Numerous modelling efforts have attempted to characterize the effects of different non-pharmaceutical interventions (NPIs) on the Covid-19 spread. Arguably the most famous is one published in Nature by an Imperial College group. A slight variation of it was later published in Science by a group of Oxford researchers. Both publications are based on hierarchical Bayesian modelling that aims to explain observed data by information on enacted NPIs. Due to the Bayesian approach, the models become quite complex and opaque, with many priors that have been assigned more or less ad hoc, and there are even priors on the prior parameters. We show how these models can be recast into the classic linear regression framework. This enables us to transparently analyze basic concepts such as persistency of excitation, identifiability, and model sensitivity.