Session VI Effective early detection surveillance requires that every incursion of an exotic disease, or emergence of a novel disease is detected early enough to be affordably and rapidly controlled and eradicated. To achieve this, the entire animal population must be under constant surveillance, and significant or unusual disease events promptly and effectively investigated. Even in high-income, but especially in low- and middle-income countries, government animal health services alone are not able to meet these stringent demands. Private sector participation in disease surveillance is required, but currently levels of active participation and collaboration with government surveillance efforts vary greatly between production sectors. This presentation is based on some of the work of the 5-year USAID-funded TRANSFORM project being undertaken in Vietnam and Indonesia, which aims to develop sustainable market-driven risk mitigation approaches for transboundary animal diseases, including through improved early detection capacity.

1. Sustainable market-driven
early disease detection
Angus Cameron

2. How much surveillance is enough?
• Disease present
• Measure occurrence
• Case finding
• Disease absent
• Demonstrate freedom
• Early detection

4. 𝐸𝐷𝑆𝑆𝑒 = 𝐶𝑝 × 𝐶𝑡 × 𝑆𝑒𝑑

5. Target sensitivity
100%

6. Probability theory

7. Conclusion
• Early detection surveillance requirements
• Whole population under surveillance
• All the time
• Using a sensitive detection system

9. TRANSFORM
Transformational Strategies for
Farm Output Risk Mitigation

10. Objective
• Research project
• Implement effective disease risk mitigation
• Transboundary animal diseases
• Zoonoses
• Antimicrobial resistance
• Market-driven sustainability
• Consortium approaches
• Policy, biosecurity, training, nutrition …
• Ausvet’s component
• Use of data to achieve market-driven sustainability

11. Theoretical analysis
Need Comprehensive, full-time, real- time
disease event capture
Data source Producers / farmers
Challenges Motivation – why record events?
Budget – who pays?
Technology – how to manage the data?

12. Motivation
• Benefits
• Costs
• Risks

13. Benefits
• Farm-level data management and analysis service
• Improved farm-level decision-making
• Improved profitability
• Industry wide data integration and analysis service
• Benchmarking
• Research
• Surveillance
• Early warning
• Endemic threats
• Market access
• Policy development
• Production forecasting

14. Cost and risks
• Costs
• Very low cost or free to user (data generators)
• Simple and intuitive
• Privacy and security
• Secure data integration (not data sharing)
• Shared de-identified outputs of analysis
• Ownership
• Data generators retain ownership and control
• Governance
• Collaborative decision-making
• What data
• What analyses
• Who can access results

15. Budget
• Information  decisions  profit
• Stakeholder classification
• Data providers (producers) and consumers
• Incur cost (data capture)
• Reap benefits (better management)
• Data consumers
• Input providers (feed, pharmaceuticals, genetics)
• Large integrated producers
• Government
• Others
• Business model: paid subscriptions to data streams
• Sustainable funding for data management
• Funding for improved surveillance

16. Data management
• Cloud servers, big data management tools
• Highly secure
• High capacity, scalable
• Cost effective
• Low management overhead
• Flexible generic epidemiological data management
platform
• Omnísyan

17. Ruminants
Fieldworks
Aquatics
Pondworks
…

21. Conclusion
• Example of “Big Surveillance”
• Creation and use of big data for surveillance
• Market-driven approach
• Address issues with donor-funded interventions, and
competing demands on government budgets
• If the approach provides a demonstrably good return
on investment, it is worth funding
• Exchange of value
• Data from some stakeholders benefits others
• Investment by data beneficiaries supports data
providers