Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) and their interactions, and parameterizing the process of infection based on such interactions based on the geography and demography of the city. However, such models are computationally very expensive, and the complexity is often linear in the total number of agents. This seriously limits the usage of such models for simulations, which often have to be run hundreds of times for policy planning and even model parameter estimation. An alternative is to develop an emulator, a surrogate model that can predict the Agent-Based Simulator's output based on its initial conditions and parameters. In this paper, we discuss a Deep Learning model based on Dilated Convolutional Neural Network that can emulate such an agent based model with high accuracy. We show that use of this model instead of the original Agent-Based Model provides us major gains in the speed of simulations, allowing much quicker calibration to observations, and more extensive scenario analysis. The models we consider are spatially explicit, as the locations of the infected individuals are simulated instead of the gross counts. Another aspect of our emulation framework is its divide-and-conquer approach that divides the city into several small overlapping blocks and carries out the emulation in them parallelly, after which these results are merged together. This ensures that the same emulator can work for a city of any size, and also provides significant improvement of time complexity of the emulator, compared to the original simulator.

翻译：个体仿真模型对于模拟物理或社会过程（如城市疫情传播）具有重要价值。此类模型通过定义个体（智能体）的行为及相互作用，并基于城市地理与人口统计特征对感染过程进行参数化。然而，这类模型计算成本极高，其复杂度通常与智能体总数呈线性关系，严重制约了其在政策规划（需数百次迭代）乃至模型参数估计等场景中的应用。替代方案是开发仿真器——一种基于初始条件和参数预测个体仿真模型输出的替代模型。本文提出一种基于扩张卷积神经网络的深度学习模型，能够高精度地仿真此类个体模型。研究表明，使用该模型替代原始个体模型可大幅提升仿真速度，从而实现更快速的数据标定和更广泛的情景分析。我们所考虑的模型具有空间显式特性，可模拟感染者具体位置而非粗略数量统计。此外，本仿真框架采用分治策略：将城市划分为若干重叠小区域并行执行仿真，最终合并结果。该方法确保同一仿真器可处理任意规模城市，且相较于原始仿真器，显著提升了时间效率。