Fig. 5. Model overview.

Unshaded, white nodes are observed. From bottom to top: The mean effect parameter of NPI i is α_i, and the country-specific effect parameter is α_i,c. On each day t, a country’s daily reproduction number R_t,c depends on the country’s basic reproduction number R_0,c and the active NPIs. The active NPIs are encoded by x_i,t,c, which is 1 if NPI i is active in country c at time t, and 0 otherwise. R_t,c is transformed into the daily growth rate g_t,c using the generation interval parameters and subsequently is used to compute the new infections $N_{t,c}^{(\mathrm{C})}$ and $N_{t,c}^{(\mathrm{D})}$ that will subsequently become confirmed cases and deaths, respectively. Finally, the expected numbers of daily confirmed cases $y_{t,c}^{(\mathrm{C})}$ and deaths $y_{t,c}^{(\mathrm{D})}$ are computed using discrete convolutions of $N_{t,c}^{(.)}$ with the relevant delay distributions. Our model uses both case and death data; it splits all nodes above the daily growth rate g_t,c into separate branches for deaths and confirmed cases. We account for uncertainty in the generation interval, infection-to–case confirmation delay, and the infection-to-death delay by placing priors over the parameters of these distributions.