Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models
Abstract
Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the underlying modelling assumptions and limitations communicated so that decision-makers can readily understand them? When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. We argue that any COVID-19 simulation model that is used to guide critical policy decisions would benefit from being supported with such a case to explain how, and to what extent, the evidence from the simulation can be relied on to substantiate policy conclusions. This would enable a critical review of the implicit assumptions and inherent uncertainty in modelling, and would give the overall decision-making process greater transparency and accountability.
Cite
@article{arxiv.2005.08381,
title = {Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models},
author = {Ibrahim Habli and Rob Alexander and Richard Hawkins and Mark Sujan and John McDermid and Chiara Picardi and Tom Lawton},
journal= {arXiv preprint arXiv:2005.08381},
year = {2023}
}
Comments
6 pages and 2 figures