All models are wrong, but some are completely wrong

This is a sobering blog post and as such Financial Times published misleading (and fake) news. Statistical parameters are extremely difficult to point out and in my own extremely limited knowledge of epidemiology, you need to do extensive random sampling of tests to detect viral antibodies.

Last week the Financial Times published the headline ‘Coronavirus may have infected half of UK population’, reporting on a new mathematical model of COVID-19 epidemic progression. The model produced radically different results when the researchers changed the value of a parameter named ρ – the rate of severe disease amongst the infected. The FT chose to run with an inflammatory headline, assuming an extreme value of ρ that most researchers consider highly implausible.

Since its publication, hundreds of scientists have attacked the work, forcing the original authors to state publicly that they were not trying to make a forecast at all. But the damage had already been done: many other media organisations, such as the BBC, had already broadcast the headline [1].

In the era of instant news, inflammatory headlines with erroneous results need to be curtailed. While FT makes grandiose claims about “journalism”, it is just another useless media outlet that peddles stupidity under the cover of “intelligent coverage”.


via All models are wrong, but some are completely wrong – Royal Statistical Society Data Science Section