demographic variables used from actual patients in the VPS data set. These best-fit statistical distributions were built into the simulation model action logic. 3.
Appendix 4: Procedure for Developing an Evolutionary Strategies Algorithm to Determine Thresholds for Optimal Pandemic Survival 1. Casualty arrival for triage follows the scheme illustrated in Figure 1. 2. POD and DV values for each patient are generated as random variables from the actual statistical distributions derived from the VPS dataset. These statistical distributions were produced using prediction equations presented in Appendix 1 with input clinical and demographic variables used from actual patients in the VPS data set. These best-fit statistical distributions were built into the simulation model action logic. 3. The first trial set of the thresholds TPOD and TDV is generated by the model’s optimization module SimulationRunner. 4. POD and DV values generated in step 2 are compared to the trial TPOD and TDV values from step 3. If POD< TPOD and DV< TDV the decision to admit for treatment is made. All others are rejected from treatment and assumed to die. 5. Patients triaged into the admit group with no PICU bed immediately available are tested for exceeding the maximum wait-time in queue. Those exceeding the corresponding limits are counted as deaths prior to receiving treatment. 6. The treatment group (those that pass triage and are admitted to a PICU bed, and existing patients occupying beds at the start of CSC activation) are tested for mortality likelihood using best-fit equations from Appendix 3 (Supplemental Digital Content 3, http://links.lww.com/CCM/B836) to quantify the segment of the treatment group that dies. 7. Occupancy rates and the percentage of died (treated and untreated) are calculated and incorporated into the simulation model to calculate the objective function OF. 8. Steps 1-7 are replicated for all patients using a new set of random variables for POD and DV generated from POD and DV statistical distributions mentioned in step 2 to capture statistical variability and produce outputs with reasonably narrow confidence intervals. 9. A new set of thresholds TPOD and TDV is generated by the optimization module SimulationRunner. 10. Steps 1-9 are replicated, comparing the new value of objective function (OF) with previous results. Threshold combinations that yield greater OF results are prioritized preferentially compared to previous results. Improving or declining trends in OF results emerge directing the selection of the next threshold combinations for analysis. 11. The optimization procedure stops and the best optimal set of TPOD and TDV values is accepted when no meaningful improvement of OF is achieved.
