(1) Background: The stochastic nature of agent-based models (ABMs) may be responsible for the variability of simulated outputs. Multiple simulation runs (i.e., replicates) need to be performed to have enough sample size for hypothesis testing and validating simulations. The simulation outputs in the early-stage of simulations from non-terminating ABMs may be underestimated (or overestimated). To avoid this initialization bias, the simulations need to be run for a burn-in period. This study proposes to use multiple scale space-time patterns to determine the number of required replicates and burn-in periods in spatially explicit ABMs, and develop an indicator for these purposes. (2) Methods: ABMs of vector-borne disease transmission were used as the case study. Particularly, we developed an index, D, which enables to take into consideration a successive coefficient of variance (CV) over replicates and simulation years. The comparison between the number of replicates and the burn-in periods determined by D and those chosen by CV was performed. (3) Results: When only a single pattern was used to determine the number of replicates and the burn-in periods, the results varied depending on the pattern. (4) Conclusions: As multiple scale space-time patterns were used for the purposes, the simulated outputs after the burn-in periods with a proper number of replicates would well reproduce multiple patterns of phenomena. The outputs may also be more useful for hypothesis testing and validation.
Using multiple scale space-time patterns in variance-based global sensitivity analysis for spatially explicit agent-based models
