Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior. However, conventional ABM frameworks are not differentiable and present challenges in scalability; due to which it is non-trivial to connect them to auxiliary data sources. In this paper, we introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources. This provides an array of practical benefits for calibration, forecasting, and evaluating policy interventions. We demonstrate the efficacy of GradABM via extensive experiments with real COVID-19 and influenza datasets.

翻译：机械模拟是流行病学中不可或缺的工具，用于探索复杂动态感染在不同条件下的行为，并应对不确定环境。基于智能体的模型（ABM）是一种日益流行的模拟范式，能够以细粒度的细节和个体行为的自主性来表示接触相互作用的异质性。然而，传统的ABM框架不可微分，且在可扩展性方面存在挑战，因此难以将其与辅助数据源连接。本文提出GradABM：一种可扩展、可微分的基于智能体建模设计，适用于基于梯度学习和自动微分的方法。GradABM可在普通硬件上数秒内快速模拟百万级群体，集成深度神经网络，并吸收异构数据源。这为校准、预测和评估政策干预提供了系列实际优势。我们通过使用真实COVID-19和流感数据集的广泛实验，证明了GradABM的有效性。