Post-normal science part 2

OK, I stopped my last blog half-way through my train of thought. I'm trying to get to the tricky area of science in policy and practice - where it gets messy. I'm coming at this from my work on implementation of science and quality improvement. 

These areas of applied science and also multidisciplinary science have to make different claims to validity or 'truth' compared to normal science, which primarily attempts to be objective. So, in my view applied science or implementation takes some evidence-based theory and it gets mixed with policy framework, economic or market pressures. In public health this mixing should be led by general principles which guide these decisions, such as precautionary principle. My concern is that some discourses such as anti-lockdown, anti-vax etc may contain a grain of evidence-based theory but it is mixed in a different set of values such as market principles, which may lead to solutions which appear morally questionable, such as exacerbating generational divisions (eg locking older and vulnerable people away, whilst supposedly economically active, younger people can go shopping etc).

Now, I'm not sure whether anyone would identify with the discourse that I've outlined, or whether I have caricatured a set of media-represented messages... but my impression of post-normal science is that it takes quite a different set of values or principles as foundation to offer quite a different interpretation of the same data/observations. I also wonder whether post-normal science is a discourse that is pre-empting a Kuhn-ian revolution. That is, with a different set of values the data has different meaning, and hence different recommendations. For me, a key way to progress debates would be to have transparency about assumptions, and also better communication of key aims rather than media-friendly soundbites "herd immunity", "zero-covid" etc

My thoughts on post-normal science, COVID and public health

Prompted by interesting blogs on post-normal science:

Brigitte Nerlich - Public engagement with ‘post-normal science’

Ken Rice - Science in a time of COVID-19

Listening to the modellers instead of public health

From the beginning of news coverage of the pandemic management in UK, the focus has been on modelling the data, flattening the curve, ‘squashing the sombrero’ etc. The days of February and March 2020 seemed to focus on preventing a large second wave, which we heard had been a major cause of death in the 2018 flu pandemic. We were told that the epidemiologist had run models that could compare different interventions; closing schools, airports, etc to tell us which of these could have most effect. Which of these should be prioritised and which could remain open to ‘save the economy’ – thus the concept of a dilemma of either saving the economy or the managing the health crisis was disseminated. The power of these modelling techniques was this apparent intelligence and targeting of interventions, thus ‘saving’ other populations or ‘markets’ that could continue, because the model indicated that they had little impact on future spread of the epidemic. The strength of these techniques are in the raw data available and the weakness in the initial assumptions that make up the model. These assumptions are things like how transmissible is the virus by close contact between people, versus passing in a large building such as an airport, for example.

Modelling was favoured by UK Government, I think, because this was an opportunity to ‘show off’ our latest development in ‘big data’. Also mentioned in the news media was behavioural economics and ‘nudge theory’ – which is another favourite of Government, where the population can be ‘managed’ through friendly nudges rather than draconian legislation. There was talk about fatigue of the population if Government locked down too soon.

I was pleased, initially, to hear that Government were going to ‘follow the science’ – it was a relief after the post-expert years – where we had heard enough from experts. However the science that was chosen to be followed and the interventions prioritised seemed to be selectively chosen.

What was wrong with public health?

Public health professionals have been managing epidemics effectively for over 150 years. Arguably one of UK’s best ‘global exports’, but I guess it was seen as too traditional, or even basic. The strength of the public health approach is that it is not so reliant on initial assumptions, but in the basics of cutting transmission links by isolating people who are ill or may be contagious. Rather than ‘big data’ and ‘smart’ approaches, this requires local resources and public health experts working with local communities. People have to forego some rights in exchange for protection from infection; rights include some elements of privacy, freedom to go to work, to go to the shops. For me, the key issue to learn from the management of COVID is that rather than predicting behaviour of the population, we need to build trust, and this needs to be done at a community level, rather than at a population level. Trust in a local health protection officer may be more easily obtained rather than trust in an app which has access to your personal data.

Public health is not a ‘pure science’ – it’s a multidisciplinary science and also a professional practice. It acknowledges political and economic pressures and attempts to bring to bear the best science from different academic disciplines. I don’t think anyone would connect public health with post-normal science…