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.

中文翻译：

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预测登革热发病率的稳健非参数模型

疾病监测不仅对于预先发现疫情至关重要，而且对于监测疾病的长期趋势也至关重要。在本文中，我们的目标是建立一个战术模型来监测登革热，特别是。大多数现有的登革热预测模型利用其已知的气候和社会人口因素与发病率之间的关系；然而，它们不够灵活，无法捕捉发生率的急剧上升和下降。这是我们论文中使用的方法的动机。我们建立了一个非参数的、灵活的、高斯过程 (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，并表明与其他现有方法相比，GP 模型执行准确，