While models are important tools in the toolbox – and can be helpful for decision making – they are math exercises - built around a series of assumptions and are only as good as those assumptions. They also need to be applied with consideration of contextual factors in the context and alongside other public health tools.
For example, if we don’t know the denominator and how many people have already been infected, infection fatality rate (as opposed to case fatality rate), age-specific mortality rates, underlying conditions, plus we have no real evidence on the effectiveness of the interventions being proposed. So the conclusions are far from evidence-based.
This may not be the best source of information for rational decision making by our policymakers.