估计具有点和聚合病例数据的Log高斯Cox过程模型的二十种方法：用于R的rts2包
Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R
AI总结：
该论文介绍了R包rts2，提供了二十种估计Log高斯Cox过程模型的方法，包括数据处理工具和针对聚合数据的新颖实现，支持实时监测和不确定性量化。
摘要：
R包rts2为Log高斯Cox过程（LGCP）模型提供了数据处理和模型拟合工具。LGCP模型是疾病及其他类型监测的关键方法，并基于空间参考和时间戳的病例数据提供跨感兴趣区域的风险预测手段。然而，这些模型可能难以指定且计算上估计成本高。对于许多监测场景，我们需要利用常规可用数据在近实时内获得结果，以指导和引导政策响应，或者由于计算资源有限。目前，在实时背景下，具有可靠预测和不确定性量化的软件实现有限。rts2包提供了一系列现代高斯过程近似和模型拟合方法来拟合LGCP，包括使用贝叶斯和随机最大似然方法估计协方差参数。该包提供了一套数据处理工具。我们还提供了一种新颖的实现，当病例数据聚合到不规则网格（如人口普查区）时估计LGCP。
英文摘要：
The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas.
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