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Confidence in Covid-19 models

Nguyen, James (2024) Confidence in Covid-19 models. Synthese. ISSN 1573-0964

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Epidemiological models of the transmission of SARS-CoV-2 played an important role in guiding the decisions of policy-makers during the pandemic. Such models provide output projections, in the form of time-series of infections, hospitalisations, and deaths, under various different parameter and scenario assumptions. In this paper I caution against handling these outputs uncritically: raw model-outputs should not be presented as direct projections in contexts where modelling results are required to support policy-decisions. I argue that model uncertainty should be handled and communicated transparently. Drawing on methods used by climate scientists in the fifth IPCC report I suggest that this can be done by: attaching confidence judgements to projections based on model results; being transparent about how multi-model ensembles are supposed to deal with such uncertainty; and using expert judgement to ‘translate’ model-outputs into projections about the actual world. In a slogan: tell me what you think (and why), not (just) what your models say. I then diffuse the worry that this approach infects model-based policy advice with some undesirably subjective elements, and explore how my discussion fares if one thinks the role of a scientific advisor is to prompt action, rather than communicate information.

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Item Type: Published Article or Volume
Keywords: Models · Simulations · Covid-19 · Climate · Confidence · Objectivity · Science and policy
Subjects: General Issues > Computer Simulation
General Issues > Models and Idealization
Depositing User: James Nguyen
Date Deposited: 05 Apr 2024 06:38
Last Modified: 05 Apr 2024 06:38
Item ID: 23253
Journal or Publication Title: Synthese
Publisher: Springer (Springer Science+Business Media B.V.)
DOI or Unique Handle:
Subjects: General Issues > Computer Simulation
General Issues > Models and Idealization
Date: 5 February 2024
ISSN: 1573-0964

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