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Model uncertainty, the COVID-19 pandemic, and the science-policy interface

Thorén, Henrik LU and Gerlee, Philip (2024) In Royal Society Open Science 11(2).
Abstract
The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is considerably complicated, we argue, by the fact that the models themselves are often instrumental in structuring the involved uncertainties. In this paper we explore how models ‘domesticate’ uncertainties and what this implies for science-for-policy. We analyse three examples of uncertainty domestication in models of COVID-19 and argue that we need to pay more attention to how uncertainties are domesticated in models used for policy support, and the many ways in which uncertainties are domesticated within... (More)
The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is considerably complicated, we argue, by the fact that the models themselves are often instrumental in structuring the involved uncertainties. In this paper we explore how models ‘domesticate’ uncertainties and what this implies for science-for-policy. We analyse three examples of uncertainty domestication in models of COVID-19 and argue that we need to pay more attention to how uncertainties are domesticated in models used for policy support, and the many ways in which uncertainties are domesticated within particular models can fail to fit with the needs and demands of policymakers and planners. (Less)
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Royal Society Open Science
volume
11
issue
2
article number
230803
publisher
Royal Society Publishing
external identifiers
  • scopus:85185335838
  • pmid:38356870
ISSN
2054-5703
DOI
10.1098/rsos.230803
language
English
LU publication?
yes
id
829614b5-213a-423e-b281-cd7a5b18313f
date added to LUP
2024-01-11 14:54:56
date last changed
2024-03-28 03:00:35
@article{829614b5-213a-423e-b281-cd7a5b18313f,
  abstract     = {{The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is considerably complicated, we argue, by the fact that the models themselves are often instrumental in structuring the involved uncertainties. In this paper we explore how models ‘domesticate’ uncertainties and what this implies for science-for-policy. We analyse three examples of uncertainty domestication in models of COVID-19 and argue that we need to pay more attention to how uncertainties are domesticated in models used for policy support, and the many ways in which uncertainties are domesticated within particular models can fail to fit with the needs and demands of policymakers and planners.}},
  author       = {{Thorén, Henrik and Gerlee, Philip}},
  issn         = {{2054-5703}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{Royal Society Publishing}},
  series       = {{Royal Society Open Science}},
  title        = {{Model uncertainty, the COVID-19 pandemic, and the science-policy interface}},
  url          = {{http://dx.doi.org/10.1098/rsos.230803}},
  doi          = {{10.1098/rsos.230803}},
  volume       = {{11}},
  year         = {{2024}},
}