{
  "id": 13463,
  "url": "https://arxiv.org/abs/2607.21559v1",
  "title": "Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana",
  "summary": "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). T",
  "authors": "T. Ansah-Narh, Y. Asare Afrane",
  "category": "research",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T17:40:51.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13463",
  "original_url": "https://arxiv.org/abs/2607.21559v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}