The spread of infectious diseases arises from complex interactions between disease dynamics and human behavior. Predicting the outcome of this complex system is difficult. Consequently, there has been a recent emphasis on comparing the relative risks of different policy options rather than precise predictions. Here, one performs a parameter sweep to generate a large number of possible scenarios for human behavior under different policy options and identifies the relative risks of different decisions regarding policy or design choices. In particular, this approach has been used to identify effective approaches to social distancing in crowded locations, with pedestrian dynamics used to simulate the movement of individuals. This incurs a large computational load, though. The traditional approach of optimizing the implementation of existing mathematical models on parallel systems leads to a moderate improvement in computational performance. In contrast, we show that when dealing with human behavior, we can create a model from scratch that takes computer architectural features into account, yielding much higher performance without requiring complicated parallelization efforts. Our solution is based on two key observations. (i) Models do not capture human behavior as precisely as models for scientific phenomena describe natural processes. Consequently, there is some leeway in designing a model to suit the computational architecture. (ii) The result of a parameter sweep, rather than a single simulation, is the semantically meaningful result. Our model leverages these features to perform efficiently on CPUs and GPUs. We obtain a speedup factor of around 60 using this new model on two Xeon Platinum 8280 CPUs and a factor 125 speedup on 4 NVIDIA Quadro RTX 5000 GPUs over a parallel implementation of the existing model. The careful design of a GPU implementation makes it fast enough for real-time decision-making. We illustrate it on an application to COVID-19.

传染病的传播源于疾病动态与人类行为之间的复杂相互作用。很难预测这个复杂系统的结果。因此，最近的重点是比较不同政策选择的相对风险，而不是精确的预测。在这里，人们执行参数扫描，以生成大量不同政策选项下人类行为的可能场景，并确定有关政策或设计选择的不同决策的相对风险。特别是，这种方法已被用于确定在拥挤地点保持社交距离的有效方法，并使用行人动力学来模拟个人的运动。但是，这会产生很大的计算负载。在并行系统上优化现有数学模型的实现的传统方法导致计算性能的适度改进。相比之下，我们表明，在处理人类行为时，我们可以从头开始创建一个将计算机架构特征考虑在内的模型，从而产生更高的性能，而无需复杂的并行化工作。我们的解决方案基于两个关键观察。(i) 模型不能像科学现象模型描述自然过程那样精确地捕捉人类行为。因此，在设计模型以适应计算架构时存在一些余地。(ii) 参数扫描的结果，而不是单个模拟，是语义上有意义的结果。我们的模型利用这些特性在 CPU 和 GPU 上高效执行。我们在两个 Xeon Platinum 8280 CPU 上使用这个新模型获得了大约 60 倍的加速因子，在 4 个 NVIDIA Quadro RTX 5000 GPU 上通过现有模型的并行实现获得了 125 倍的加速因子。GPU 实现的精心设计使其足够快以进行实时决策。我们在 COVID-19 的应用程序中对其进行了说明。