# Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models

arXiv: https://arxiv.org/abs/2005.08381 · HTML: https://mlsanity.com/paper/2005.08381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 1 of 1)

- [Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models](https://mlsanity.com/paper/2309.12941/) (2023)

## Abstract (arXiv)

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.