One intriguing aspect of the scientific discussion about #COVID19 measures is how entirely unscientific it feels to me. "Letting young people get infected by the virus is immoral" or "lockdowns are evil" are not scientific statements.
Science can inform on the current situation (e.g. number of undetected #COVID19 cases) and provide plausible projections under different interventions, or absence thereof. Though, science does generally not, in itself, inform on the morality of any such intervention.
Thus, when a scientist expresses their views on what #COVID19 measures the population should adopt, they do not tend to follow the science or go against it, but make a subjective, emotional statement about what they believe is morally/ethically right or wrong.
There is a scientific approach to decide on the merit of #COVID19 measures, based on 'scientific utilitarianism'. Scientists can estimate the cost of measures in terms of gain/loss of lives, years of life, or more complex indices including e.g. wellbeing and education.
A utilitarian approach to #COVID19 may seem appealing to some, but it is not without its challenges. While the calculations are based on science, the choice of which metric to maximise/minimise is not. This becomes particularly difficult when qualitative factors are included.
For example, a #COVID19 model minimising deaths in the short term is likely to give a very different answer about what public health measures perform 'best', relative to one maximising years of life over the next decades and even more so from one including wellbeing/education.
The choice of the metric to minimise/maximise (e.g. deaths, years of life or anything more inclusive of general health) falls outside the realms of science. It is a moral/ethical problem that should not be left to decide by scientists alone.
It feels somewhat unlikely to me that a societal consensus could emerge about what metric #COVID19 public health measures should aim to minimise/maximise (e.g. deaths or years of life), over what timescale and at what geographical scale (country level or more globally).
In the unlikely event a societal consensus emerged on what metric to minimise/maximise, one problem remains. Long-term projections from complex mathematical models tend to be imperfect, because the models are flawed, poorly parameterised or the situation changed unexpectedly.
That said, despite the limitations of an utilitarian approach to #COVID19, injecting a bit more science in the scientific debate on #COVID19 should be beneficial to everyone.
You can follow @BallouxFrancois.
Tip: mention @twtextapp on a Twitter thread with the keyword “unroll” to get a link to it.

Latest Threads Unrolled: