Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana
2026-07-23 • Artificial Intelligence
Artificial IntelligenceComputational Engineering, Finance, and ScienceEmerging Technologies
AI summaryⓘ
The authors used a special method to find unusual patterns in malaria cases across Ghana from 2014 to 2023. They discovered that some places, like Tamale, had very high malaria cases during these unusual times, while other places had frequent but smaller unusual events. This means that places with the most malaria aren't always the ones with the most surprising changes in malaria. Their work shows that just looking at how many cases happen doesn’t tell the whole story, and that tracking unusual changes can help better focus malaria control efforts.
malaria surveillanceanomaly detectiontransmission patternsspatial analysistemporal analysismalaria hotspotsepidemiologyGhanadisease burdenseasonality
Authors
T. Ansah-Narh, Y. Asare Afrane
Abstract
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.