Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization
Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization
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DOI：
10.1098/rsif.2017.0520
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发表时间：
2017-09-01
影响因子：
3.9
通讯作者：
Gething, Peter W.
中科院分区：
文献类型：
--
作者：
Bhatt, Samir;Cameron, Ewan;Gething, Peter W.
传染病地图——绘制感染力、流行程度和人类健康负担的空间变化——为支持实现全球健康目标的规划提供了重要的证据基础。当代疾病绘图工作已采用统计建模方法来正确认识可用测量及其空间插值的不确定性。最常见的此类方法是高斯过程回归，这是一个由两个组件组成的数学框架：利用多个自变量的预测能力的均值函数，以及针对均值残差产生时空收缩的协方差函数。尽管已经开发了许多技术来提高协方差函数的灵活性和拟合性，但平均函数的模型通常仅限于简单的线性项。对于已知由环境和社会经济因素之间复杂的相互作用驱动的传染病，改进的平均函数模型可以极大地提高预测能力。在这里，我们提出了一种基于堆叠泛化的集成方法，该方法允许将多个非线性算法均值函数联合嵌入高斯过程框架内。我们应用这种方法来绘制撒哈拉以南非洲地区的恶性疟原虫流行率数据，结果表明，广义集成方法明显优于任何单独的方法。
Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.