Many criticized the article below co-authored by Jay Bhattacharya, who also co-wrote the Great Barrington Declaration.

But I haven't seen a detailed explanation of why the article was wrong + dangerous. So I'll give one here


Imagine the spread of SARS-CoV-2 as an accelerating car.

Some brakes help slow the car, such as masks, social distancing, contract tracing, etc.

But even without brakes, the car will eventually start slowing down on its own; that's herd immunity.


Once you have enough people immune to infection at herd immunity (whether immune by prior infection or vaccination), you can stop using the brakes and the car still won't accelerate.

Bad idea to release the brakes too early without herd immunity.


Bhattacharya doesn't like various brakes for ideological reasons (there's a reason he goes to right-wing outlets a lot).

So he exaggerated how close India was to herd immunity.

"a near majority of the population has developed immunity to the virus"

That's consistent with Bhattacharya exaggerating the number of infections for over a year.
It's convenient for him in a number of ways, such as allowing him to give COVID-19 fatality rates so low they're impossible.


Bhattacharya accepts models when they're convenient for his ideology, + ditches them otherwise. He mixed that with his usual bad extrapolations from non-representative samples.

Anyway, India was nowhere near ~50% of their population being infected.


So the car is facing the edge of the cliff.

Bhattacharya (falsely) tells the driver they are very close to not needing the brakes anymore to slow down the car.

What could be worse than that?

Well... Bhattacharya telling them brakes don't work. 🤦‍♂️


I'm not going to rehash the reasons why limiting people being near each other limits transmission of a virus that spreads by people being near each other.

Not like Bhattacharya + his fans will learn at this point anyway.


One of Bhattacharya's proposed solutions is to reserve vaccinations for people who were not infected before.

That would be an interesting point,... except that he can't help but add to that his distortions of immunology and vaccines.


There's good reason to think that vaccines will work better than "natural" infection with SARS-CoV-2.

There's also a chance they may improve the immune response of those previously infected.

There are other issues as well.


For herd immunity, we want antibodies + B cells / plasma cells that prevent re-infection; i.e. we want neutralizing antibodies that cause sterilizing immunity.

(If any non-expert says, "but T cells!!" to you, ignore them



- not all antibodies are neutralizing
- neutralizing antibodies can wane, allowing for re-infection
- SARS-CoV-2 mutations could evade antibodies, or result in a more contagious form needing more people immune for herd immunity



Vaccines can help with this by increasing levels of neutralizing antibodies or addressing variants (see part 11/Y).

Conversely, allowing many infections facilitates the evolution of harmful mutants.



So who in their right mind would advocate for taking off brakes to allow for many infections?

Jay Bhattacharya, and his Great Barrington Declaration group.

At this point, he's just sabotaging the brakes for India and pushing the car off the cliff.


But if you're going to push the car off the cliff, you may want to downplay how dangerous that is.

So Bhattacharya reaches into his usual bag of tricks for doing that. For example, citing Ioannidis' work on fatality rates:


But Ioannidis' poor work leads to fatality rates so impossibly low that they require more people are infected than actually exist (Bhattacharya did the same in part 6/Y).

One of the places that happens is... India.


And of course, Bhattacharya knowingly abuses the misleading "infection survival rate" framing.

Easy to cover up how bad 600,000 deaths are by saying '99.4% survival rate', without mentioning 100 million infected.


As the car progresses on its descent towards the cliff, Bhattacharya downplays the risk to younger people onboard.

Ioannidis' debunked + impossible work makes room for that, as does willfully ignoring better studies


Bhattacharya also downplays the risk of sabotaging brakes, by side-stepping how India + other nations under-estimate their number of COVID-19 deaths.




The "brakes / cliff" analogy might offend some.

I no longer care. 🙂

I value the feelings of deniers like Bhattacharya less than the lives they put at risk.

Red circle is when Bhattacharya's India article was published:


On vaccinating infected people (see part 10/Y):

- vaccines help infected folks
- other priorities, like vaccinating the elderly + those in regular contact with the infected (ex: healthcare staff)
- risk of doses expiring waiting to find non-infected

And Bhattacharya's January 2021 article was in line with horrible advice he's given to India *for months.*

For example, before there was a vaccine he advocated for herd immunity via people getting infected.

July 30, 2020:

Yet Bhattacharya + his Great Barrington Declaration team refuse to admit to they were wrong.

That's in contrast to those with integrity like Monica Gandhi, who apologized for what she said on India + herd immunity.



The results in 7/Y showing ~24% infected, were out by February 4.
I re-tweeted them on February 10.

So if Bhattacharya bothered to pay attention, he had weeks to correct his dangerous mistake + warn people.

Yet he didn't.


So hopefully this thread explained some reasons why so many experts were rightly upset with Jay Bhattacharya.

He's been ludicrously irresponsible during this pandemic.

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More from @AtomsksSanakan

26 Apr

There was a recent effort to champion Nate Silver (@NateSilver538) as a non-expert who speaks uncomfortable truths experts don't want to hear.

That's misguided, as we can see by examining how many SARS-CoV-2-infected get hospitalized.


Some context:

Infection fatality rate, or IFR, is the proportion of people infected with the virus SARS-CoV-2 who die of the disease COVID-19.

Infection hospitalization rate, or IHR, is like IFR, but with COVID-19 hospitalizations instead of deaths


Seroprevalence studies measure antibody levels to estimate the number of infected people.

Dividing COVID-19 hospitalizations by that number of infected people gives a seroprevalence-based IHR.

IHR is good to know.

Read 19 tweets
11 Apr

As we get closer to the end of the pandemic, it's worthwhile to look back on false claims that helped make the pandemic worse.

One of these claims was:
COVID-19 is not much of a danger to people outside of nursing homes + other institutions.


John Ioannidis is a proponent of this claim.

He argued that relatively few SARS-CoV-2-infected people died of the disease COVID-19, outside of nursing homes.

In other words: the infection fatality rate, or IFR, was low outside of nursing homes.


He defended this idea since at least early May 2020. And he continued to defend it in his most recent work:

"in Europe and the Americas (~0.2% among community-dwelling non-institutionalized people)"

Read 10 tweets
3 Apr

You may recently have heard that COVID-19 has a fatality rate of ~0.15%, making it akin to a bad flu.

In reality, a more accurate fatality rate would be closer to ~0.6%, as per the WHO.
That's ≥10X worse than seasonal flu, and ~100X worse than the 2009 swine flu pandemic.


Infection fatality rate (IFR) is the proportion of people infected with the virus SARS-CoV-2 who die of the disease COVID-19.

IFR for seasonal flu is <0.1%, as per the WHO, among others:


So, following @BallouxFrancois, who in their right mind would claim COVID-19 has an IFR comparable with that of seasonal flu?

Well, I can think of at least 2 people.
You likely know who one of them is. 😉

from 2:27 :
Read 30 tweets
27 Mar

John Ioannidis published an article defending his low estimate of COVID-19's fatality rate.

It contains so many distortions that I'll try something I've never done on Twitter for a paper:

Go thru distortions page-by-page.

This will take awhile. 😑

onlinelibrary.wiley.com/doi/10.1111/ec… Image

Some context:

Infection fatality rate, or IFR, is the proportion of people infected with the virus SARS-CoV-2 who die of the disease COVID-19.

There are many IFR estimates, including some from Ioannidis.

institutefordiseasemodeling.github.io/nCoV-public/an… Image

Seroprevalence studies (serosurveys) measure antibody levels to estimate the number of infected people.

Dividing COVID-19 deaths by that number of infected people gives a seroprevalence-based IFR.

who.int/bulletin/volum… Image
Read 48 tweets
26 Mar

One my pet peeves is tone trolling, which is:
emphasizing the *tone* of a discussion on X, to avoid addressing the *substance/evidence/facts* on X.

This thread will cover an instance of tone trolling from @VPrasadMDMPH.


In the above tweet, Prasad uses tone trolling to defend John Ioannidis. Since at least March 2020, + continuing to now, Ioannidis made obviously incorrect claims that downplayed the risk of COVID-19.


Example: Ioannidis so under-estimated the proportion of people infected people who die of COVID-19 (i.e. the infection fatality rate, or "IFR"), that he needs more people to be infected than actually exist.

That's impossible

Read 11 tweets
6 Mar

I made some threads on how those behind the Great Barrington Declaration (GBD; @gbdeclaration) spread disinformation on COVID-19.

On this thread I'll go over some reasons why the GBD itself is nonsense

GBD's main point is "focused protection"; i.e. strategies that limit infection risk among older people + others at greater risk of dying from COVID-19, while allowing less vulnerable people to live with less restrictions.


An obvious problem with that is infection can spread from people less at risk of dying from COVID-19, to people at greater risk of dying from COVID-19.

So allowing the non-vulnerable to get infected places the vulnerable at risk.

Read 19 tweets

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