COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Richard M Wood; Christopher J McWilliams; Matthew J Thomas; Christopher P Bourdeaux; Christos Vasilakis

Estimates for the key output measures of interest are presented alongside each of the considered scenarios in Table 3 . Transient outputs corresponding to each of these key areas of interest are presented in Figure 3 across all scenarios. This highlights the key dynamical relationships between these variables. For instance, when full capacity is reached (left plots) then capacity-dependent deaths start to occur (middle plots) based on the extent to which demand continues to exceed supply; with the magnitude of this determining the rate at which deaths accumulate (right plots). Table 3 . Simulation key output measures of interest obtained over 1,000 simulation replications. Note that strategies under Scenarios 1-5 relate to the epidemic curves for cases requiring intensive care equivalent to those contained in Figure 1 , with Scenarios 6 and 7 based upon a 50% flattened version of the Isolation curve containing the same total demand but spread over a 1.5-fold longer period of time. Note * denotes capacity-dependent deaths following rejected intensive care admission. Table 2 ). Black solid lines represent the mean with grey bands containing the 95% confidence intervals. Dashed lines represent capacity associated with respective scenarios (provided as an input to the simulation).