I took a deeper dive into the Chongquin jogger study. It suggests Omicron is incredibly contagious. Is it flawed? are the data real? Follow the 🧵
For the full story, look here: covid.dropcite.com/articles/3d411…
The first 8 weeks of the summer of 2022, the city of Chongqing, saw only 11 cases of COVID per week in a city of 30 million. Then, they recorded 54 cases in a week. As cases continued to rise, experts from around the country scrambled to understand what had happened.
Early in the pandemic, investigators attention focused on Longtou Park, a 42.5 acre park in the city center. Two cases were in park staff on August 18. When they began to question other cases, they discovered that 34 of them had all been in the park on the morning of August 16
A man who was waiting for the results of a COVID screening test, had gone for a 35 minute run around the lake at the center of the park, unmasked. An hour later he got the positive test result. Patient Zero (PZ)
Again, much of the story here. covid.dropcite.com/articles/3d411…
Reviewing surveillance video, investigators identified 104 people who were unmasked and came within one meter of PZ. Of the 104, 13 had been diagnosed with COVID. Another 19 had visited or been at the lake during the run.
Calculating incidence rates requires denominators, but we only know 3: 1. Those with close, maskless exposure to PZ during his run,
2.Those who were in the park but did not have close, maskless exposure, and 3. Those who were not in the park that day.
Investigators sequenced the genomes from all the COVID cases among park visitors and staff from whom they could obtain an adequate sample. Of the 34 testable samples, 29 were an exact match to PZ. The other 5 had a single mutation.
The study suggests that PZ was infected on a plane by three people who were on the plane THE DAY BEFORE. This seems unlikely. (More on DropCite)
38 of the 39 people who became infected in the park were not wearing masks. What was the base rate of mask wearing among people in the park?
“Masks are still ubiquitous in Covid-Zero China, even outdoors in the major cities” Bloomberg 8-2-22
.bloomberg.com/news/features/…
I have been criticized for calling the evidence “overwhelming”, but if these data are accurate, this as close to metaphysical certainty as I have seen. Are the data real? Where are the sequence data? I sent questions to the authors and posted them: covid.dropcite.com/articles/3d411…
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Trump bought RFK Jr’s support by offering him control over health-related federal agencies. With every utterance, Kennedy demonstrates how completely unqualified he is to play that role. Let’s look at one example.
🧵1/12
His stands range from anti-vax to pro-lard. Among his public proclamations, this one proposing to gut the FDA stands out. It is hard to imagine anyone compressing more ignorance into a single post. Let’s unpack. 2/12
Since starting in 1906, the FDA has been about the elimination of unsafe additives from foods and rigorous scientific scrutiny of the safety and efficacy of new drugs. Consider the items on his list in this context. 3/12
The End of Expertise: Science, X, and the Problem of Plausible Credibility
Jay Bhattacharya claims to be an epidemiologist
He’s not
He’s a health economist
But the banner under him on Fox says Stanford Medicine
That gives him plausible credibility.
This is a problem.
Link in 🧵
On X, subject matter expertise can even be a liability. I taught epidemiology for twenty years but can find myself lectured to and mocked by a truck driver. 2/5
A recent Stanford symposium had an agenda packed with speakers from fields close to epidemiology or virology, let's call it proximate expertise, with academic appointments that gave them plausible credibility. 3/5healthpolicy.fsi.stanford.edu/events/pandemi…
This graph is intended to show that vaccines did nothing to reduce disease.
It's a Master Class in Misrepresentation of Data.
Let's read it line by line.
The most dramatic line (red) is for scarlet fever caused by streptococcus bacteria.
THERE IS NO VACCINE FOR SCARLET FEVER.
🧵
The smallpox vaccine was introduced in 1796 when it caused almost one in five deaths in London. The grey line in the graph cuts off the most dramatic improvement, starting 42 years AFTER the introduction of the vaccine. Here's the full picture.
This graph shows mortality, not disease incidence. There were dramatic improvements in management of diseases such as measles (green line) that reduced mortality, but not incidence. The drop in measles incidence after the vaccine was introduced could not be more clear.
4 yrs ago a @stanford team asserted the pre-vaccine COVID IFR=0.17%
The results are in.
With that IFR, 1.2 million US COVID deaths would imply 690 M infections.
BUT, the vaccine reduces IFR by 60-90%, which implies 1.3 to 2.5 BILLION infections.
HOW DID THEY GET IT SO WRONG?
This FB ad was used to recruit.
What would motivate you to leave home during a shelter in place order, drive across town, and wait in line to give a blood sample?
It seems likely someone who had been sick would be far more motivated.
That’s a problem.
11,000 people clicked the ad.
They don’t report views, but the average click rate for FB ads is 0.9%.
Even a 5% click rate would imply 220,000 views.
3,300 participated.
Overall participation rate < 1.5%.
This group is highly self-selected.
4 years ago, a Stanford team lead by @drjBhattacharya began a study to determine seroprevalence of COVID antibodies.
It played a key role in driving the wildly incorrect belief that COVID is no worse than the flu.
How did it go so wrong?
There are two key challenges in seroepidemiology. 1. Ensuring you have a valid test. 2. Ensuring that you have a representative sample. (Does the seroprevalence in your sample reflect the population infection rate?)
The study had problems with both issues.
Reports indicate that two faculty members, a virologist and a pathologist, were asked to validate the test. Both warned that the test had problems and refused to participate in the study. But selection bias was a much bigger problem.
In the 4 years since their imposition, non-pharmaceutical interventions (NPIs), from masks to school closures, have been vehemently declared to be ineffective. This study estimates that they combined to prevent over 800,000 COVID deaths. medrxiv.org/content/10.110…
The study estimates COVID death rates for US states as a function of just 4 variables: vaccination rates, population density, NPI effectiveness (more on this indicator below), and COVID deaths prior to NPIs. This model predicts 70% of variation in COVID deaths rates
The indicator of NPI effectiveness is the reduction in state influenza mortality in 2020-22 as compared to average influenza mortality from 2016-19. Influenza mortality reduction (IMR) reflects reduction in transmission of respiratory diseases due to the aggregate impact of NPIs