论文标题
具有神经网络替代的基于代理的流行病学模型的准确校准
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates
论文作者
论文摘要
将复杂的流行病学模型校准为观察到的数据是一个至关重要的步骤，可以通过估计生殖数量来提供有关当前疾病动态的见解，即\ \提供可靠的预测和场景探索。在这里，我们提出了一种新的方法，可以使用大量的模拟集合为美国的不同主要大都市地区校准基于代理的模型-Epicast-。特别是，我们提出：一个新的基于神经网络的替代模型，能够同时模拟所有不同的位置；以及一个新颖的后验估计，不仅提供了所有参数的更准确的后验估计，而且还可以使整个区域的全局参数的联合拟合。
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.
