Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana
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 anom...
| Authors | T. Ansah-Narh, Y. Asare Afrane |
|---|---|
| Primary category | cs.AI |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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