The COVID-19 pandemic highlighted the limitations of existing epidemic simulation tools. These tools provide information that guides non-pharmaceutical interventions (NPIs), yet many struggle to capture complex dynamics while remaining computationally practical and interpretable. We introduce DEpiABS, a scalable, differentiable agent-based model (DABM) that balances mechanistic detail, computational efficiency and interpretability. DEpiABS captures individual-level heterogeneity in health status, behaviour, and resource constraints, while also modelling epidemic processes like viral mutation and reinfection dynamics. The model is fully differentiable, enabling fast simulation and gradient-based parameter calibration. Building on this foundation, we introduce a z-score-based scaling method that maps small-scale simulations to any real-world population sizes with negligible loss in output granularity, reducing the computational burden when modelling large populations. We validate DEpiABS through sensitivity analysis and calibration to COVID-19 and flu data from ten regions of varying scales. Compared to the baseline, DEpiABS is more detailed, fully interpretable, and has reduced the average normal deviation in forecasting from 0.97 to 0.92 on COVID-19 mortality data and from 0.41 to 0.32 on influenza-like-illness data. Critically, these improvements are achieved without relying on auxiliary data, making DEpiABS a reliable, generalisable, and data-efficient framework for future epidemic response modelling.

翻译：COVID-19大流行凸显了现有流行病模拟工具的局限性。这些工具为制定非药物干预措施提供指导信息，但许多工具难以在保持计算实用性和可解释性的同时捕捉复杂的动态过程。我们提出了DEpiABS，一个可扩展、可微分的基于智能体模型，它在机制细节、计算效率和可解释性之间取得了平衡。DEpiABS能够捕捉个体在健康状况、行为和资源约束方面的异质性，同时模拟病毒突变和再感染动态等流行病过程。该模型完全可微分，支持快速模拟和基于梯度的参数校准。在此基础上，我们引入了一种基于z分数的缩放方法，可将小规模模拟映射到任意现实世界的人口规模，且输出粒度损失可忽略不计，从而降低了模拟大规模人口时的计算负担。我们通过对来自十个不同规模区域的COVID-19和流感数据进行敏感性分析和校准，验证了DEpiABS的有效性。与基线模型相比，DEpiABS更详细、完全可解释，并且在COVID-19死亡率数据预测中将平均正态偏差从0.97降至0.92，在流感样疾病数据预测中从0.41降至0.32。关键的是，这些改进的实现不依赖于辅助数据，使得DEpiABS成为一个可靠、可推广且数据高效的后疫情时代流行病应对建模框架。