# Geospatial modelling for zoonotic disease hotspot identification within a One Health framework: a systematic review

## TL;DR

This paper reviews how geospatial modeling is used to identify zoonotic disease hotspots and finds that full integration of human, animal, and environmental factors remains limited.

## Contribution

The study provides the first systematic review of geospatial modeling applications in zoonotic disease hotspots through a One Health lens.

## Key findings

- Only 15.2% of studies fully integrated human, animal, and environmental domains in their geospatial models.
- Climatic variables were most commonly used, while socioecological and animal health variables were less consistently included.
- Publication output increased after 2020, with a geographic focus on Africa, Asia, and Europe.

## 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.

The online version contains supplementary material available at 10.1186/s42522-026-00194-8.