Trouble in the trough: how uncertainties were downplayed in the UK’s science advice on Covid-19

Abstract

The 2020 Covid-19 pandemic has forced science advisory institutions and processes into an unusually prominent role, and placed their decisions under intense public, political and media scrutiny. In the UK, much of the focus has been on whether the government was too late in implementing its lockdown policy, resulting in thousands of unnecessary deaths. Some experts have argued that this was the result of poor data being fed into epidemiological models in the early days of the pandemic, resulting in inaccurate estimates of the virus's doubling rate. In this article, I argue that a fuller explanation is provided by an analysis of how the multiple uncertainties arising from poor quality data, a predictable characteristic of an emergency situation, were represented in the advice to decision makers. Epidemiological modelling showed a wide range of credible doubling rates, while the science advice based upon modelling presented a much narrower range of doubling rates. I explain this puzzle by showing how some science advisors were both knowledge producers (through epidemiological models) and knowledge users (through the development of advice), roles associated with different perceptions of scientific uncertainty. This conflation of experts' roles gave rise to contradictions in the representation of uncertainty over the doubling rate. Role conflation presents a challenge to science advice, and highlights the need for a diversity of expertise, a structured process for selecting experts, and greater clarity regarding the methods by which expert consensus is achieved. The analysis indicates an urgent research agenda that can help strengthen the UK science advice system after Covid-19.

Antimicrobial Resistance as a Global Health Threat: The Need to Learn Lessons from the COVID‐19 Pandemic

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SAGE advice and political decision-making: ‘Following the science’ in times of epistemic uncertainty

“…Saltelli et al (2020) do not address the UK situation directly but draw on the idea of post normal science to provide a more general manifesto for the use of models that repeats many of the standard themes from the STS literature, such as the need for wider scrutiny of assumptions and data, and concludes with a call for the scrutiny of mathematical models to be a 'social activity' (Saltelli et al, 2020: 484). Pearce (2020) focuses more directly on the UK and uses the certainty trough metaphor (MacKenzie, 1990) to argue that SAGE scientists were caught between competing role demands and that this led them to downplay the uncertainty around the estimation of a key epidemiological parameter. Both papers imply that the failure in the UK was a failure of science in the sense that SAGE failed to convey the limitations and uncertainties of epidemiological knowledge properly.…”

Integrating Multiple Research Methods to Unravel the Complexity of Human‐Water Systems

“…Do reservoirs alleviate droughts? Concurrently, decision makers have incentives to downplay the aforementioned uncertainties and complexities (Pearce, 2020). Politicians can present “as scientific evidence” a specific outcome, picked ad‐hoc from a broader range of results, which is then used “as a sound justification” for precise actions (Bacevic, 2020).…”