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

Comprehensive Summary

This systematic review examines how geospatial modelling has been used to identify zoonotic disease hotspots within a One Health framework. Nyengere et al. conducted a PRISMA-guided systematic review of peer-reviewed studies published between 2000 and 2025, searching multiple databases and set criteria for inclusion, screening, and quality evaluation. 46 studies met the inclusion criteria, with publication frequency rates increasing after 2020 and most studies concentrated in Africa, Asia, and Europe. Bayesian spatial models, machine learning, satellite-based analyses, and ecological niche modelling were commonly used. Specifically, climate-related factors were included most often, while socio-ecological and animal health factors were included less consistently. Furthermore, only 15.2% of studies demonstrated full integration of human, animal, and environmental domains. The review concludes that although geospatial modelling plays a growing role in hotspot identification, it is still limited by disconnected data systems and incomplete integration across human, animal, and environmental sectors, highlighting the need for better coordination of surveillance systems and more consistent research methods.

Outcomes and Implications

This research is important because many communities, especially in parts of Africa, continue to experience repeated outbreaks of zoonotic disease. Infections such as anthrax, brucellosis, and Rift Valley Fever are often influenced by environmental conditions, livestock practices, land use changes, wildlife movement, and underlying social and economic challenges, placing rural populations at a higher risk. Since these factors overlap and influence one another in particular locations, identifying where risk is concentrated is important for timely and coordinated response efforts. Understanding the location and drivers of disease hotspots supports earlier detection, more targeted prevention strategies, and more effective use of limited public health resources. These findings can help improve how we track and prepare for disease outbreaks. If environmental and animal health data are better combined with human health data in spatial models, clinicians and public health teams could receive earlier warnings about disease transmission. This could help guide decisions about vaccination campaigns, vector control efforts, and resource allocation before cases rise. It also encourages stronger collaboration between physicians, veterinarians, and environmental health professionals, which is essential for preventing and managing zoonotic diseases.