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Viral Infections and Outbreaks Research / Zoonotic Diseases and Public Health · Journal article
Zoonotic Diseases · September 7, 2026
A consensus or society position rather than new primary data.
This is a conceptual guidance paper proposing the One Health Intelligence Maturity Model (OHIMM) and an operational framework for integrating multisectoral surveillance data (human, animal, wildlife, environmental, climatic, genomic, socioeconomic) to improve zoonotic disease preparedness. The authors illustrate the framework using West Nile Virus and Rift Valley Fever examples but do not present empirical validation, outcome data, or performance metrics; the work is intended to support future implementation research rather than provide evidence of effectiveness.
Journal article. Systems-level framework applicable to zoonotic disease surveillance and preparedness across human, animal, and environmental health sectors.
Proposes One Health Intelligence Systems (OHIS) framework integrating multisectoral information from human, animal, wildlife, environmental, climatic, laboratory, genomic, and socioeconomic domains Identifies fragmented data systems, limited interoperability, and challenges in translating multisectoral information into actionable intelligence as key constraints on One Health implementation Illustrates framework application using West Nile Virus surveillance in Europe and Rift Valley Fever surveillance in Africa as case examples
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Public health and veterinary professionals engaged in disease surveillance and preparedness should consider this framework as guidance for integrating multisectoral data sources; however, the framework remains unvalidated and requires implementation research to demonstrate effectiveness in improving outbreak detection, warning times, or response outcomes.
A conceptual framework and maturity model for implementing One Health Intelligence systems; provides operational guidance for integrated zoonotic disease preparedness without empirical outcome data.
Public health and veterinary professionals engaged in disease surveillance and preparedness should consider this framework as guidance for integrating multisectoral data sources; however, the framework remains unvalidated and requires implementation research to demonstrate effectiveness in improving outbreak detection, warning times, or response outcomes.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
The increasing frequency and complexity of zoonotic disease outbreaks highlight the limitations of traditional surveillance systems operating within specific disciplines. Although the One Health approach provides a widely accepted framework for addressing interconnected human, animal, and environmental health threats, its practical implementation remains constrained by fragmented data systems, limited interoperability, and challenges in translating multisectoral information into timely, actionable intelligence. Advances in artificial intelligence, geospatial analytics, Earth observation, climate monitoring, genomic epidemiology, and digital health technologies provide new opportunities to strengthen integrated preparedness and anticipatory decision-making. Building on emerging international One Health Intelligence (OHI) initiatives, this perspective presents an operational framework for implementing One Health Intelligence Systems (OHIS) and proposes the One Health Intelligence Maturity Model (OHIMM) as a conceptual tool to support the progressive development and assessment of intelligence capabilities. The framework integrates multisectoral information from human, animal, wildlife, environmental, climatic, laboratory, genomic, and socioeconomic domains to support risk assessment, early warning, preparedness, and response at the human–animal–environment interface. Using West Nile Virus in Europe and Rift Valley Fever in Africa as illustrative examples, we demonstrate how existing surveillance and multidisciplinary intelligence components can be integrated to strengthen situational awareness and anticipatory public health action while discussing the governance, interoperability, and implementation challenges that remain. We argue that operationalizing OHI through structured implementation frameworks and maturity assessment can support the development of more predictive, adaptive, and resilient systems for zoonotic disease preparedness while providing a foundation for future validation and implementation research.
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