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Veterinary syndromic surveillance: Current initiatives and potential for development
Fernanda C Dórea1, Javier Sanchez, Crawford W Revie
1Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE, C1A 4P3, Canada. fdorea@upei.ca
Veterinary syndromic surveillance systems are advancing for animal and public health, aligning with One Medicine principles. Key challenges include data integration and real-time decision support for disease control.
Area of Science:
- Veterinary epidemiology
- Public health surveillance
- One Medicine
Background:
- Syndromic surveillance systems are crucial for early detection of animal disease outbreaks.
- Recent developments focus on integrating animal health data for broader public health insights.
- The
- One Medicine
- concept emphasizes the interconnectedness of animal, human, and environmental health.
Purpose of the Study:
- To review recent advancements in veterinary syndromic surveillance systems.
- To identify systems focused on outbreak detection using systematic monitoring of animal population data.
- To explore the evolution of veterinary syndromic surveillance towards public health applications.
Main Methods:
- A comprehensive search of peer-reviewed and grey literature was conducted.
- Systems were identified based on their explicit focus on outbreak detection and systematic monitoring.
- The review analyzed the progress, data sources, challenges, and future directions of these systems.
Main Results:
- Developments in veterinary syndromic surveillance extend beyond animal health to include animal sentinels for public health.
- Clinical and laboratory data are primary information sources, with exploration of additional data streams.
- Challenges include data availability, classification, integration, and connecting surveillance outputs to decision support systems.
Conclusions:
- Diagnostic laboratories offer the most accessible data for veterinary syndromic surveillance currently.
- Consolidating data standards and integrating animal and public health surveillance systems are critical next steps.
- Effective disease management and control depend on linking systematic monitoring outputs to real-time decision support.
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