We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.

中文翻译：

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用于小区域疾病聚类估计的时空破坏过程。

我们提出了一种用于疾病聚类估计的时空破坏过程。空间和时间效应的相关性是通过使用时空协变量相关的内核破坏过程引入的。我们通过检查每个模型在各种形状和大小的聚类检测方面的能力，将该模型与时空标准随机效应模型进行了比较。这种比较是针对已知真实风险的模拟数据进行的。对于模拟数据，我们观察到时空破棒过程在检测中高风险集群方面表现更好。对于真实数据，即 1997-2007 年南卡罗来纳州特定县的低出生体重发生率，我们已经说明了如何使用所提出的模型来找到较高发病率县的分组。