Thread by @tomaspueyo

Epidemiology has striking similarities to digital virality.

Eg, the Transmission Rate R in epidemiology is very similar to the k-factor in digital products' virality. It's one of the reasons why I was so concerned so early about the #coronavirus. Thread.
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The R conveys how many ppl will be infected by a source infected person. For the coronavirus, the worldwide average without reduction measures is ~2.7. Anything above 1 will spread.

This is why The Hammer and the Dance focused on the R.
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medium.com/@tomaspueyo/co…

The k-factor is the same thing: It conveys how many friends will a new user bring to your product. Anything above 1 is awesome: Your product will explode on its own, spreading between friends.

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The R depends on:
1. The number of contacts spreaders have
2. What % of these get infected
3. What % of these infect others
4. How fast that entire cycle happens.

This is why I drew this graph for the Hammer and the Dance, and updated it once science had caught up

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Similarly, in product virality, the k-factor depends on:

1. The number of viral messages users send
2. What % of these start using your product
3. What % of these become viral too
4. How fast that entire cycle happens.

Here it is, illustrated:
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This comes from a full article I wrote on virality a year ago or so.
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medium.com/swlh/the-12-ru…

And this is how my viral applications grew thanks to their high k-factors. One of them passed 10 million users within 7 months.

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When I was modeling the #coronavirus back in Feb, fear struck when I realized its eerie similarity to my old apps: They had grown like wildfire, and their k-factor wasn't even as high as corona's R0=2.5 (what we thought at the time).

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Also, both the R and the k-factor have the same downside: They don't convey how fast the spread happens. R0=2.5 is bad if it takes 14 days, but it would be much, much worse if it took 14 hours to spread to those same 2.5 ppl.
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