ArticleSocial science & medicine (1982)2022
Making pandemics big: On the situational performance of Covid-19 mathematical models.
Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Models of Pandemics: What Should We Do Differently Next Time?Journal of evaluation in clinical practice · 2026Article
- Predictions, Uncertainty, and Collective Epistemic Work: How Projected Futures Informed and Misinformed Enactments of Covid-19.Social studies of science · 2026Article
- In search of a 'good number': knowledge controversy and population estimates in the endgame of hepatitis C elimination.BMJ global health · 2024Article
- Framing the pandemic: Multiplying "crises" in Dutch healthcare governance during the emerging COVID-19 pandemic.Social science & medicine (1982) · 2023Article
- Domesticating models: On the contingency of Covid-19 modelling in UK media and policy.Social studies of science · 2023Article
- A consensus of evidence: The role of SPI-M-O in the UK COVID-19 response.Advances in biological regulation · 2022Article
- Uncomfortable science: How mathematical models, and consensus, come to be in public policy.Sociology of health & illness · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we trace how mathematical models are made 'evidence enough' and 'useful for policy'. Working with the interview accounts of mathematical modellers and other scientists engaged in the UK Covid-19 response, we focus on two weeks in March 2020 prior to the announcement of an unprecedented national lockdown. A key thread in our analysis is how pandemics are made 'big'. We follow the work of one particular device, that of modelled 'doubling-time'. By following how modelled doubling-time entangles in its assemblage of evidence-making, we draw attention to multiple actors, including beyond models and metrics, which affect how evidence is performed in relation to the scale of epidemic and its policy response. We draw attention to: policy; Government scientific advice infrastructure; time; uncertainty; and leaps of faith. The 'bigness' of the pandemic, and its evidencing, is situated in social and affective practices, in which uncertainty and dis-ease are inseparable from calculus. This materialises modelling in policy as an 'uncomfortable science'. We argue that situational fit in-the-moment is at least as important as empirical fit when attending to what models perform in policy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.