Fig. 4: Model Overview.

Dark blue nodes are observed. We describe the diagram from bottom to top. The mean effect parameter of NPI \(i\) is \({\beta }_{i}\). On each day \(t\), a location’s reproduction number \({R}_{t,l}\) depends on the basic reproduction number \({\widetilde{R}}_{0,l}\), the NPIs active in that location and a location-specific latent weekly random walk. The active NPIs are encoded by \({x}_{i,t,l}\), which is 1 if NPI \(i\) is active in location \(l\) at time \(t\), and 0 otherwise. A random walk flexibly accounts for trends in transmission due to unobserved factors. \({R}_{t,l}\) is used to compute daily infections \({N}_{t,l}\) given the generation interval distribution and the infections on previous days. Finally, the expected number of daily confirmed cases \({y}_{t,l}^{\left(C\right)}\) and deaths \({y}_{t,l}^{\left(D\right)}\) are computed using discrete convolutions of \({N}_{t,l}\) with the relevant delay distributions.