用共识法识别加纳疟疾异常传播，发现高发区与异常频发区不重合。
Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana
- 基于共识的无监督框架分析月度疟疾数据，识别时空异常模式。
- 坦马累异常负担最高，阿散蒂地区异常频率最密集，二者不一致。
- 异常月病例数显著偏高（d=3.252），适合疫情监测与精准防控。
针对2014-2023年加纳的月度疟疾监测数据，应用共识异常检测框架识别非典型传播模式。异常在时空上呈现高度结构性，阿散蒂和北部地区频繁出现异常，持续热点集中在塔马莱、库马西和阿克拉。关键发现是：异常负担（异常期累计病例）与异常频率（异常行为持续性）存在空间差异——塔马莱异常负担最高，而阿散蒂地区异常频率最高，表明高负担区未必是异常最频繁区。异常月份在统计上独立于正常月份，病例数显著更高（Cohen's $d = 3.252$），季节偏差大（$d > 1.2$）。仅靠疟疾负担无法完整刻画传播动态。该框架通过区分传播普遍区域与行为异常区域，可增强监测能力，优化调查优先级，支持靶向干预策略。
原文摘要 · Abstract (English)
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's $d = 3.252$) 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.
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