  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.
