Pokharel Gyanendra, Deardon Rob
Department of Mathematics and Statistics, University of Guelph, ON N1G2W1, Canada.
Spat Spatiotemporal Epidemiol. 2014 Oct;11:59-77. doi: 10.1016/j.sste.2014.08.003. Epub 2014 Sep 16.
Parameter estimation for mechanistic models of infectious disease can be computationally intensive. Nsoesie et al. (2011) introduced an approach for inference on infectious disease data based on the idea of supervised learning. Their method involves simulating epidemics from various infectious disease models, and using classifiers built from the epidemic curve data to predict which model were most likely to have generated observed epidemic curves. They showed that the classification approach could fairly identify underlying characteristics of the disease system, without fitting various transmission models via, say, Bayesian Markov chain Monte Carlo. We extend this work to the case where the underlying infectious disease model is inherently spatial. Our goal is to compare the use of global epidemic curves for building the classifier, with the use of spatially stratified epidemic curves. We demonstrate these methods on simulated data and apply the method to analyze a tomato spotted wilt virus epidemic dataset.
传染病机理模型的参数估计可能计算量很大。恩索西等人（2011年）基于监督学习的理念引入了一种对传染病数据进行推断的方法。他们的方法包括从各种传染病模型模拟疫情，并使用根据疫情曲线数据构建的分类器来预测哪些模型最有可能生成观察到的疫情曲线。他们表明，这种分类方法可以相当准确地识别疾病系统的潜在特征，而无需通过例如贝叶斯马尔可夫链蒙特卡罗方法拟合各种传播模型。我们将这项工作扩展到潜在传染病模型本质上具有空间性的情况。我们的目标是比较使用全球疫情曲线构建分类器与使用空间分层疫情曲线的情况。我们在模拟数据上演示这些方法，并应用该方法分析一个番茄斑萎病毒疫情数据集。
