Epidemics, ethics and uncertainty: the roles of statistics versus mathematics
- Date: Friday 4 November 2022, 11:00 – 12:00
- Location: Roger Stevens LT 22 (10M.22)
- Cost: Free
Jane Hutton, University of Warwick
A few mathematicians have had considerable influence in the last two years over whether people lived and flourished, or died.
Some mathematicians have focussed on mathematical models which only consider a single illness. Applied statisticians know that it is critical to first decide what the question is: "Minimise deaths due to Covid-19 this year?" or "Minimise the impact of Covid-19 on well-being over ten years?"
The ethical status of an expert who gives a simple answer to the first question, without uncertainty or alternatives, will be examined.
Some publications by influential mathematics groups were directly misleading. People were placed under effect house arrest, when, on balance of probability, they were innocent of Covid.
Numbers of "covid" hospitalisations and deaths were quoted without context, helping to create a climate of fear. Predictions of 6,000 UK hospital admissions per day in January 2022 relied on the assumption that South African scientists are incompetent. I will discuss whether this can be construed as racism, and compare the issues with ideas of racial inequity in UK covid death rates.
Uncertainty in diagnostic tests, missing information and measurement errors all feed into transmitted variation. Despite this, mathematical predictions of covid cases were used to justify lockdowns even in countries where people would starve as a consequence. Some statisticians have tried to estimate the damage to children's education and wellbeing, and illness and deaths due to lack of access.
Such mathematical modelling cannot be justified within virtue, deontological, utilitarian or care ethics, though Zoroaster or Nietzsche might be invoked. It is essential to consider context, and the probable consequences, as explained in the International Statistics Institute Code of Professional Ethics. Assessment of the validity of model assumptions, data quality, adequacy of the fit of models and accuracy of predictions is essential, and essentially statistical.