Wuhan's House of Cards, part II: The Devil's in the Details
The epidemiology of Wuhan's outbreak is a case-study of outliers
*Note — My current focus is researching COVID-19’s origins, as part of the D.R.A.S.T.I.C. team of scientists, journalists & researchers. Recent news: D.R.A.S.T.I.C.’s research forms a large portion of the basis for investigations begun by the US Senate, House & National Institutes of Health. Recent appearances and/or discussion on 60 Minutes, The Joe Rogan Exp., Fox News, JRE [again], Bill Maher, & CNN. I am thankful for the efforts of @TheSeeker, @gilles , @Harvard2H, @DaoYu15, among others, for placing the breadcrumbs that encouraged me to begin digging deeper into this set of outliers.
SSgt Rixey (circa 2014):
“What do Marine General James Mattis and the hip-hop artist Macklemore have in common?”
The former is the most esteemed US military leader of this century, who led the 1st Marine Division to Baghdad in the spring of 2003; later, as the newly-appointed Secretary of Defense, led the renewed push against ISIS - and in 10 months eradicated that group which had plagued the Middle East for 4 years.
The latter wrote the massive hit single “Thrift Shop” [video below] , a song about finding ‘vintage’ clothing treasures at a local thrift store - including bedsheets that smell like urine and a pink velour jumpsuit. Thus, one could be forgiven for failing to detect an obvious connection between the two; one could also be forgiven for pondering the probability that COVID-19 might’ve caused me permanent neurological damage. My wife would probably support that hypothesis, lol.
But, I’m willing to throw another dumpster fire into the pot on this one - outliers actually helps explain the problem and the solution, as the excavation of Wuhan’s tragedy continues.
The answer is that both of them referenced and recommended a book called Outliers by Malcolm Gladwell, for the same reason - they are outliers themselves, and both are passionate about telling others what that means and what it takes to rise above the crowd. Ironically, outliers are often the trigger of a disaster, and during a disaster we need leadership outliers more than ever.
Outliers can be statistical or tangible [i.e. a winning lottery ticket, an honest politician, etc.] - and they can teach us a lot.*
*But,* only if someone is actually looking - it’s surprisingly easy to miss things you’re not looking for.
As I continue to accumulate and correlate data from the earliest days of the outbreak in Wuhan, outliers keep emerging, but most of the attention has centered on anomalous features of SARS-CoV-2’s genome. ‘Thrift Shop’ was a smash-hit single, but Macklemore’s breakthrough album begins with a song about outliers - not ostentatious second-hand outfits. He wanted that message to be the first thing his fans heard.
Stripped down to its essence, my goal is to point out that there is always more than one way to skin a cat, and for some unknown reason, the path with the largest collection of demonstrable anomalies is the one less traveled. Perhaps it’s a function of the nature of the evidence, which is littered throughout a diverse field of sexy genres like geospatial relationships, urban planning, surveys of case trends, hospital usage, cell phone and social media data, traffic flow patterns, access to public parks, etc.
I can’t explain why the WHO et al haven’t figured it out. But, then again, the WHO didn’t even bother to count some dots [see below]; I’ll gladly step in and do anything that might lead to more arrows in my quiver.
I wanted to highlight some of my latest analyses, because my gut tells me that I’m not the only one who would conclude that China’s official COVID-19 data is the statistical equivalent of this music video:
I: Magic Tricks
The surprising response to two of my recent Twitter threads has made it clear to me that people can find epidemiological statistics sexy, if the key takeaways can be broken down into digestible chunks. Only a week after I lamented the relative lack of interest into my House of Cards project, especially when compared to my research into Dr. Fauci’s censorship efforts, this sarcastic take [below; click here—> for my Father’s Day ‘open letter’ original Twitter thread ] on my latest calculations became my most-viewed thread ever - in less than 4 hours [24 hours later, it was 5 times larger than that number]:
Although the performance of this ‘outlier’ tweet can be partially explained by the fact that my followership has quadrupled in the last six weeks, it’s also a reminder that not everyone is as excited as I am to take the long and winding road to every destination - or, in this case, a detailed walkthrough of two dozen epidemiological studies of the Wuhan outbreak (the latest total is actually 74). This is why this ‘sequel’ to my May article is less of a direct continuation of that detailed narrative, and more focused on relating a few key emerging trends I’ve observed in research on this topic
[despite the fact that my list of sources has tripled since then]:
Observers may notice that several items on the list are from members of the D.R.A.S.T.I.C. group I’m a part of [including my own article from May] - but given that much of the current origin research stems from the group’s findings, and that members/advocates are currently engaging in a sort of ‘public peer review’ via testimony in ongoing Congressional investigations, I’m confident that their inclusion is warranted.
I’m also working on a more academic-level review/synthesis, so that anyone interested in deeper analysis can readily perform their own due diligence. For now, I’ll let Anthony Hopkins’ fictional cannibalistic intellectual see us off with wisdom that many actual/non-fictional/respected scientists seem to find offensive these days:
My overarching reason for going back through all of the epidemiology-related research about the COVID-19 pandemic in Wuhan was to see if there were any anomalies that could shed further light on the mystery, but may have been overlooked. Disturbingly, the publication of the World Health Organization’s COVID-19 Origins Investigation report on March 30th failed to spark more rigorous debate on the issue, even though the conclusions were so porous that even the secretary-general of the WHO rejected its assertions. As it stood, I still could count discoveries into this topic on my two hands and have a few fingers still available.
I finally broadened my search and, following @TheSeeker268’s well-worn path, started finding all sorts of epidemiological studies into the Wuhan outbreak - just in Chinese instead of English. @gdemaneuf’s synthesis - published last October - detailed all of the evidence he’d found tucked away in the Internet Archive, but so little information had been shared about the earliest cases that it’s been difficult to even find edge pieces that match, much less a recognizable preview of what the end product would look like.
So, I decided to start with the basics - literally, counting the dots on the graphs given to the WHO. Here's what the WHO report shows for the distribution of early COVID-19 cases in Wuhan [pics 1 & 2]. And here  is what it looks like if you count the dots:
Forgive me for my insolence - likely borne of ignorance - but I find it insulting that the WHO accepted charts of ‘data’ that literally omitted 15-20% of the early pandemic cases - the Chinese National Health Commission didn’t even put 174 dots on any of the charts of the earliest 174 cases [44 &18 cases, respectively, were simply missing from the maps].
I feel obligated to offer some additional perspective. The global community at-large waited a year for this report; I doubt any grieving relatives would find comfort in knowing that their loved ones were worth .0027 seconds of investigative effort [370 deaths per second for the 10,800 seconds that the WHO team stood inside the Wuhan Institute of Virology]. During that 3-hour period, they saw no physical evidence, were given no direct access to data or records, and left with no samples, answers and few justifications.
In contrast, 60 minutes of research per victim were performed during a similar period of time by the 9/11 commission here in the United States. If the same level of due diligence were applied to the COVID-19 pandemic, a hypothetical commission would finish its report in the year 2,483 A.D. - 462 years from now, working nonstop.
That’s what nearly 7 orders of magnitude looks like.
It’s also what happens when humanity’s leadership reaches its lowest level of competence right as we enter one of modern history’s greatest moments of need for such leadership.
Now that I’ve made what I hope is a sound argument in favor of applying more than 2.7 milliseconds per death of investigative effort in the quest for justice for COVID-19 victims, it should be easier to appreciate the scale of the WHO’s failure. The scientific establishment - one that has chastised societies across the globe for daring to question their wisdom - didn’t just fail to look at the data given to them; the WHO team took the pre-compiled statistics and co-authored the final report. In other words, they don’t appear to have even bothered to check the homework before grading it Imagine if the 9/11 commission ‘gathered evidence’ by agreeing in advance to let the Taliban have complete control over the gathering of evidence [and a year to ‘prepare’ it], while preventing the prosecution from obtaining other evidence or arguing their case beyond the limits of what the defense would accept. Consider, if you will, potential questions that were never asked:
Outlier A) Why would China use 'home address' as the plot point to show case distribution? MERS/SARS were largely driven by nosocomial [hospital-acquired] infection. If a respiratory pathogen had recently jumped from one species to another, the most effective incubator would be a hospital full of compromised patients and nurses/staff who continuously move from room to room in the course of treating patients. This pattern would be intensified if the primary mode of transmission was aerosol, because few doctors and hospitals would have the experience and/or facilities to mitigate transmission. None would’ve been prepared for the number of cases that were coming.
Outlier A+) Also, the WHO report itself listed all early clusters, of which there were only 5 (3 were spouse pairs). Given the reported demographics of pre-2020 cases, the soon-to-be proven high rate of nosocomial transmission, the occupation breakdown provided by China, other studies that showed several office clusters, etc., the probability of only 3 spouses and 1 child contracting COVID-19 from confirmed relatives out of 174 total is….negligible.
In the first 3 weeks of January, office and family clusters exploded everywhere; on the 14th, Zhong Nanshan [China’s most famous doctor] confirmed that a single patient in Wuhan had infected 14 medical workers, but officials resisted announcing lockdowns or warning the public about the danger for a full week afterwards.
Outlier A++) One possible excuse could be that personal information like home addresses would fall under 'protected/private information' categories, thus providing a convenient excuse to withhold patient data that could be easily charted and analyzed. However, China didn’t just withhold data in defiance of their obligations to the WHO - they blatantly [and repeatedly] manipulated the bare-minimum case statistics until late February 2020, when they implemented strict censorship controls over all COVID-19 research.
Outlier B) This isn’t a case of incompetence or of an extra ‘burden’ being placed upon a suffering nation, uniquely and unfairly being ‘targeted.’ China’s 104,101 reported cases places it next to Kosovo, Montenegro & Namibia in raw terms; the combined population of those three countries is 5,041,971 - less than half the size of Wuhan’s metropolitan area.
Outlier C) in May of 2020, while the rest of the planet was largely shut down, the Chinese CDC conducted a mass testing operation in Wuhan, ultimately obtaining 9.98 million samples to determine seroprevalence, in only 19 days [see graphic below]. It’s hard to overstate the amount of work that must’ve gone into planning an operation of that scale with such accuracy. It get easier to visualize, however, when one understands how large the CCP domestic surveillance apparatus is, and how active it is in the day-to-day management of the society. All cell phone activations include steps to download the official government app [s] that serves as an open door for the government to monitor all actions from all of its citizens, whenever they choose to do so.
Outlier C+) When the government called for a large-scale study, scientists had the advantage of state coercion to ‘encourage’ residents to comply. Most of the coordination was handled via within the government-controlled apps; residents typically received text messages that directed each of them individually to report at the time/date chosen for them.
Outlier C++) This survey was executed in record time, despite being historic in size and sheer volume of data to track. The takeaway is that this effort took only 3 weeks. Therefore, the notion that Chinese scientists had made no progress in investigating transmission chains from 174 individuals is a giant middle finger to the rest of the world. Officials have open access to GPS tracking of all devices, which means that every movement of those individuals and most of their potential transmission events would’ve allowed authorities to construct effective investigation efforts, even if they truly didn’t know how the virus arrived in Wuhan.
The obvious implication is that even the most basic epidemiological data from the first 174 cases [those prior to 1/1/20] would clearly depict how the outbreak unfolded, which everyone – China, the WHO, Xi Xinping, President Biden, and even my 13 year-old son [we’ve discussed it] – understands. China’s mass surveillance state obviously has a very clear picture of what happened, which is certainly also the reason why it has continued to refuse to provide such basic data.
Outlier D) Why would they report health care worker (HCW) cases as a ratio of district pop., but then refuse to identify which hospitals are which? They had to do extra work to produce this, which looks like an editorial change to me. There's no legitimate reason to withhold hospital data, especially from the WHO, but the bad reason would be to hide that and other new infections to prevent someone from figuring out the true case distribution, if the evidence pointed clearly to the wrong side of the river.
Outlier E) Unless, of course, the CCP didn't want to paint the picture that their own researchers have painted in the 18 months since - a picture that shows a very different early distribution. This doesn't look like an 85%-15% West/East ratio to me:
Outlier F) To recap, we have a wealth of information about early cases - from one side of the Yangtze river. The 85%-15% ratio has to evolve to 60%-39%, but the West exponentially grows afterwards. How to square this circle?
What if we take the 'missing' cases from WHO image and add them to the East's numbers? Well, how lucky is that? The new ratio is... 60%-39%. So, why would they want to minimize the East?
Spoiler Alert - that's not where the Huanan Market is.
II: 10,000 Hours
So where does that leave us?
The world has suffered through 18 months of a nightmare made worse by governments more focused on avoiding all risks to themselves, not their constituents; this quickly devolved into a government race to the draconian rock-bottom while continuing to sacrifice those at risk - for the sake of ‘the children.’
Someday, I’ll return to the realm of domestic affairs and elucidate the failures that actually exposed those most at risk, while justifying the excuse for central governments to exert greater emergency authority. But, for now, I’ll just point out that intentional ignorance is a proximal cause for many of the problems domestically and diplomatically.
If General Mattis & Macklemore were here, they’d point out that recognizing and understanding outliers is only half of the equation; knowledge without experience is…. well…. trusting the scientists, rather than the science. The only reason I’ve been able to catch snippets of value is that I went looking for them, a lot. It’s time we held scientists to a higher standard before we place them on a pedestal. Then DRASTIC won’t have to count dots on a map for them.
We can reasonably assume that the 1/23/20 lock-down in Wuhan curbed the spread of COVID-19, and that the effect was near-simultaneous across the districts. Just as the cosmic microwave background [CMB] radiation bears the mark of rapid inflation in our early universe, allowing us to glimpse its structure very early in its existence, the final pattern in Wuhan should at least be reasonably similar to its early distribution.
Or perhaps I should liken it to a more relatable concept:
What we’ve seen instead, however, is a reverse 4:1 ratio or more as of 1/1 in the WHO report [on the West and East banks of the Yangtze River, respectively; the Weibo data shows that the next 2 weeks were more active in Wuchang, followed by an explosion of cases across the river in Jianghan [i.e. the Huanan Market] and Jiang’an.
Some might be skeptical of using social media data as epidemiological markers, but the study shows the strength of the correlation between that data & confirmed cases. Plus, we have actual evidence, since 100’s of the messages include pics of hospital test results; also, we've been able to link some of them to known cases from the Lancet article that describes the first 41 cases, among others.
Applying the numbers reported by the Chinese members of the WHO joint investigation forward into the latter stages of the outbreak, however, leads to results that continued to defy typical epidemiological demographic trends.
All recent evidence has supported Dr. Quay’s argument that Line 2 of the subway/metro system served as the primary method of initial spread. It doesn’t just link the WIV, market, airport, and many of the hospitals in the central districts of the city – it also runs through the areas with the highest population elderly population density specifically. Given that older residents are less likely to ever have owned a car, they would be highly reliant on public transportation to get around, making enclosed subway cars ideal spreading grounds. We can also reasonably assume that older residents would be more likely to shop at open-air markets, since it is more traditional than a supermarket.
The west side of the Yangtze River as the 2nd epicenter would also explain why time-to-death rates amongst victims shrank as the epidemic progressed [aside from crammed hospitals deterring those who are sick until it becomes serious], because the higher elderly population/pop. density would be more likely to succumb more quickly to the infection. They would also have higher rates of nosocomial transmission.
Wuchang, by comparison, has a larger population but lower density; it also contained a smaller proportion of elderly residents. Despite these demographics, the confirmed case fatality rate [CFR] there was higher than the other side of the river.
The parsimonious explanation for all of these abnormalities would be that most of the ‘missing’ cases not depicted on the WHO report’s maps [20-60, depending on the image] of the pre-2020 patients were from Wuchang, which were removed in order to further highlight the Huanan Market at the expense of the part of Wuhan that the CCP wanted to obscure [the side with the Wuhan Institute of Virology and its BSL-4 laboratory]. The discrepancies in the Wuchang CFR and severity of cases can partially be explained by placing the district in its proper temporal context – the global pattern of decreasing mortality rates was for the initial wave/local epicenters to be hit hardest, with the situation gradually easing once doctors gained familiarity with the disease. Is it enough to account for disparity between Wuchang and the market districts? Probably, given the other age/density factors involved.
It’s important to note that I could keep listing more breadcrumbs [the blessing of momentum is that it becomes self-sustaining], but it’s time for the broader scientific community to putting in the work, too. The eldest members of DRASTIC have been swimming against the tide for nearly 18 months, and only recently have scientists on the shore done much more than peer through binoculars to see if the heretics had finally succumbed through sheer exhaustion.
I waded into the water because I knew it would be tragic to watch DRASTIC sacrifice so much, only to be too weak to catch the waves when they finally came.
We shouldn’t need General Mattis or Macklemore to tell us when it’s time to jump into the water; only the stunning lack of scientific leadership across the globe [Fauci/Tedros/Farrar] could account for the punch to the gut I felt when I realized why I’d noticed something that couldn’t possibly be undiscovered until then - me and countless others had never considered that the WHO would publish something as simple as 3 maps that were blatantly incomplete.
It turns out there’s no Eureka! moment when you discover something that you shouldn’t be discovering 6 weeks after the WHO report’s publication. But, for anyone involved in the academic sciences [or their bureaucratic institutions] who may find criticism from a non-scientist to be obnoxious, forgive me;
my frustration will continue until we get more help cleaning up your mess. Here’s some more dots.
10,000 Hours - Macklemore & Ryan Lewis, 2013