发表机构
Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission; Department of Medical Microbiology, University of Ghana Medical School（加纳空间科学与技术研究所，加纳原子能委员会; 加纳大学医学院医学微生物学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
利用基于共识的异常检测框架分析加纳2014 - 2023年月度疟疾监测数据，发现时空异常特征及异常负担与频率的空间差异，能区分高发区与异常传播区，助力加强疟疾监测与防控。
AI 中文摘要
一个基于共识的异常检测框架应用于加纳2014 - 2023年的月度疟疾监测数据，以识别非典型传播模式。异常在时空上具有高度结构化特征。阿散蒂和北部地区异常情况最为频繁，塔马利、库马西和阿克拉存在持续热点。关键发现是异常负担（异常时期累计病例数）和异常频率（异常行为持续性）的空间差异。该框架通过区分疟疾高发区和传播异常区，可加强监测、优化调查并支持针对性控制策略。
英文摘要
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.
Comments32, 15 figures, under review at spatial and spatio-temporal epidemiology
