Professional Responsibility

Professional Responsibility

Sweden’s approach is stupid to say the least (provided it did not take simple precautions, stated below) even disregarding the high death toll in Sweden.

One epidemiologist acting without data is shear madness and subtle medical dictatorship.

I need few basic data.

Is it an enteric virus?

Is it a respiratory virus?

Is the mode of transmission a combination of the two?

What is the gap period (I.C.P.) between the two?

 

In other words the total incubation period assuming the enteric phase was missed or callously disregard.

 

We haven’t got the data due to lying and conniving of China and WHO.

There are a lot to blame including guys and girls in Ceylon.

Simple sanitation methods (adapted in West Africa during Ebola virus endemic) like hand washing, wearing gloves, masks and gowns are neglected world wide especially in Indian subcontinent and Africa. 
 

Social distancing even though difficult to practice  in the East (assuming respiratory mode of transmission) was outstandingly successful in the West (associated with the level of education and social responsibility).

   

One can be smart after the fact and the moment I got the wind of it without telling anybody, I /we kept at home for two months (wife joined) long before WHO and Ceylon recommendations.

 

By the time warning bells were rung, the  horse had bolted from the stable.

 

Reproduction

  Friedman: 

Is Sweden getting it right with its…

President Donald Trump has often described this pandemic as our “war” with an “invisible enemy” — the coronavirus. 

That war metaphor is wrong and misleading.

Wars are fought and won by humans. 

But when you’re in a struggle with one of Mother Nature’s challenges — like a virus or a climate change — the goal is not to defeat her. 

No one can. 

She’s just chemistry, biology and physics. 

The goal is to adapt.

 

Mother Nature does not reward the strongest or the smartest. 

 

She rewards the species that are the most adaptive in evolving the chemistry, biology and physics that she has endowed them with to thrive — no matter what she throws at them.

And that’s why I believe one of the most important questions we need to answer, as these lockdowns end, is this: 

Are we going to adapt to the coronavirus —by design— the way Sweden is attempting to do — or are we going to go the same direction as Sweden —by messy default— or are we just going to say “the hell with lockdowns” and go 50 different ways?

In case you’ve missed it, Sweden has taken a radically different approach in dealing with the coronavirus. It has essentially opted for a strategy of “herd immunity” through exposure.

This strategy posits that most people under age 65 who get the coronavirus — if they do not have major preexisting medical conditions — will either experience it as a typical or tough flu, or completely asymptomatically, and the number who will get so sick that they require hospitalization or emergency care will reliably be less than the number of beds needed to care for them.

So, if you do your best to shelter and sequester all of those over 65 and those with serious preexisting conditions — notably heart and lung disease and diabetes — and let much of the rest of the population circulate and get exposed and become naturally immune, once about 60% of your population has gone through this you’ll have herd immunity and the viral transmission will be blocked. (This assumes that immunity for some period of time results from exposure, as most experts think it will.)

After all, herd immunity is our goal — either from vaccination or from enough people building natural immunity. Those are the only ways to achieve it.

The upside of Sweden’s strategy — if it works — is that your economy does not take such a deep hit from lockdowns.

 

Anders Tegnell, chief epidemiologist at Sweden’s Public Health Agency — the nation’s top infectious disease official and architect of Sweden’s coronavirus response — said in an interview published in USA Today on Tuesday: “We think that up to 25% people in Stockholm have been exposed to coronavirus and are possibly immune. A recent survey from one of our hospitals in Stockholm found that 27% of staff there are immune. We could reach herd immunity in Stockholm within a matter of weeks.”

It has come with a high cost, though. As USA Today noted: “As of April 28, (Sweden’s) COVID-19 death toll reached 2,274, about five times higher than in Denmark and 11 times higher than in Norway.”

Here’s the stone-cold truth: There are only different hellish ways to adapt to a pandemic and save both lives and livelihoods. I raise Sweden not because I think it has found the magic balance — it is way too soon to tell — but because I think we should be debating all the different ways and costs of acquiring immunity.

When I look across America, though, and see governors partly lifting lockdowns — because they feel their people just can’t take it anymore for economic or psychological reasons, even though their populations have little or no immunity — I worry we may end up developing more herd immunity but in a painful, deadly, costly, uncoordinated way that still leaves room for the coronavirus to strike hard again and overwhelm hospitals.

Herd immunity “has historically been nature’s way of ending pandemics,” added Dr. David Katz, the public health physician who helped kick off the debate in an essay he wrote in The New York Times on March 20 and in a follow-up interview we did together.

“We need to bend with her forces — even when we as a species are responsible for unleashing them,” Katz said. That means a designed strategy, based on risk profiles, of phasing back to work those least vulnerable, so we gradually cultivate the protection of herd immunity — “while concentrating our health services and social services on protecting those most vulnerable” until we can sound the all-clear.

Thomas L. Friedman is a New York Times columnist.

This is an extremely important (and also entertaining) book that should be mandatory reading not just for anybody interested in finding out about what data-driven medical studies really mean, but also for anybody engaged in any kind of empirical work. What Kendrick shows brilliantly is the extent to which the vast majority of medical recommendations and guidelines are based on data-driven studies that are fundamentally flawed and often corrupt.

He highlights how the resulting recommendations and guidelines have led (world-wide) to millions of unnecessarily early deaths, millions of people suffering unnecessary pain, and widespread use of drugs and treatments that do more harm than good (example: statins), as well as wasting billions of taxpayer dollars every year.

As researchers who have been involved in empirical studies in a very wide range of disciplines over many years we believe that much of what he says is also relevant to all of these disciplines (which include most branches of the physical, natural and environmental sciences, computer science, the social sciences, and law).

Apart from the cases of deliberate corruption and bias (of which Kendrick provides many medical examples) most of the flaws boil down to a basic misunderstanding of statistics, probability and the scientific method.

There are two notable quotes that Kendrick uses, which we believe sum up most of the problems he identifies:

1. “When a man finds a conclusion agreeable, he accepts it without argument, but when he finds it disagreeable, he will bring against it all the forces of logic and reason” Thucydides.
2. “I know that most men, including those at ease with problems of the greatest complexity, can seldom accept even the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they have delighted in explaining to colleagues, which they have proudly taught to others, and which they have woven, thread by thread, into the fabric of their lives.” Leo Tolstoy

The first sums up the extent to which results of empirical work are doctored to suit the pre-conceived biases and hopes of those undertaking it (a phenomenon also known as ‘confirmation bias’).

The second sums up the extent to which there are ideas that represent the ‘accepted orthodoxy’ in most disciplines that are impossible to challenge even when they are wrong. Those brave enough to challenge the accepted orthodoxy risk ruining their careers in their discipline.

Hence, most researchers and practitioners simply accept the orthodoxy without question and help perpetuate flawed or useless ideas in order to get funding and progress their careers. Kendrick describes how these problems lie at the heart of the fundamentally fraudulent peer review system in medicine – which applies to both submitting articles to journals and submitting research grant applications. Once again, we believe that all of the areas of research where we have worked (maths, computer science, forensics, law, and AI) suffer from the same flawed peer review system.

Kendrick is not afraid to challenge the leading figures in medicine, often exposing examples of hypocrisy and corruption. Of special interest to us, however, is that he also challenges the attitude of revered figures in our own discipline. For example, Kendrick highlights two quotes in a recent article by Nobel prize-winner Daniel Kahneman, whose work in the psychology of decision theory and risk is held in the highest esteem.:

1. “The way scientists try to convince people is hopeless because they present evidence, figures, tables, arguments, and so on. But that’s not how to convince people. People aren’t convinced by arguments, they don’t believe conclusions because they believe in the arguments that they read in favour of them. They’re convinced because they read or hear the conclusions from people they trust.
   You trust someone and you believe what they say.
   That’s how ideas are communicated.
   The arguments come later.”
2. “Why do I believe global warming is happening?
   The answer isn’t that I have gone through all the arguments and analyzed the evidence – because I haven’t. I believe the experts from the Academy of Sciences. We all have to rely on experts.”

Kendrick notes the problem here:

“In one breath he states that people aren’t convinced by arguments; they’re convinced because they read or hear conclusions from people they trust. Then he says that we all have to rely on experts. But he does not link these two thoughts together to ask the obvious question.

Just how, exactly, did the experts come to their conclusions?”

Having presented the BBC documentary on Climate Change by Numbers we also got an insight into the extent to which problems exist there.

As good as the book is (and indeed because of how good it is), we feel the need to highlight some points where we believe Kendrick gets it wrong.

There are some statistical/probability errors and over-simplifications, which mostly seem to stem from a lack of awareness of Bayesian probability. For example, he says:

“… although association cannot prove causation, a lack of association does disprove causation”.

This is not true as can be proven by the simple counter example we provide below using a Bayesian network.

Next we believe Kendrick’s faith in randomized control trials (RCTs) as being the (only) reliable empirical basis for medical decision making is misplaced. Because of Simpson’s paradox and the impossibility of accounting for all confounding variables there is, in principle, no solid basis for believing that the result of any RCT is ‘correct’. As is shown in the article here it is possible, for example, that an RCT can find a drug to be effective compared to a placebo in every possible categorization of trial participants, yet the addition of a single confounding variable can result in an exact reversal of the results.

So, if we are saying that even RCTs cannot be accepted as valid empirical evidence, does that mean that we are even more pessimistic than Kendrick about the possibility of any useful empirical research?

No - and this brings us to our final major area of disagreement with Kendrick’s thesis. In contrast to what Kendrick proposes we believe there is an important role for expert judgment in critical decision-making.

In fact, we believe expert judgment is inevitable even if every attempt is made to remove it from an empirical study (it is, for example, impossible to remove expert judgment from the very problem of framing the study and choosing the variables and data to collect).

Given the inevitability of expert judgment, we feel it should be made obvious, transparent, and open to refutation by experiment. Any scientist should be as open and honest about their judgment as possible and be prepared to make predictions and be contradicted by data.

By combining expert judgment with data it is possible to get far more reliable empirical results with much less data and effort than required for an RCT. This is essentially what we proposed in our book and which is being further developed in the EU project Bayes Knowledge.

Refuting the assertion “If there is no association (correlation) then there cannot be causation”.

Consider the two hypotheses:

• H1: “If there is no association (correlation) then there cannot be causation”.
• H2: “If there is causation there must be association (correlation).

Kendrick’s assertion (H1) is, of course, equivalent to H2. We can disprove H2 with a simple counter-example using two Boolean variables a, and b, i.e. whose states are True or False. We do this by introducing a third, latent, unobserved Boolean variable c. Specifically we define the relationship between a,b, and c via the following Bayesian network :