This paper proposes a new space–time cumulative sum (CUSUM) approach for detecting changes in spatially distributed Poisson count data subject to linear drifts. We develop expressions for the likelihood ratio test monitoring statistics and the change point estimators. The effectiveness of the proposed monitoring approach in detecting and identifying trend‐type shifts is studied by simulation under various shift scenarios in regional counts. It is shown that designing the space–time monitoring approach specifically for linear trends can enhance the change point estimation accuracy significantly. A case study for male thyroid cancer outbreak detection is presented to illustrate the application of the proposed methodology in public health surveillance.

中文翻译：

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线性趋势下计数数据的时空监视

本文提出了一种新的时空累积和（CUSUM）方法，用于检测空间分布的Poisson计数数据在线性漂移的作用下的变化。我们为似然比测试监视统计数据和变化点估计量开发表达式。通过在区域计数中的各种变化情景下进行仿真，研究了所提出的监视方法在检测和识别趋势类型变化中的有效性。结果表明，专门针对线性趋势设计时空监视方法可以显着提高变化点估计的准确性。提出了一项针对男性甲状腺癌暴发检测的案例研究，以说明所提出的方法在公共卫生监测中的应用。