论文标题
地统计学和机器学习的流行病学研究中的空气污染模型
Air pollution models in epidemiologic studies with geostatistics and machine learning
论文作者
论文摘要
大型地区空气污染模型的开发是基于人群的流行病学研究的优先事项。大数据信息系统和机器学习算法的快速开发为当前模型框架的改进开辟了新的基础。这些评论概述了最近的贡献，并概述了从地统计学和机器学习角度进行的扩展。在接下来的几年中，预期的进步将扩大学习算法的使用来对空间趋势进行建模，并将空间协方差模型纳入学习过程。这些扩展将完善现有的建模框架，从而有助于提高空气污染模型进行暴露评估的准确性。
Development of air pollution models for large regions is a priority for population-based epidemiologic studies. The rapid development of big data information systems and machine learning algorithms have opened new grounds for refinements of current model frameworks. This commentary overviews recent contributions and outlines extensions from geostatistics and machine learning perspectives. For the coming years, expected advances will expand the use of learning algorithms to model spatial trends and incorporate spatial covariance models in the learning processes. These extensions will refine existing modelling frameworks contributing to improve accuracy of air pollution models for exposure assessment.
