Agent-based models (ABMs) are widely used to study infectious disease dynamics, but their calibration is often computationally intensive, limiting their applicability in time-sensitive public health settings. We propose DeepIMC (Deep Inverse Mapping Calibration), a machine learning-based calibration framework that directly learns the inverse mapping from epidemic time series to epidemiological parameters. DeepIMC trains a bidirectional Long Short-Term Memory (BiLSTM) neural network on synthetic epidemic trajectories generated from agent-based models such as the Susceptible-Infected-Recovered (SIR) model, enabling rapid parameter estimation without repeated simulation at inference time. We evaluate DeepIMC through an extensive simulation study comprising 5,000 heterogeneous epidemic scenarios and benchmark its performance against Approximate Bayesian Computation (ABC) using likelihood-free Markov Chain Monte Carlo. The results show that DeepIMC substantially improves parameter recovery accuracy, produces sharp and well-calibrated predictive intervals, and reduces computational time by more than an order of magnitude relative to ABC. Although structural parameter identifiability constraints limit the precise recovery of all model parameters simultaneously, the calibrated models reliably reproduce epidemic trajectories and support accurate forward prediction with their estimated parameters. DeepIMC is implemented in the open-source R package epiworldRCalibrate, facilitating practical adoption for real-time epidemic modeling and policy analysis. Overall, our findings demonstrate that DeepIMC provides a scalable, operationally effective alternative to traditional simulation-based calibration methods for agent-based epidemic models.

翻译：基于智能体的模型（ABMs）被广泛用于研究传染病动力学，但其校准过程通常计算成本高昂，限制了其在时间敏感型公共卫生场景中的应用。我们提出DeepIMC（深度逆映射校准），一种基于机器学习的校准框架，可直接学习从疫情时间序列到流行病学参数的逆映射。DeepIMC在基于智能体模型（如易感-感染-康复（SIR）模型）生成的合成疫情轨迹上训练双向长短期记忆（BiLSTM）神经网络，从而在推理阶段无需重复模拟即可实现快速参数估计。我们通过涵盖5,000个异质疫情场景的大规模仿真研究评估DeepIMC，并采用基于近似贝叶斯计算（ABC）的无似然马尔可夫链蒙特卡洛方法对其性能进行基准测试。结果表明，DeepIMC显著提升了参数恢复精度，生成了尖锐且校准良好的预测区间，并将计算时间相比ABC降低了一个数量级以上。尽管结构性参数可辨识性约束限制了对所有模型参数的同步精确恢复，但校准后的模型能可靠地再现疫情轨迹，并利用其估计参数支持准确的前向预测。DeepIMC已实现于开源R语言包epiworldRCalibrate中，便于在实时疫情建模与政策分析中实际应用。总体而言，我们的研究证明DeepIMC为基于智能体的流行病模型提供了一种可扩展且操作有效的替代传统仿真校准方法。