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

Publication date: Feb 01, 2024

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.

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Concepts Keywords
Models COVID-19
Pandemic epidemiology
Policymaking policy
Science science-policy interface
uncertainty

Semantics

Type Source Name
disease MESH uncertainty
disease MESH COVID-19 pandemic
disease MESH death
disease IDO host
disease VO population
disease IDO intervention
disease VO time
disease VO device
disease IDO process
drug DRUGBANK Albendazole
drug DRUGBANK Huperzine B
drug DRUGBANK Pirenzepine
disease VO vaccinated
disease VO Viruses
drug DRUGBANK Etodolac
disease VO Gap
disease IDO production
disease VO effective
disease MESH infectious disease
pathway REACTOME Infectious disease
drug DRUGBANK Activated charcoal
disease VO company
drug DRUGBANK Water

Original Article

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