The coronavirus response
Boxed in by models
Bibliographic Data
| ID | 12567261 |
|---|---|
| Authors | Ray Pawson (0000-0001-5892-9849, University of Leeds, corresponding author) |
| Year | 2020 |
| Volume | 27 |
| Issue | 2 |
| Pages | 149-167 |
| Publication date | 2020-11-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Evaluation (JOURNAL) |
| Journal identifiers | ISSN: 1356-3890 • E-ISSN: 1461-7153 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1356389020968579 |
| OpenAlex | W3104489769 |
| Language | EN |
| Citations received | 8 |
| References cited | 16 |
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
Business · Coronavirus disease 2019 (COVID-19 · Epistemology · Foreknowledge · Infectious disease (medical specialty · Management science · Operations research · Population · Psychological intervention · Risk analysis (engineering · Sociology · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Engineering · Medicine · Psychology · Viral Infections and Outbreaks Research
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| Citations per year | 1.6 |
|---|---|
| Citation span | 2021 - 2024 (4) |
| Highly cited | No |