School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China.
Department of Statistics, University for Development Studies, Navrongo, Ghana.
Comput Math Methods Med. 2020 May 15;2020:7267801. doi: 10.1155/2020/7267801. eCollection 2020.
Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.
已有文献提出了几种用于检测疾病爆发或发病率变化的方法。这些方法大多基于似然比，以及谢哈特（Shewhart）、CUSUM 和 EWMA 方案的直接应用。我们使用 CUSUM、EWMA 和 EWMA-CUSUM 多图方案来检测疾病发病率的变化。多图是将多个单图组合在一起，用于检测过程中的变化，并且已经证明它们具有优美的特性，即在检测过程中的变化时速度很快，并且计算成本较低。模拟结果表明，多 CUSUM 图在检测率参数变化方面比 EWMA 和 EWMA-CUSUM 多图更快。使用健康数据进行实际说明，以展示方案的效率。
