Abstract
Science has a mixed record when it comes to predicting the future. Engineers build bridges based on foreknowledge of the forces that they are likely to encounter – and their constructions tend to withstand the test of time. Predicting the future course of epidemics and building intervention to contain them are much more precarious. And yet simulation models produced in prestigious centres for mathematical biology have played a significant role informing coronavirus policy in the United Kingdom and elsewhere. The predictive uncertainties include the inherent variability of the pathogen, considerable variation in host population immunity as well as the concern of this article, namely, the constantly adapting human judgements of those designing, implementing and experiencing the national response to an outbreak. Assumptions about how interventions are implemented and how people will react are, of course, built into modelling scenarios – but these estimates depict behavioural change in fixed, stimulus-response terms. Real reactions to the complex restrictions introduced to combat the virus unfold in scores of different pathways – people comply, they resist, they learn, they grow weary, they change their minds, they seek exceptions and so on. Model building is intrinsically speculative, and it is important that crisis management is not boxed in by its latent simplifications. A more pluralistic evidence base needs to be drawn on, to understand how complex interventions operate within complex societies.
Highlights
All models are wrong?‘All models are wrong’
Not many methodological maxims have their own Wikipedia entry, but this striking aphorism, usually attributed to the British statistician George Box, takes on particular significance as science struggles to predict the trajectory of the coronavirus outbreak
The science in question, mathematical biology, was perhaps not so well known to the public, but in the space of a few months, the basic concepts and imagery associated with the modelling of infectious disease have become remarkably familiar
Summary
All models are wrong?‘All models are wrong’. Not many methodological maxims have their own Wikipedia entry, but this striking aphorism, usually attributed to the British statistician George Box, takes on particular significance as science struggles to predict the trajectory of the coronavirus outbreak. The report begins with a detailed catalogue of epidemiological estimates built into the COVID-19 transmission model and moves on to provide rather scant descriptions of its sociological assumptions about how various NPIs would be implemented and heeded.
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