Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana· 加纳疟疾时空发病率的无监督共识异常检测
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 count
无监督异常检测揭示加纳疟疾高发区及时空模式。
- 核心方法
- 应用无监督共识异常检测框架分析月度疟疾监控数据，该框架能够识别高结构化的时空异常模式。
- 适合谁读
- 研究者、公共卫生政策制定者
- 要解决的问题
- 如何识别加纳2014-2023年间疟疾病例数据中的时空异常传播模式。
- 关键实验
- 使用2014-2023年的月度疟疾病例数据进行实验，识别出Ashanti和Northern地区为主要的异常高发区，Tamale、Kumasi和Accra为持续热点。
- 主要贡献
- 揭示了异常负担与异常频率之间的空间差异，为疟疾防控提供新的视角。
- 意义与局限
- 研究结果有助于更好地理解疟疾的时空传播特性，指导公共卫生资源的分配。但模型可能无法完全捕捉所有复杂的社会经济因素对疟疾传播的影响。