A multi-disease simulation swaps its road-injury module for a generative model, cutting runtime 86.6% while DALY error stays at 13%
The Malawi multi-disease agent-based simulation Thanzi La Onse replaces step-by-step event simulation of its road traffic injury module with a conditional tabular generative adversarial network that samples outcomes directly at the agent level. Across 50 independent runs, mean CPU runtime improves by 86.6% (95% bootstrap confidence interval [85.2, 88.1]), mean disability-adjusted life years averaged over age, sex and year carry a 13% error, and multi-disease dynamics are still recovered.