将共识异常检测框架应用于加纳 (2014-2023) 的每月疟疾监测数据，以识别非典型传播模式。异常在空间和时间上是高度结构化的。阿散蒂和北部地区是最常发生的异常,，其中塔马利,、库马西, 和阿克拉的持续热点。一个关键发现是异常期间的异常负担(累积病例)与异常频率(异常行为的持续性)之间的空间区别。玉米粉蒸肉在异常,期间负担最高，而最高异常率集中在阿散蒂地区,，这表明高负担地区不一定是异常传播最频繁的地区。与正常月份相比，异常月份形成了一个统计上不同的组,，病例数(Cohen的 $d = 3.252$) 更高，并且季节偏差较大 ($d > 1.2$)。仅疟疾负担就无法提供传播动态的完整图景。通过区分疟疾最流行的地方和传播行为最异常的地方,，该框架可以加强监测, 优先调查, 并支持有针对性的控制策略。
A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns. Anomalies were highly structured in space and time. Ashanti and Northern Regions accounted for most recurrent anomalies, with persistent hotspots at Tamale, Kumasi, and Accra. A key finding was the spatial distinction between anomaly burden (cumulative cases during anomalous periods) and anomaly frequency (persistence of unusual behaviour). Tamale had the highest burden during anomalies, whereas the highest anomaly rates clustered in Ashanti districts, showing that high-burden areas are not necessarily those with the most frequent anomalous transmission. Anomalous months formed a statistically distinct group, with much higher case counts (Cohen的 $d = 3.252$) and large seasonal deviations ($d > 1.2$) compared with normal months. Malaria burden alone provides an incomplete picture of transmission dynamics. By distinguishing where malaria is most prevalent from where transmission behaves most unusually, this framework can strengthen surveillance, prioritise investigations, and support targeted control strategies.
科目: 人工智能 (cs.AI); 计算工程, 金融, 和科学 (cs.CE); 新兴技术 (cs.ET); 应用程序 (stat.AP); 机器学习 (stat.ML)
Subjects: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Applications (stat.AP); Machine Learning (stat.ML)