论文标题
动态高斯过程模型的演变，并应用于疟疾疫苗覆盖范围预测
The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction
论文作者
论文摘要
基于高斯工艺（GP）的统计替代物是模拟模拟现实现象或复杂系统的昂贵计算机模型的输出的流行，廉价的替代品。在这里，我们讨论了动态GP模型的演变 - 具有时间序列输出的计算机模拟器的计算有效统计替代物。主要思想是使用标准GP模型的卷积，其中权重由时间组件的响应矩阵的奇异值分解（SVD）引导。动态GP模型还采用了一种局部建模方法，用于为大型数据集构建统计模型。 在本章中，我们使用几个流行的基于测试功能的计算机模拟器来说明动态GP模型的演变。我们还使用此模型来预测全球疟疾疫苗的覆盖范围。疟疾仍在影响八十多个国家集中在热带地带上。仅在2019年，这就是全球435,000多人死亡的原因。如果及时诊断出恶意很容易治愈，但是常见症状使它变得困难。我们专注于最近发现的称为Mos-Quirix（RTS，S）的可靠疫苗，该疫苗目前正在人类试验中。借助从世界卫生组织获得的有关其他疫苗的剂量，功效，疾病发病率和通讯性的公开数据，我们预测78个易疟疾国家的疫苗覆盖范围。
Gaussian process (GP) based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that simulate real-world phenomena or complex systems. Here, we discuss the evolution of dynamic GP model - a computationally efficient statistical surrogate for a computer simulator with time series outputs. The main idea is to use a convolution of standard GP models, where the weights are guided by a singular value decomposition (SVD) of the response matrix over the time component. The dynamic GP model also adopts a localized modeling approach for building a statistical model for large datasets. In this chapter, we use several popular test function based computer simulators to illustrate the evolution of dynamic GP models. We also use this model for predicting the coverage of Malaria vaccine worldwide. Malaria is still affecting more than eighty countries concentrated in the tropical belt. In 2019 alone, it was the cause of more than 435,000 deaths worldwide. The malice is easy to cure if diagnosed in time, but the common symptoms make it difficult. We focus on a recently discovered reliable vaccine called Mos-Quirix (RTS,S) which is currently going under human trials. With the help of publicly available data on dosages, efficacy, disease incidence and communicability of other vaccines obtained from the World Health Organisation, we predict vaccine coverage for 78 Malaria-prone countries.
