Sunetra Gupta

On Sage’s Covid models

In the confusion that has arisen from the demonstrable inability of a certain type of mathematical model to predict the time course of the SARS-CoV-2 pandemic, many have taken to reciting – with a variable mixture of glee and sympathy – that ‘all models are wrong but some are useful’.

I have never been comfortable with this statement (it doesn’t really deserve to be called an aphorism) and even less so the smugness with which it is typically pronounced. One might as well say all metaphors are wrong but some are useful.

‘Wrong’ seems to be employed here to suggest that ‘statistical or scientific models always fall short of the complexities of reality (but can still be useful nonetheless)’. But a model, by definition, does not strive to recapitulate reality – instead, it replaces it with an abstraction which advances our comprehension of a system in a way that cannot be achieved simply by spectating upon it or by painstakingly cataloguing its complexities. In the words of Georgia O’Keeffe: ‘Nothing is less real than realism. Details are confusing. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.’

This is not so much a case of poor workmanship as an adherence to incorrect assumptions

The level of detail a model can possess before becoming more confusing than reality itself is a matter of debate but the reason why the Sage and other models failed were not due to an excess of realism compared to other efforts, like the Danish modellers who were more successful in their predictions. Neither was it a matter of badly executed code as many have suggested. It is a fallacy to assume that their polishing code or updating their parameters would have allowed them to arrive at the correct answer. This is not so much a case of poor workmanship as an adherence to incorrect assumptions.

The observed dynamics of SARS-CoV-2 can be easily explained by assuming that the virus arrived at different times within the seasonal cycle of changes in viral transmissibility in different regions, and – as is the case with seasonal coronaviruses – that immunity from natural exposure does not durably block infection, even though protection from severe disease can be lifelong.

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