Early ChinCOVID

I’ve thought for months that SARS-CoV-2 was already widespread long before the lockdowns began, making them destructively pointless. And I’ve collected a fair bit of data to support that, including a case in Washington in December. Well, here’s another data point.

Coronavirus was in northern Italy in December, officials reveal after studying wastewater
SARS-Cov-2 RNA (ribonucleic acid) was found in samples collected in Milan and Turin on Dec. 18, 2019, and in Bologna on Jan. 29, 2020.

Detectable levels of RNA in wastewater indicate a lot of infected people, not just one or two. Community transmission was in full swing in Milan by 12/18/2019.

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NBC and NYT still trying for ChinCOVID panic

Ah, NBC; that non-biased COVID-19 fact-checking Trump on COVID-19.

COVID-19 cases are on the rise in 21 states, according to data compiled by The New York Times. There is some indication that expanded testing is catching more cases, but public health experts say that in reality, the surges are due to states’ reopening and people’s relaxing their social distancing protocols.

And Ms. Timm’s source for that, the NYT, is still full of bovine excrement.

Despite even their graph showing a clear decline in Georgia’s new daily cases, they claim we’re still “mostly the same.” BS.

Georgia’s new cases have definitely declined. What the NYT isn’t telling readers is that 6,207 of Georgia’s 53,249 cases — 11.7% — are antibody-positives. By definition, even though they are reported as post-lockdown, they are old cases. Antibody testing largely started just after the lockdown lifted. All of those represent old cases that should be reported as “new” on past dates. Instead, the state is graphing viral and antibody testing together, reporting antibody as new, and creating an imaginary post-lockdown surge.

A proper viral-positive curve would show an even higher peak, and a rapid decline. Not a double peak.

And if Timm at NBC, and the NYT, were interested in truth and facts, they could have noted that antibody testing suggests that 5.9% of Georgia’s population — 626,428 — had ChinCOVID, never knew it, got over it, and developed immunity. Add that to the known 47,974 cases, and that brings Georgia’s COVID-19 mortality rate down to 0.34%.

Oh, well. I probably can’t expect unbiased accuracy from Timm.

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Yet Another Early-COVID Datapoint

I’ve thought that SARS-CoV-2 was circulating widely long before people realized it; among other things, thing making the lockdowns pointless. Barn doors, horses; you know the drill. I collected quite a few bits of data to support that hypothesis, including confirmed community spread in Washington state back in December 2019 (before China even announced it).

Now we have this Harvard study suggesting it was becoming widespread in China last summer.

The global COVID-19 pandemic was originally linked to a zoonotic spillover event in Wuhan’s Huanan Seafood Market in November or December of 2019. However, recent evidence suggests that the virus may have already been circulating at the time of the outbreak. Here we use previously validated data streams – satellite imagery of hospital parking lots and Baidu search queries of disease related terms – to investigate this possibility. We observe an upward trend in hospital traffic and search volume beginning in late Summer and early Fall 2019. While queries of the respiratory symptom “cough” show seasonal fluctuations coinciding with yearly influenza seasons, “diarrhea” is a more COVID-19 specific symptom and only shows an association with the current epidemic. The increase of both signals precede the documented start of the COVID-19 pandemic in December, highlighting the value of novel digital sources for surveillance of emerging pathogens.

It’s purely statistical, and doesn’t prove anything, and China denies it. Call it confirmation bias, but it is consistent with all the other things I found.

It would certainly explain why 5.9% of the tested population in Georgia is already positive for SARS-CoV-2 antibodies.

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IHME Model: Declining into utter bullshit

I’m now checking the IHME Georgia COVID-19 model just for giggles. These dishonest scumbuckets are getting further and further from reality. And I’m not even talking about their projections.

IHME currently claims that Georgia saw 965 confirmed infections on June 4, 2020. Confirmed; not model projection.

Georgia DPH says…

89

So from which stinking orifice did IHME pull an additional imaginary 876 cases? Cases that the state agency that gathers and reports this data doesn’t know about?

Just for scale, while IHME is claiming 965 cases that day, Georgia reports that its peak new cases day was April 20, with 950.

As for deaths, IHME is still holding deaths in reserve to falsely maintain a fake curve. IHME claims 2,084 had died by June 3, while the state says 2,159. While that might seem optimistic on IHME’s part, you have to remember that they’ll maliciously report the extra deaths on a later day to make it look like lots of people are still dying. For instance…

Daily deaths, June 3
IHME: 29
GA DPH (the source of the real data): 9

Georgia has not had 29 daily deaths since May 12. And it’s been dropping since. It was declining then.

The IHME model is fraudulent. If it isn’t deliberate as I think, they need to show the source of their alleged data.

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Georgia: No Post-Lockdown COVID-19 Surge

As of 6/2/2020, 5:49:29 PM, Georgia has recorded 48,207 cases of COVID-19 positive tests. And the new cases graph is showing an impressive post-lockdown surge. So why does my post title say the opposite?

Because GA DPH is finally showing separate tallies for viral (active infection) and antibody (post-infection/recovered) tests. 5,395 of the positives were antibody tests. Antibody testing started after the lockdown ended. And that matters because…

To test positive for SARS-CoV-2 antibodies, one must be exposed to the virus, be infected enough to stimulate an immune response, begin producing the appropriate antibodies, and produce enough to be detectable. The entire process can take weeks. That means any positive antibody test represents someone who was infected before the lockdown ended. They’re simply reporting those as new, post-lockdown cases. But in reality, they have no idea when they really occurred (as opposed to finding out about them).

DPH is still graphing viral and antibody testing together. I wish the idiots would separate those out. But a SWAG at the numbers strongly suggests that nearly the entire post-lockdown bump was really antibody-positives.

There was no post-lockdown surge in new cases; it was a surge in reporting, as predicted. The lockdown was pointless from a public health perspective. Kemp locked down Georgia three months after community transmission had already started in the US (despite CDC claims that it was late-January/early-February).

Another interesting point about Georgia’s antibody testing: 5.9% of those tested were positive. Remember; those were people who’d never had any symptoms to speak of, or they would have had viral testing before. COVID-19 has spread across the entire state; viral testing showed cases in every county. Georgia’s population is 10,617,423. Extrapolating, it’s very likely 5.9% of the population would be antibody-positive.

626,428

Georgia has reported 2,102 COVID-19 deaths (never mind for now that we know that number is inflated). That gives us a COVID-19 mortality rate of 0.335%. One-third of one percent. One-tenth of the 3.4% WHO claimed. The vast majority of whom were elderly and/or infirm (and many of those wouldn’t have happened if some states hadn’t decided nursing homes full of the elderly and/or infirm were a good place to stick the infected). But the powers that-be-locked down the entire younger working and student population, crippling the economy for — hopefully only — years.

Here are some more numbers to play with: You’ve heard that over 108,000 have died of COVID-19 in the US. Do you know how many the CDC actually has ICD-10 coded as COVID-19?

86,495.

Yeah, the other 21,000 may have had (or once had) SARS-CoV-2, but that wasn’t what killed them. It was little things like murder, or surgical complications.

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COVID-19: January? TRY to keep up.

The CDC now says SARS-CoV-2 may have been spreading slowly in the US in late January.

The first U.S. cases of nontravel–related COVID-19 were confirmed on February 26 and 28, 2020, suggesting that community transmission was occurring by late February.

This is — among other reasons — is why I do not consider the CDC to be a good source for data. Aside from the fact that nation-wide outbreaks occurred that were too widespread and early to be consistent with a mere late January slow spread, we know that there were nontravel-related illnesses well before that.

• Ohio: Yes, Ohio, far from Washington. Antibody testing found a case dating as early as January 7. Patients in five counties spread across the state.
• Washington: Two days after Christmas last year; December 27. Nontravel. That’s when she went symptomatic. Exposure had to be a week or two before that, meaning it was spreading mid-December.

Yes, we know that SARS-CoV-2 was widespread in the US by late last year. Three months before the gov noticed, and decided to use it as an excuse for a totalitarian police state.

Do try to keep up, CDC. Here’s a collection of links to case studies indicating extensive — and mostly harmless — exposure long before the lockdowns.

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My COVID-19 prediction a month later

On April 23, I made a prediction: that Georgia would not see a post-lockdown surge in COVID-19 cases. You can follow that link for my full reasoning, but the short form is that I’d seen indicators that the virus was already widespread before the lockdown ever started.

By May 12, I saw some preliminary indicators that also seemed to support my view; to wit, we did not see a decline in the rate of daily infections which one would expect if the lockdown slowed exposures.

If we were going to see a new, post-lockdown surge, I thought it would start to appear approximately two weeks later, based on a roughly 14 day incubation period. The lockdown was lifted on May 1. I waited a little more than two weeks to give the state’s data time to catch up with local reporting. How did I do?

From that, you might think that my prediction of no new uptick was a complete failure. But wait.

As of this writing, that data is useless for confirming or denying my prediction. Georgia went and made some changes.

First, after the lockdown ended the state began offering COVID-19 screening to anyone. Previously, it was only available for those displaying symptoms. Unless they can report whether post-lockdown positives were symptomatic or not, we don’t know if we’re seeing something other than what we would have if testing had always been available regardless of symptoms.

Second, and far more serious… that graph no longer reports just SARS-CoV-2 testing. It now includes post-lockdown antibody screening. That is, people who never even knew they “had” COVID-19, but had been exposed enough to develop an immune response. And since, not being sick, they don’t know what days the “cases” developed, they seem to be reporting an antibody positive on the day of the test. A person might have been exposed all the way back in January, but it’s reported as happening after the lockdown lifted.

The uptick could be asymptomatic cases we’d never have seen before, because the state wasn’t looking for asymptomatic cases before the reopening. It could be antibody positives. We don’t know how much of which.

The state now says they’ll separate viral and antibody positives and report them separately. Until that happens, my prediction remains untestable, damnit. But the deaths-per-day graph may be another proxy. As yet, that does not appear to show an uptick; the 7-day average curve still looks like a classic epidemic curve. The state also reports that COVID-19 hospitalizations are “down 34% since May 1st.”

Hopefully they’ll sort out that data soon.

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IHME Model May Not Be Fraud, Exactly, After All

I had a discussion with a reporter who had done a story claiming that there were 32 COVID-19 deaths, and 610 new cases, “in a span of 24 hours in Georgia.”

Since the Georgia COVID-19 Dashboard reported 2 new deaths and 6 new cases (as of 5/13/2020, 1:25:00 PM), I was a little curious where she got her numbers.

It turns out she was just looking at how much the “Confirmed COVID-19 Cases” and “Deaths” total numbers at the top of the page incremented, and apparently assumed all those cases happened in the previous 24 hours. I explained that the increment actually includes cases from different days. Due to delays in testing and reporting by counties and labs, it can be weeks before a case that popped up on 4/27 finally appears on the Dashboard. In fact, I picked 4/27, because yesterday that date did pick up some new cases, making the new peak day (previously the peak day was 4/20).

While older dates are fairly stable now, the numbers for the past two or three weeks can be fluid.

But then it hit me: I’ve called the IHME model fraudulent because they are clearly generating new peaks by lumping days worth of data together and reporting them as occurring on the same day… just like this reporter did.

The reporter is being sloppy, and gives the impression those cases/deaths occurred in that 24 hour period. That’s the kind of reporting that panicked the nation into an unwarranted house arrest. Great for clickbait, bad for informing people.

For IHME — if this is what they’re doing — it’s laziness and incompetence that’s totally irresponsible in an allegedly scientific endeavour.

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Elephant Repellent

Bob walked down the driveway on his way to check the mail, and was hit with a horrifically noxious odor. He glanced around for the dead whatever, and saw neighbor Tom with a garden sprayer carefully applying a repugnant something to his lawn.

“What the hell are you spraying, Tom?” he shouted. “That shit reeks!”

Tom straightened up, smiling. “It’s elephant repellent. Those suckers are hell on lawns.”

“Elephant repellent! Are you nuts? This is Georgia; we don’t have elephants except in zoos.”

Tom beamed proudly. “See? It works.”

---

I’m sure you’ve heard that, or some variant before. But now you’re living it.

IHME told us we all going to die if we didn’t “social distance” and go into house arrest. When the real world didn’t match the predictions (and never did), they said, “See? It works.”

Let’s take Georgia for example. DPH conveniently graphs cases for us.

This is what the statewide cases look like, and they’ve even overlaid the graph with lines indicating when large gatherings were banned and when we went into lockdown. If social distancing and lockdowns “worked” we would expect to see discontinuities in the trend line at those dates.

We don’t see the “expected” discontinuities. In fact, if you look closely, what you do see is:

1. Once the epidemic kicked in we had a sharp rise until March 19.
2. At March 20, we see the trend begin to slow slightly. That’s days before the large gathering ban.
3. From March 20 to April 11 — that’s through the gathering ban (March 23) and the lockdown (April 2), the trend is darned near linear.

This is an “ideal” model of an epidemic, based on typical spread of any epidemic.

Does that look familiar? If the lockdown et al “worked,” our trend curve should have topped out early, then run more less flat for an extended period of time. Instead…

We peaked and declined just like any other epidemic.

There is a final test of whether the draconian measures “worked.” If the lockdown was making a noticeable difference, then approximately two weeks (SARS-CoV-2 incubation period runs around 3-14 days) after it ended in Georgia, we should see a significant uptick in new cases, as people “start” getting exposed again.

So far there’s no sign of a an uptick, but while it could have started showing, it probably won’t for a few more days. And then reporting will have to catch up.

As I’ve said before, I don’t think we’ll see the uptick, for the same reason we didn’t see the “expected” discontinuities is daily cases: COVID-19 was already widespread long before the “Oh my god, we’re all gonna die” reactions.” As the Diamond Princess and Roosevelt case studies showed, along with random COVID-19 virus and antigen testing, the disease is widespread, and almost no one knows they had it.

Our glorious leaders sold us elephant repellent.

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This why the IHME COVID-19 model is fraudulent

I mostly avoid the crappy IHME model now, but I happened to see a reference to it today. A quick comparison to reality made it very clear what they are doing.

This is IHME for Georgia, deaths per day; solid red is allegedly the real numbers, and the rest is projected. Note that their total deaths projection is actually fairly close to reality. But also note their curve.

IHME claims Georgia peaked at 100 deaths per day, and continues to have high spikes.

Now this is the actual deaths per day reported by Georgia. Deaths per day gradually increased, accelerated, peaked, and has been declining since mid-April. It peaked at 48.

Why are the numbers so different? Georgia’s graph shows daily deaths. IHME doesn’t; they save up days’ worth of reporting, then graph those days’ worth as occurring on one day. Thus, the total comes close to Georgia’s reality, but they get to generate continuing spikes that, when curve-fitted and projected, give a much higher and extended curve than reality. They “projected” 45 deaths for 4/29 and 4/30, based on that curve. Reality was 7 and 3, respectively.

Falsifying dates of death to generate continuing peaks is fraud. They’ve had plenty of time to look at the real data reported through multiple sources. If it was merely an error, they had plenty of time to correct it. They did not. They continue to use false data. They have no excuse for not noticing that they’re claiming a peak over twice as high as Georgia’s actual peak; 100 vs. 48.

Since they also know governments were using their model to make plans to deal with the outbreak, and knowingly provided — are still providing — a falsified model, I think a criminal investigation is warranted.

Added:For the slow, here’s a simplified example of how counting incidents on the wrong day skews the model. The vertical axis is Cases. The horizontal axis is Days.

In this example, we had ten total cases, with 1 case occurring per day. That is graphed in black. The curve is flat.

But using IHME’s methodology…

Day 1: They report 1 case.
Day 2: skipped
Day 3: They added days 2 and 3, and reported both on day 3.
Day 4: skipped
Day 5: skipped
Day 6: Added days 4, 5 and 6 together, and reported all on day 6.
Day 7: skipped
Day 8: skipped
Day 9: skipped
Day 10: Added days 7, 8, 9, and 10 together, and reported all on day 10.

A much different curve, showing cases increasing now. But the total number of cases is the same.

Fraud.

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