JetBlue Funded a Stanford Study That Said the Coronavirus Wasn’t That Deadly | Hacker News Reader

JetBlue Funded a Stanford Study That Said the Coronavirus Wasn’t That Deadly

buzzfeednews.com

78 pointselorant41 comments

Did I miss something in this article? It says that David Neeleman of Jetblue donated $5,000. I don't know how big Ioannidis' research budget is but this is peanuts.

You would think there would be better things for reporters to look into, such as whether Ioannidis might actually have a point.

"Ioannidis added that he did not know how much the study cost, but the funding came from an anonymized pool of financial gifts given to Stanford’s Office of Development: “This form of funding is the most unconflicted type of funding process to do research. It secures perfect intellectual and scientific independence of the study.”

Anonymizes pool of financial gifts but he was in contact with the guy? Is that a joke?

And then suggesting funding a lab if the kit is validated:

> “David, I think you should write Taia a note and tell her you’ll support her lab if she validates this kit.” Bendavid confirmed that he put Neeleman and Wang in touch.

> Anonymizes pool of financial gifts but he was in contact with the guy? Is that a joke?

More than a joke, it sounds like Ioannidis was out right lying. From the article:

> But according to Neeleman, the authors did know he’d given money to fund the study. Neeleman confirmed that he made a $5,000 donation to Stanford to be given to these researchers and that he was in communication with them while they were conducting their research.

There is a way to head off all the quibbling about false positive rates and methodology, etc.

Look at the all-cause mortality figures for April vs the historical average for that month. If it isn't COVID-19 killing all of those people at a multiple of the normal death rate, we have an even bigger mystery of what is causing all the extra deaths.

In fact, these numbers were apparent for NYC a month ago: https://www.nytimes.com/interactive/2020/04/10/upshot/corona...

It’s not too much of a mystery. There’s a very good likelihood that the fact that people aren’t getting regular healthcare for other health problem has lead to higher deaths rates. Hospital utilization is significantly below normal currently which means those common heart attacks, strokes, cancer screenings, among others are still occurring but not treated and resulting in higher fatalities.

But then why would it be correspondingly higher in New York along with their infection rate? Other places were just as locked down without a spike of similar magnitude. That makes no sense.

Good point, and that could indicate those unaccounted deaths are skewed towards being covid-19 related. There’s still odd patterns with the covid-19 hotspots, and I’d suspect that the underlying reasons could correlate with higher non-covid-19 death rates. As in older and/or sicker populations.

Didn't it follow the infection lag for the most part? Why would heart attacks lag lockdown infection growth stoppage by the same amount as virus deaths?

> Hospital utilization is significantly below normal currently

this is true but misleading.

hospitial utilization is down because elective procedures are cancelled, not because its illegal to show up with a heart attack or something absurd like that.

ERs are still open like normal, albeit in a quarantine-sensitive model.

That’s technically accurate, but doesn’t account for how many more serious diagnosis begin from routine care or how afraid people are of covid-19 they’ll still not go in even when they normally would. A local hospital put out a PSA that yes they were in fact open, safe, and that people should come in for medical issues. They indicated worries that people were suffering more serious issues due to fear of the pandemic.

That only works if the quibbling is done in good faith and is based on facts.

You cannot be serious. You fell into the trap the NY Times statistics fooled you in. And I don't think the NYT made a simple mistake, looks more like a deliberate lie.

You cannot compare flu peaks in different months. The normal flu peaks much earlier, this years flu peaked April. Also normal flu waves come every second year, not every single year. With proper graphs you would see that.

You can either shift the graphs to their respective peaks, or compare the absolute numbers. So why is the excess mortality in sum the same as with the last flu season? Around 80.000 for the US? Same for Italy, France. Much lower for all the other countries. Outliers are only B, NL, UK, Spain. Not much extra deaths. In Italy there were even much less extra deaths for the people <65. Also compare the IFR's. Not much to worry here.

And this study is the flawed one. You really need to look at the dozen other good antibody and IFR studies, with the exact same results. From all over the world.

i don't know if you missed it but there was a 2 month worldwide lockdown just recently. when is the last time there was a lockdown for flu? even if the curves are shifted versions of each other up until now do you really think that lockdown had absolutely zero effect on the covid curve? so how do you explain the many people that haven't contributed to the covid curve (flattening the curve duh) yet (unfortunately).

The effect of the lockdown on the IFR is zero. That's a rate. Only certain categories affect the IFR, like density, age distribution, obesity, air pollution, health system.

The lockdown could have an impact on the number of absolute deaths, but the question is still out of its positive or negative. Flattening the curve kept the virus longer alive than necessary, it was only necessary for countries with not enough ICU beds. Not many countries were in that desperate situation. Then we have 7 countries who didn't do these lockdowns, which do have the very same curves. Then we do have the countries where the curves already went down before the lockdown. Seeing the lockdown measures in the curves is pretty hard. You mostly see it with manipulated curves, as the NY times curves. Then you also get curves not from actual data, but calculated data from estimation models. And we know you bad those models were. Like the famous Imperial College model, which was totally off. Or the German RKI model which always points up for the very last week, but with the real data coming in next week it was still going down. Wrong model. And it's getting worse, not better. They don't give away the data, but you can see the wrong models at their web page. Since April 24 also on the previously serious euromomo.eu stats. Now it also went to the false model data.

You do those drastic measures to keep the ICU's working but not much more, as too much lockdown will only lead to more deaths, not less. Keeping people in their homes is counterproductive, as this is their natural habitat. There it will be kept alive.

Of course lockdowns do help with the spread, to keep the R rate down not becoming too exponential. Esp with an initial R0 of 5. But this only helps in the very first week, afterwards you cannot run after each single case anymore.

When was the last lockdown for flu? Spanish flu famously. Massive immunosystem overreaction, like with the critical MERS virus, with an IFR of 50%. Was there a MERS lockdown, even if it was the most critical flu event recently? No, it was for an medium harmless SARS-2 variant, with an high R0 and a low IFR (of about 0.3, with 0.02% for <65 year olds). Remember, this rate is lower than using your car driving to work. The economy crash is due to a huge media panic to save a couple of fat old men, who would have died with the next flu anyway, to say it overly bluntly.

The comparison to computer viri might be interesting. Why is there always a panic on more harmless viri with an extremely high distribution rate, and not with the extremely harmful ones with a much lower distribution rate? that's the strategy of terror. for an effective computer virus you do make it spreading, harmless, unpredictable to keep the media circus rolling, and you need play the media. Destruction rate of 0.3% sounds perfect.

> Flattening the curve kept the virus longer alive than necessary, it was only necessary for countries with not enough ICU beds. Not many countries were in that desperate situation.

Are you joking? Spain is hardly a third-world country and it not only was forced to comandeer hotels to serve as ICUs but also Spain's armed forces were forced to setup an emergency hospital with a capacity for 5k beds in Madrid's convention center just to be able to handle all the covid19 cases.

On top of that, Spain also had to comandeer an ice ring in Madrid to be able to have a spot to store all the dead bodies.

What exactly leads you to believe that "it was only necessary for countries with not enough ICU"?

Spain falls into the "not enough" section obviously. But there was no 10x excess mortality as predicted. Did you see all the dead bodies from the last flu season on TV? all the ventilator patients then?

I believe in nothing, I'm just looking at the numbers. virology experts do the expertise. politicians do the ignoring. media is doing the twisting.

Lockdowns were not necessary in most of the countries as you will see, and most experts already agree on. Selective lockdowns in some herds, yes. protecting the nursing homes, yes. but closing the schools, certainly not, in hindsight. a.s.o. It's not like the flu where the kids are spreading the virus around.

I’m most concerned about the new reporting from Boyd, that according to gold standard ELISA retest of positive samples, just under half came back negative, indicating a possible false positive.

Of course, the samples going through freeze thaw cycles may contribute to antibody degradation but seriously concerning.

This seems like a really big deal, so I'm going to second the request for a source on it.

Nevermind, it's mentioned in the original article which I missed the first time around:

> The following week, Boyd had results in hand. Using ELISA tests to reassess the samples of community members who had come up positive for antibodies on the Premier test, he had ended up getting positive results for a little over half of them.

You got a link to that? Thanks

Why would Ioannidis risk reputation for such a small amount? I do not bite it. He did a basic research which is what he is paid to do. If his analysis has flaws

a, The paper was peer reviewed b. We can see other peer reviewed papers disprove his findings.

The flame wars feed conspiracy theories in both sides.

I am not an expert, I am not even a researcher any more, however my possibly flawed opinion is that deadliness of the virus is not a number written in stone and may have seasonal and regional variation.

That's the only documented amount. We don't know of all the other funders or LLC funders that are associated with him.

Even aside from the funding, the article unveils many more disturbing things.

This is turning a flawed study into a conspiracy theory and ignoring important related epidemiology. The fact is that the large number of asymptomatic cases led to initial undercounting of infections so the virus appears to be less deadly when looking at numbers that include previously missed cases. This doesn't make the pandemic any less serious.

At this point we have data from places with no lockdowns, mild lockdowns, strong lockdowns, and recently ended lockdowns. All of this verifies the long held epidemiology that points out that once a highly infectious agent is loose in the general public lockdowns have extremely limited benefit. This is not a political issue and does not relate to managing airlines or running clickbait web news sites.

"once a highly infectious agent is loose in the general public lockdowns have extremely limited benefit."

Huh? What data supports this? It looks like the top dozen or so countries in terms of new cases are almost completely different from a month or two ago. Isn't it obvious this is because the US and western Europe that had the lockdowns slowed it relative to others?

Here is a list in descending order of new cases from a day or two ago, see how the US and UK are the only remaining of the first wave. Spain and Italy aren't even there! I don't see how your comment could be more at odds with the information I'm seeing.

USA

Brazil

Russia

Peru

India

Chile

Pakistan

Mexico

Iran

Saudi Arabia

Turkey

There are a number of analysts comparing the effectiveness of attempts to mitigate this pandemic and finding lockdowns to be high cost and risk for low impact as compared to distancing, handwashing, masks, testing, and tracking. One of the most prominent is Isaac Ben-Israel https://www.timesofisrael.com/top-israeli-prof-claims-simple... out-after-70-days/

I'm sorry you don't like the actual math, but the reality is that the initial wave of this pandemic has been burning out in all the effected areas in very similar ways regardless of lockdowns or not. Personally I find detailed studies by experienced professionals like Isaac Ben-Israel more compelling than what you present here.

"I'm sorry you don't like the actual math"

How is that a response to widely reported data? What are you disagreeing with? Are you saying my data is incorrect or that I'm interpreting it wrong?

My list is based on numbers from https://www.worldometers.info/coronavirus/ - is there an issue with their sources?

That's mostly a list of most populous countries in the world. Is that data per capita?

https://ourworldindata.org/grapher/total-daily-covid-cases-p...

there you go (may 9)

  df=pd.read_csv("~/Downloads/total-daily-covid-cases-per-million.csv")
  df.sort_values(['Date', 'Daily new confirmed cases per million (cases)'])[-20:][['Entity', 'Daily new confirmed cases per million (cases)']].to_string(index=False)
  Out[14]: 
  Entity  Daily new confirmed cases per million (cases)
  Saudi Arabia                                    48.860
  Armenia                                         48.933
  Belgium                                         50.994
  Gabon                                           52.118
  Portugal                                        54.233
  United Arab Emirates                            55.913
  Gibraltar                                       59.363
  Sweden                                          63.569
  United Kingdom                                  68.482
  Chile                                           72.765
  Russia                                          73.314
  United States                                   81.440
  North America                                   84.775
  Sao Tome and Principe                           95.820
  Belarus                                         98.737
  Peru                                           100.722
  Singapore                                      131.274
  Bahrain                                        143.984
  Kuwait                                         150.097
  Qatar                                          455.041

That is interesting, but per capita data is not the answer to what I was interested in originally - what countries is the epidemic growing fastest in is different from in what countries is most of the epidemic's growth happening in.

To expand on my other post, since I can't edit it any longer, the fact that it's many of the largest countries with the most cases is a point of note - the first wave wasn't the largest countries so if that's where we're headed, things are changing.

No, it's based on the actual numbers of new cases.

Per capita data tends to have very small countries at the top because they have a larger urban proportion and a larger amount of interaction with foreigners.

not disclosing a financial conflict of interest is a mayor breach in how to conduct science, and there is exactly zero clickbait let alone conspiracy theories here.

The study was thoroughly flawed at the most basic levels of sampling and analysis. The article supposes that the authors of this flawed study did what they did because of who they knew and what the political implications of their published results might be. That is a conspiracy theory and it is presented for emotional impact which makes it clickbait.

Meanwhile increased testing is showing increased numbers in the population who are infected with only minor if any symptoms. This has major implications for public health and correct policy, but what is being asserted is that anything that causes a change to the overall mortality rate is likely about politics.

their study is flawed, the scientists involved went almost exclusively onto right-wing programs to hype their biased results, and they didn't disclose important financial ties - particularly the jetblue guy that wrote an op-ed for the ben shapiro website (damning imo). you can whine about this article being clickbait but it's incredibly well-written and sourced.

>The whistleblower complaint made note of the authors’ media appearances, observing that they “coordinated the online publication of a non-peer-reviewed pre-print with multiple press appearances” and “continued to promote the results of this and subsequent studies despite severe criticisms of their pre-print from multiple statisticians.”

Indeed, scientists immediately attacked the high-profile study’s recruitment of participants and data analysis, with one calling it a textbook example of “how NOT to do statistics” and another saying he was “alarmed at their sloppy behavior.” One major cause for alarm was that the authors were overly confident in their test’s false-positive rate.

They explained that by saying they went to those sources because left wing news outlets and left win financial source black balled them. But that's not even the issue. How do you explain the fact that their numbers fall right in line with the study from Bonn Germany, or New york, or all the other random sampling studies. Are they all lying?

New York has far more deaths per capita, it makes no sense for their % positive numbers to fall in line with New York. It is similarly inconsistent with recent numbers out of Spain/Madrid, which was hit quite hard and is still floating around only 10% positive.

There have been, conservatively, at least 20,000 COVID-19 deaths in NYC alone. NYC has a population of 8,300,000. 20,000 / 8,300,000 puts a hard lower bound on IFR of ~0.24%. That is, if we assume that the entire population of NYC has already had SARS-CoV-2, then the lowest mathematically possible IFR for NYC would be ~0.24%.

Ioannidis' paper says 2.49%-4.16% of Santa Clara County has already been infected. Santa Clara County has a population of 1.9 million. That means we should expect (1900000 * 0.0249) * 0.0024 == 113 deaths at the lower bound, and (1900000 * 0.0416) * 0.0024 == 189 deaths at the upper bound. Santa Clara County has already recorded 134 deaths, so that number seem to fit. But it fits with the hard, categorical data from NYC only if we assume 100% of NYC to have already been infected.

TL;DR: Unless you're prepared to believe that

1) most of NYC residents have already been infected, or

2) that IFR is significantly greater than 0.24% and the actual number of COVID-19 deaths in Santa Clara County is multiples of what's reported

then it's clear that Ioannidis' paper is wrong. #2 seems far more plausible and may even be true, but the controversy is the implication of his paper on derived IFR. And if #2 is what's happening, the IFR is definitely greater than what people applying the paper (including, AFAIU, Ioannidis' himself) predict.

I will not defend his study. Pretty sure there are enough holes in it.

I am not sure why we think the IFR would be constant by region, though. Just doing an age banded calculation is enough to show that it is variable for populations. Such that it seems safe to assume other things could cause variation, as well.

An early thought I had was age being a proxy to lung damage from long term exposure to air pollution. This still holds for the population in NYC, as many of them are from there. Same for Italy. Many elderly are retired locals. Notable that both places have some of the worst air in the world. Contrast with California, which has been very progressive with emissions. I don't have data on age ranges in California. And I confess I have stopped following the news on this. (Peek in the occasional story that makes it here. Starting to pay more attention again.)

The NYC data is the outlier. There may be contributing factors which are causing that 20,000 number to be inflated, not the least of which are the financial incentives for emergency funding for COVID diagnoses.

Which numbers? The inferred IFR?

Neeleman is no longer the CEO of JetBlue.

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