All Coronavirus Models Were Wrong. What happens next?

Some models were — and still are — useful

Pictured: Too long for Scrabble. Source: Pexels

There is an old statistical adage that, towards the start of this endless ennui* I wrote about. While it has appeared in various formats over the years, the general thrust is the same: “All models are wrong, some models are useful”.

Sadly for me, searching for “model” on stock photo websites returns very little maths and lots of semi-nude women. Source: Pexels

The basic meaning is wonderfully simple — modelling cannot ever capture the ‘truth’, because that is not its purpose. We create models to mathematically codify our predictions, and like all guesses these are prey to our assumptions.

Thus, we don’t categorize models into ‘right’ or ‘wrong’, because it’s a waste of time — they’re all going to be wrong to some degree. We can never fully realize the complexity of human experiences with even the most complex maths, because our inputs are confined to the things we know. Imagine trying to account for every single potential transmission of COVID-19 in a statistical framework, from the casual contact of two people on public transport to the lengthy exposure in a movie theatre. Even the best, most sophisticated models only take the first steps in the tangled web of interconnectivity that we call…