I've been searching for a good metaphor to explain what genes are. Hear me out. (No doubt, someone has thought of this before.)
Think of an orchestra.
There are players -- these are the laws of physics and chemistry.
There's a symphony -- this is the organism.
Finally there are the instruments -- these are the genes.
During a performance, each instrument is played intermittently. This has to do with how the composition plays out, how everything works together.
This is the development of the organism.
Some pieces (say, a blues standard) can be played with just a few instruments. Others (say, Beethoven's 5th) require a larger orchestra.
Don't confuse the number of genes with the complexity and subtlety of the organism.
Most instruments have their quirks; they're slightly out of tune. This adds to the nuance of any piece.
Genes have point mutations; this leads to varying outcomes for the organism.
People confuse the idea of having an instrument in the repertoire, with the idea of playing an instrument intermittently in a performance.
The existence of a gene and its experession over the life of an organism are very different things.
Sometimes instruments change. The harpsichord led to the piano.
Sometimes instruments appear out of nowhere. The steel drum was repurposed out of junk.
Recording adds a layer of complexity. Riffs can be reused.
Remixing is key. New combinations work in unexpected ways.
This is variation.
Ultimately, the audience is moved by the performance; or not.
This is natural selection.
It's not a perfect metaphor; nothing ever is. But (compared to "blueprints" or "programs" or "circuits") I think it gets closer to the heart of things, closer to the structure of life.
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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’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.