We introduce a novel two-stage parameter estimation framework designed to improve computational efficiency in settings involving complex, stochastic, or analytically intractable dynamic models. The proposed method, termed \textit{ABC-RF-rejection}, integrates Approximate Bayesian Computation (ABC) rejection sampling with Random Forest (RF) classification to efficiently screen parameter sets that produce simulations consistent with observed data. We evaluate the performance of this approach using both a deterministic Susceptible-Infected-Removed (SIR) epidemic model and a spatially explicit stochastic epidemic model. Results indicate that ABC-RF-rejection achieves substantial gains in computational efficiency while maintaining parameter inference accuracy comparable with standard ABC rejection methods. Finally, we apply the algorithm to estimate parameters governing the spatial spread of cassava brown streak disease (CBSD) in Nakasongola district, Uganda.

翻译：本文提出一种新型两阶段参数估计框架，旨在提升复杂、随机或解析难处理的动态模型场景中的计算效率。所提出的方法称为\textit{ABC-RF-rejection}，它将近似贝叶斯计算（ABC）拒绝采样与随机森林（RF）分类相结合，以高效筛选能产生与观测数据一致的模拟结果的参数集。我们通过确定性易感-感染-移除（SIR）传染病模型和空间显式随机传染病模型评估了该方法的性能。结果表明，ABC-RF-rejection在保持与标准ABC拒绝方法相当的参数推断精度的同时，实现了计算效率的大幅提升。最后，我们将该算法应用于乌干达Nakasongola地区木薯褐条病（CBSD）空间传播的参数估计。