ArticleOne health outlook2026
Geospatial modelling for zoonotic disease hotspot identification within a One Health framework: a systematic review.
Article in One health outlook, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zoonotic diseases continue to pose significant public health threats worldwide, driven by complex interactions at the human-animal-environment interface. Geospatial modelling has emerged as a critical tool for identifying disease hotspots and supporting One Health-oriented surveillance and intervention strategies. However, a systematic synthesis of how geospatial approaches operationalize One Health principles remains limited. A systematic review was conducted following PRISMA 2021 guidelines to synthesise peer reviewed studies published between 2000 and 2025 that applied geospatial modelling to identify zoonotic disease hotspots. Multiple bibliographic databases were searched, and studies were screened using predefined inclusion criteria. Data were extracted on modelling approaches, predictor variables, geographic focus, and levels of One Health integration, followed by qualitative and quantitative descriptive synthesis. A total of 46 studies met the inclusion criteria. Publication output increased markedly after 2020, with studies concentrated in Africa, Asia, and Europe. Bayesian spatial models, satellite imagery-based analyses, machine learning methods, and ecological niche modelling were most frequently employed. Climatic variables dominated predictor selection, while socio ecological and animal health variables were less consistently integrated. Full integration of human, animal, and environmental domains was observed in only 15.2% of studies, with most exhibiting partial or implicit alignment with One Health principles. Data availability, quality, and spatial and temporal resolution were the most reported limitations. Geospatial modelling plays an increasingly important role in zoonotic disease hotspot identification, yet its capacity to operationalise One Health remains constrained by data fragmentation and uneven domain integration. Strengthening integrated surveillance systems, expanding socio ecological predictor inclusion, and promoting harmonised methodological standards are essential for enhancing the policy relevance and operational impact of geospatial approaches in zoonotic disease prevention and control.
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