Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e.\ by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model -- EpiCast -- using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.

翻译：对观察到的数据进行复杂的流行病学模型校准是一个关键步骤,它既能提供对当前疾病动态的洞察力,即估计生殖数,又能提供可靠的预测和情景探索。 在这里,我们提出了一个新方法来校准一个以代理器为基础的模型 -- -- EpiCast -- -- 使用一套针对美国不同主要大都市地区的大型模拟组合。特别是,我们提议:一个新的以神经网络为基础的代孕模型,能够同时模仿所有不同地点;以及一个新的事后估计,不仅能够提供所有参数的更准确的后方估计,而且能够使全球参数在各区域联合安装。