Novel Eigen space method for multiple Spatiotemporal rare diseases clusters detection: a case study of waterborne disease

Abstract The development of robust and efficient analytical tools for informed decision making, mainly in epidemiological contexts, remains a persistent challenge. This study presents an enhanced algorithm designed to accurately detect vulnerable spatiotemporal hotspots associated with unexpected di...

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Autori principali:	Muhammad Fayyaz, Alamgir, Sami Ullah, Hameed Ali, Abdulrahman Obaid Alshammari, Zeineb Klai, Bilal Himmat
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2025-10-01
Serie:	Scientific Reports
Soggetti:	Waterborne diseases Spatiotemporal cluster detection Multi-EigenSpot algorithm Singular value decomposition Rare event detection Epidemiological hotspot analysis
Accesso online:	https://doi.org/10.1038/s41598-025-21792-y
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