基于精确高斯过程的登革热预警系统
An Accurate Gaussian Process-Based Early Warning System for Dengue Fever
摘要：
登革热是一种蚊子传播的疾病，存在于巴西所有领土。然而，巴西政府缺乏准确的预警系统来快速预测未来的登革热疫情。这一系统将有助于卫生当局规划其行动，减少该疾病在该国的影响。然而，大多数尝试登革热模型使用参数模型，强制执行特定的预期行为，并未能捕捉登革热动态的内在复杂性。因此，我们提出了一个新的贝叶斯非参数模型的基础上高斯过程设计一个准确和灵活的模型，优于以前的/标准的技术，可以纳入预警系统，特别是在城市从东南部和中西部地区。该模型还有助于通过分析所产生的协方差函数来了解巴西的登革热动态。
英文摘要：
Dengue fever is a mosquito-borne disease present in all Brazilian territory. Brazilian government, however, lacks an accurate early warning system to quickly predict future dengue outbreaks. Such system would help health authorities to plan their actions and to reduce the impact of the disease in the country. However, most attempts to model dengue fever use parametric models which enforce a specific expected behaviour and fail to capture the inherent complexity of dengue dynamics. Therefore, we propose a new Bayesian non-parametric model based on Gaussian processes to design an accurate and flexible model that outperforms previous/standard techniques and can be incorporated into an early warning system, specially at cities from Southeast and Center-West regions. The model also helps understanding dengue dynamics in Brazil through the analysis of the covariance functions generated.
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