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Geospatial modeling aids zoonotic disease hotspot identification, but fully integrating human, animal, and environmental data for One Health approaches remains challenging due to data limitations.

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Area of Science:

  • Public Health
  • Epidemiology
  • Geospatial Science

Background:

  • Zoonotic diseases present global public health challenges, necessitating integrated surveillance.
  • Geospatial modeling is crucial for identifying disease hotspots and informing One Health strategies.
  • Limited synthesis exists on how geospatial methods operationalize One Health principles.

Purpose of the Study:

  • To systematically review studies applying geospatial modeling to identify zoonotic disease hotspots.
  • To assess the integration of One Health principles within these geospatial approaches.
  • To identify limitations and future directions for geospatial modeling in zoonotic disease control.

Main Methods:

  • Systematic review following PRISMA 2021 guidelines (2000-2025).
  • Searched multiple databases for studies on geospatial modeling of zoonotic disease hotspots.
  • Extracted data on methods, variables, geography, and One Health integration; performed descriptive synthesis.

Main Results:

  • 46 studies met criteria, with increased publication post-2020, concentrated in Africa, Asia, and Europe.
  • Common methods included Bayesian spatial models, satellite imagery, machine learning, and ecological niche modeling.
  • Climatic variables were dominant predictors; socio-ecological and animal health variables were less integrated. Full One Health integration was found in only 15.2% of studies.

Conclusions:

  • Geospatial modeling is vital for zoonotic disease hotspot identification but faces constraints in fully operationalizing One Health.
  • Data fragmentation and uneven integration of human, animal, and environmental domains limit current approaches.
  • Enhancing integrated surveillance, including socio-ecological predictors, and standardizing methods are crucial for policy impact.