Training on Mastering Real-Time Bio-Surveillance and Epidemic Intelligence

Timely detection of public health threats is the cornerstone of effective outbreak prevention and response. Real-time bio-surveillance and epidemic intelligence enable health systems to identify signals early, analyze risks rapidly, and act decisively before crises escalate.

This course provides a comprehensive, practice-oriented framework for building and managing real-time surveillance and epidemic intelligence systems. It aligns with global approaches from the World Health Organization and the Centers for Disease Control and Prevention, integrating indicator-based surveillance (IBS) and event-based surveillance (EBS) into a unified intelligence model.

Participants will learn how to design surveillance architectures, integrate multi-source data streams, apply analytics for early warning, and operationalize epidemic intelligence within Public Health Emergency Operations Centers (PHEOCs). The course also covers digital tools, data governance, risk communication, and decision-support systems.

Through simulations, real-world datasets, and applied exercises, participants develop the capability to transform raw data into actionable intelligence that drives rapid and coordinated public health action.

Duration

10 Days

Who Should Attend

• Epidemiologists and surveillance officers
• Public health data analysts and informatics specialists
• PHEOC staff and emergency response coordinators
• Laboratory and diagnostic services professionals
• Health information system managers
• NGO and humanitarian health program staff
• Policy makers involved in disease surveillance and response

Individual Impact

• Strengthen expertise in epidemic intelligence and surveillance systems
• Improve ability to detect and analyze public health threats early
• Enhance skills in data analytics and interpretation
• Build capacity in real-time decision-support systems
• Increase effectiveness in outbreak detection and response

Organizational Impact

• Strengthen national and sub-national surveillance systems
• Improve early warning and rapid response capacity
• Enhance data-driven decision-making in public health
• Reduce delays in outbreak detection and reporting
• Strengthen integration of surveillance with emergency response systems

By the end of this course, participants will be able to:

• Design and implement real-time bio-surveillance systems
• Integrate indicator-based and event-based surveillance approaches
• Analyze multi-source data for early outbreak detection
• Apply epidemic intelligence methodologies for risk assessment
• Develop early warning and alert systems
• Use digital tools and dashboards for real-time monitoring
• Strengthen data governance, quality, and interoperability
• Translate surveillance data into actionable public health decisions

Module 1: Foundations of Bio-Surveillance and Epidemic Intelligence

• Concepts and evolution of disease surveillance
• Indicator-based vs event-based surveillance systems
• Epidemic intelligence frameworks and workflows
• Global standards and reporting requirements
• Exercise: Map national surveillance systems
• Case Study: Early detection of emerging outbreaks

Module 2: Surveillance System Design and Architecture

• Designing integrated surveillance systems
• Data sources: health facilities, laboratories, communities, media
• System interoperability and data integration
• Digital platforms and infrastructure
• Practical: Design a surveillance architecture
• Case Study: National surveillance system integration

Module 3: Data Collection, Management, and Quality Assurance

• Data collection tools and protocols
• Data validation, cleaning, and standardization
• Managing large and real-time datasets
• Ensuring data quality and completeness
• Exercise: Data quality assessment
• Case Study: Challenges in surveillance data management

Module 4: Analytics and Early Warning Systems

• Statistical and algorithmic approaches to outbreak detection
• Thresholds, alerts, and anomaly detection
• Predictive analytics and modeling basics
• Visualization and dashboards
• Practical: Develop an early warning model
• Case Study: Detecting outbreaks using real-time data

Module 5: Event-Based Surveillance and Signal Verification

• Monitoring informal data sources (media, community reports)
• Signal detection and triage
• Verification and risk assessment processes
• Integrating EBS with formal surveillance systems
• Exercise: Signal verification workflow
• Case Study: Event-based detection of outbreaks

Module 6: Laboratory Integration and Genomic Surveillance

• Role of laboratories in surveillance systems
• Linking lab data with epidemiological data
• Genomic surveillance and pathogen tracking
• Data sharing and reporting mechanisms
• Practical: Design lab-surveillance integration
• Case Study: Genomic tracking of disease spread

Module 7: Information Systems and Digital Tools

• Health information systems and interoperability
• Use of mobile and cloud-based surveillance tools
• Real-time dashboards and reporting platforms
• Data security and system resilience
• Exercise: Build a digital surveillance dashboard
• Case Study: Digital transformation in surveillance

Module 8: Epidemic Intelligence in PHEOCs

• Integrating surveillance with emergency operations centers
• Information flow and decision-support systems
• Situation reports and briefing products
• Coordination with response teams
• Practical: Develop an epidemic intelligence workflow
• Case Study: PHEOC-driven outbreak response

Module 9: Risk Communication and Decision-Making

• Communicating risks to decision-makers and the public
• Translating data into actionable insights
• Managing uncertainty and incomplete data
• Ethical considerations in surveillance
• Exercise: Develop a risk communication brief
• Case Study: Communication during outbreaks

Module 10: Simulation and Applied Epidemic Intelligence

• End-to-end outbreak detection and response simulation
• Multi-source data analysis in real time
• Decision-making under pressure
• After-action reviews and system improvement
• Capstone Exercise: Design a national real-time bio-surveillance system
• Case Study: Lessons learned from global epidemics