The so-called Covid “supermodel” commissioned by the Govt of India is fundamentally flawed.
Not surprising, since it was built by people who had no clue about epidemiology or public health (the Govt ignored the advice of *actual* public health experts).
See for yourself. Thread
On 09/03/21 they explicitly predicted there would be no “second wave”.
OK, models can get their predictions wrong. Presumably, once it became clear there was a raging second wave, the model builders would admit their model was flawed, right?
Wrong.
The basis for the claim of “no second wave” is this projection from 09/03/21. It predicts a “gentle increase” followed by a “gentle decline”, peaking at about 30,000 cases per day.
Once you make a prediction, you either stick by it, or admit the model is wrong. But ...
What do we have here? A projection on 25/03/21 that India will peak at “between 70-80K new infections per day”. OK, just off by a factor of two, maybe not enough to admit any flaws.
Let’s hope they stick to this prediction.
Oh wait ...
At least they’re “embarrassed” that they failed to predict a second wave <smiley face>. “Of course,” they say, “this wave is being driven primarily by Maharashtra”.
Maybe good to let the Govt know that they should not making policy based on these predictions?
Three days later, 28/03/21. Hey the data are matching our predictions <smiley face>! It’s all good, you can trust us. Well, the peak has gone up to 85K per day, but what’s a few percent error between friends.
Final prediction?
No ...
02/04/21. Yes we admit there is a second wave. But don’t worry, the peak will be at 100K infections per day.
Final prediction?
Oh no, two days later on 04/04/21, “the peak keeps going up”!
What does this even mean? Why not say “our prediction was wrong”?
Is this a good time to introspect that there’s something fundamentally flawed about the “supermodel”, whose predictions change from day to day?
One day later, 05/04/21. Predicted peak has shifted to 114K per day.
Recall that the 04/04/21 prediction was 110K per day. Where did 78K come from? Apparently the predictions are changing so rapidly they are now making more than one prediction per day.
Let’s shift goalposts. Let’s say we got it correct as long as we predicted the *timing* of the peak correctly, since we clearly have no idea what we are doing about the actual case load.
“Will keep a count <smiley face>”.
07/04/21. “Peak value for India keeps rising! It is now 150K”.
But don’t worry, we’re now only predicting the timing. We predict a peak Apr 21-25, and we’re sticking with this prediction.
One week later, 14/04/21. Peak now at 190K but hey, we’re sticking to the Apr 20-25 prediction.
Here’s a nice touch: “The date of turning will decide the value of the peak.” I.e. I’ll only predict the peak caseload once we’re past the peak!
Well, we’re past Apr 20-25 and still nowhere near the peak. Yesterday we saw 4,01,991 cases, far above *any* of the predictions of the so-called “supermodel”.
Is it now time to retire the supermodel and turn to advice from genuine public health experts? Apparently not.
It is not my intent to pick on one individual. Manindra Agarwal is a respected scientist in his field, but he is not an epidemiologist.
The main fault lies with the Govt, for ignoring true experts and turning to non-experts, who told them what they wanted to hear.
(Apologies for the mis-spelling, it should be Manindra *Agrawal*.)
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What’s behind India’s second wave? A simple model can help us understand what’s going on.
There’s lots of variation between cities, so it’s best to look at a single city like Bangalore. Why did cases in Bangalore start to dip in Oct 2020? Why did they start to rise in Mar 2021?
“R0” is the average number of new infections that would be caused by one infected individual, assuming nobody is immune. This depends both on behaviour and viral biology.
Let’s define the “level of caution” as the inverse of R0. Less cautious people cause more infections.
Let’s scale the y-axis from 0 to 1. In RED is the fraction of people who have recovered, and so are immune (measured from BOTTOM). In GREEN is the level of caution (measured from TOP).
When the red and green curves cross, new cases per day start to fall. This is “herd immunity”.
"The term community transmission means that the source of infection for the spread of an illness is unknown or a link in terms of contacts between patients and other people is missing." [Wikipedia]
The novel coronavirus originated in Wuhan. So *every* case in India must have arisen, via a chain of contacts, from Wuhan. Therefore, *every* case in India involves an international traveller as part of the chain of infection. This is a matter of logic, not epidemiology.
2/5
Community transmission is a statement about the limits of our knowledge. It's not a statement about the virus.
In some cases we can establish the chain to an int'l traveller. This is not community transmission.
In some cases we can't. This is community transmission.
3/5
In coming days, it's important for the public to know that experts looking at the same data can legitimately disagree.
E.g. Consider the debate about whether community transmission has begun. Due to Bayes' Theorem, the answer depends on what you already believe.
Thread.
1/9
Assume the ideal case: don't consider asymptomatic carriers, let test results be instant.
We test 1,000 symptomatic cases and all are negative. This means there's no community transmission right?
Wrong. To interpret the result, we need to make a bunch of assumptions.
2/9
First we need to define some level of C.T. to look for. In the high-incidence group of symptomatic int'l travellers & contacts, about 2% are positive (283 out of 15,701 tested).
Let's assume that incidence due to C.T. among all symptomatic people is a tenth of that: 0.2%.
3/9
I was shocked to see such shoddy journalism from @the_hindu. They spun a 3-month-old *open-access* paper into a "secret" conspiracy theory, without even requesting a statement from the authors! The report, even the headline, contained many false statements.
A science journalist would have read the paper and spoken to the authors, rather indulging in speculation in the midst of a public health crisis. As @NCBS_Bangalore was not given a chance to respond, we have issued a clarification, covered by @1amnerd. thewire.in/the-sciences/c…
This thread explains the facts, and shows precisely how flawed the original report was. @the_hindu has been sent a formal statement from NCBS, but they are yet to issue a correction.
In dogs, three genes *independently* determine if the coat is short or long, soft or wiry, and straight or curly! science.sciencemag.org/content/326/59…
This kind of "gene-for-X" story is actually very rare, despite what the news headlines often claim. So why is it true in this case?
Knowing how cells work, it's not surprising most traits are "complex": they depend on 1000s of genes: quantamagazine.org/omnigenic-mode…. Conversely, mutations in most genes are "pleiotropic": they affect 1000s of traits.
So when would we expect a single gene to control a single trait?
Genes encode both "material" and "regulatory" proteins (as well as regulatory RNA and DNA elements).
Just like a tap controls the outflow of hot and cold water, regulatory proteins control the levels of the material proteins that give a cell its physical and chemical properties.