and after, but it's been extremely low, and seems to be about as slow in other regions (perhaps regardless of exposure rates, or independent of any herd immunity). I got curious and dug up some stats from a few sources, all referring to the Bergamo region of Lombardy province, with a population of about 1.12 million. (there's also a Bergamo city of 122,000, which population I once used with deaths in the region to miscalculate 2.5% dead in the city. Rather, it's about 0.5% dead in the city (some 670) and almost as high in the region (was rounded-off to 3,000 dead, later to 6,000 dead - more exact numbers in time).
10%, 20%, 36%, and 50% estimated exposure marked, using the crude formula of 236.23 cases / 1% infected that gives the result recorded June 3: 57% or more infected, at just 13,465 confirmed cases. It would mean some 46 unconfirmed cases for every confirmed one - inexact, but seroprevalence tests often find ratios similar to that. We can see barely-suppressed acceleration at 10 and 20%, what seems to be control efforts starting to win at what happens to be around 36% infected, and just moderate change from there through loosening controls (after which levels rise a bit faster) to 57% and to August 25, where I use the 15,269 cases to calculate less than 64% infected (noting spread should be slower on average after June 3 than it was before). Anywhere past 50% immune, the rate of spread is bound the affected at least a bit, and it might be a major factor by around 65%. But nowhere in here do we see a significant change in rates of infection - and thus of deaths and bodily damage - caused by mass immunity. This is the same kind of curve we see everywhere else, just more extreme: a deep, widespread outbreak was slow to bring under control, even with unusually strict measures hardly anyone but China can still match. But finally it was brought under control, at great cost of course, and might just remain totally manageable from here, so long as people stay vigilant.
sources:
https://github.com/pcm-dpc/COVID-19/tree/master/schede-riepilogative/province
http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1
Update Aug. 31: this being my antibodies-seroprevalence spot, there will be more of that, and I'll try to note and add it here. Already some updates:
* Pakistan found to have 11% with antibodies. That doesn't say much, but it's a national average. Rates in cities like Karachi and Islamabad must be far higher, but I haven't seen where these are reported. One expert estimates (or knows?) about 40% in Karachi have been infected.
* Ischgl, Austria: 42.4% - Aug. 25 report on a wider study at this Austrian ski town famously hard-hit by infections, if not deaths.
* New York City: 27% average, areas past 50% - An August 19 NYT report cites a city study that found gigher levels of antibodies than previously known. A map shows huge swathes of the city above 35%, and gives 27% as the city-wide average.
"In one ZIP code in Queens, more than 50 percent of people who had gotten tested were found to have antibodies, a strikingly high rate. But no ZIP code south of 96th Street in Manhattan had a positive rate of more than 20 percent."
"Across the city, more than 27 percent of those tested had positive antibody results. The borough with the highest rate was the Bronx, at 33 percent. Manhattan had the lowest rate, at 19 percent."
High points labeled on the map:
E. Elmhurst 45.7%
Borough park 46.8%
Corona (?!) 51.6%
"Prof John Newton, director of health improvement at Public Health England (PHE), said: "The way we count deaths in people with Covid-19 in England was originally chosen to avoid underestimating deaths caused by the virus in the early stages of the pandemic." But he said the new methods of calculating deaths from the virus would give "crucial information about both recent trends and the overall mortality burden due to Covid-19".
"The new methodology for counting deaths means the total number of people in the UK who have died from Covid-19 comes down from 46,706 to 41,329 - a reduction of 12%. And figures for deaths in England for the most recent week of data - 18 to 24 July - will drop by 75%, from 442 to 111."
