论文标题
用于预测登革热发病率的强大而非参数模型
A robust and non-parametric model for prediction of dengue incidence
论文作者
论文摘要
疾病监测不仅对于先前检测暴发至关重要，而且对于从长远来看监测疾病的趋势也是必不可少的。在本文中，我们旨在建立一个战术模型，尤其是对登革热的监视。登革热预测的大多数现有模型都利用了其气候和社会人口统计学因素之间的已知关系，但它们的发病率计数不足以捕获发病率计数的陡峭而突然的上升和下降。这是我们论文中使用的方法的动机。我们构建了一个非参数，灵活的高斯工艺（GP）回归模型，该模型依赖于过去的登革热发病率和气候协变量，并表明GP模型与其他现有方法相比，与其他现有方法相比，GP模型可以准确地执行，从而为健康机构提供了良好的战术和强大模型来计划其行动过程。
Disease surveillance is essential not only for the prior detection of outbreaks but also for monitoring trends of the disease in the long run. In this paper, we aim to build a tactical model for the surveillance of dengue, in particular. Most existing models for dengue prediction exploit its known relationships between climate and socio-demographic factors with the incidence counts, however they are not flexible enough to capture the steep and sudden rise and fall of the incidence counts. This has been the motivation for the methodology used in our paper. We build a non-parametric, flexible, Gaussian Process (GP) regression model that relies on past dengue incidence counts and climate covariates, and show that the GP model performs accurately, in comparison with the other existing methodologies, thus proving to be a good tactical and robust model for health authorities to plan their course of action.
