Training on Leveraging AI and Big Data for Predictive Outbreak Modeling

Artificial Intelligence (AI) and big data analytics are transforming how public health systems detect, predict, and respond to disease outbreaks. By integrating large-scale epidemiological, environmental, mobility, genomic, and social data, predictive outbreak modeling enables earlier intervention, smarter resource allocation, and improved emergency preparedness.

This course provides a practical and strategic framework for applying AI and big data technologies to predictive outbreak modeling and epidemic intelligence. It integrates public health analytics principles with modern machine learning approaches, aligning with emerging digital health and epidemic preparedness strategies promoted by the World Health Organization and global public health data initiatives.

Participants will learn how to build predictive models, integrate multi-source datasets, apply machine learning techniques, and interpret outbreak forecasts for operational decision-making. The course also covers ethical AI governance, data quality, surveillance integration, geospatial analytics, and real-time epidemic intelligence systems.

Through applied exercises, simulations, and case studies, participants develop the capability to transform complex datasets into actionable forecasting models that support proactive outbreak prevention and response.

Duration

10 Days

Who Should Attend

• Epidemiologists and disease surveillance specialists
• Public health data scientists and analysts
• Health informatics and digital health professionals
• AI and machine learning practitioners in healthcare
• Public health emergency managers and PHEOC staff
• Research institutions and academic professionals
• Policy makers involved in digital health and preparedness

Individual Impact

• Strengthen expertise in AI-driven epidemic intelligence
• Improve ability to analyze complex outbreak datasets
• Enhance skills in predictive analytics and forecasting
• Build capacity in machine learning applications for public health
• Increase effectiveness in data-driven outbreak preparedness and response

Organizational Impact

• Strengthen predictive surveillance and early warning systems
• Improve outbreak preparedness and response planning
• Enhance data-driven decision-making capabilities
• Reduce delays in identifying emerging health threats
• Strengthen digital transformation and innovation in public health systems

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

• Apply AI and machine learning techniques to outbreak prediction
• Integrate large-scale datasets for epidemic intelligence
• Design predictive models for disease spread and risk analysis
• Use geospatial and mobility data for outbreak forecasting
• Develop early warning and anomaly detection systems
• Interpret predictive analytics for public health decision-making
• Address ethical, legal, and governance issues in AI-driven surveillance
• Strengthen integration between predictive models and emergency response systems

Module 1: Foundations of AI and Big Data in Public Health

• Introduction to AI, machine learning, and big data concepts
• Applications of AI in disease surveillance and outbreak response
• Types of public health datasets and data ecosystems
• Overview of predictive outbreak modeling frameworks
• Exercise: Assess organizational data readiness
• Case Study: AI applications in epidemic prediction

Module 2: Epidemiological Data and Big Data Integration

• Sources of outbreak-related data
• Integrating epidemiological, laboratory, mobility, and environmental datasets
• Data interoperability and integration frameworks
• Managing structured and unstructured data
• Practical: Build a multi-source data architecture
• Case Study: Integrated epidemic intelligence systems

Module 3: Data Preparation, Quality, and Governance

• Data cleaning and preprocessing techniques
• Managing missing, inconsistent, and biased data
• Data governance, ethics, and privacy considerations
• Legal and regulatory frameworks for health data
• Exercise: Conduct a data quality assessment
• Case Study: Ethical challenges in AI surveillance systems

Module 4: Machine Learning for Outbreak Prediction

• Supervised and unsupervised learning methods
• Classification and forecasting models
• Time-series analysis for epidemic prediction
• Model selection and performance evaluation
• Practical: Develop a predictive outbreak model
• Case Study: Forecasting disease transmission patterns

Module 5: Geospatial Analytics and Mobility Modeling

• GIS and spatial epidemiology concepts
• Mapping outbreak hotspots and transmission clusters
• Population mobility and contact pattern analysis
• Environmental and climate data integration
• Exercise: Build a geospatial outbreak risk model
• Case Study: Mobility data during epidemic spread

Module 6: Early Warning Systems and Anomaly Detection

• Syndromic surveillance and signal detection
• AI-driven anomaly and trend detection systems
• Threshold modeling and alert generation
• Real-time surveillance dashboards
• Practical: Design an early warning system
• Case Study: AI-supported outbreak alerts

Module 7: Real-Time Epidemic Intelligence and Decision Support

• Translating analytics into operational intelligence
• Integrating predictive models into PHEOCs
• Risk assessment and scenario analysis
• Decision-support systems for emergency response
• Exercise: Develop a decision-support workflow
• Case Study: Data-driven response coordination

Module 8: Visualization, Communication, and Interpretation

• Data storytelling and visualization techniques
• Communicating uncertainty in predictive models
• Dashboard design and public health reporting
• Building trust in AI-generated insights
• Practical: Create a predictive analytics dashboard
• Case Study: Communicating outbreak forecasts

Module 9: Scaling AI Systems for Public Health Preparedness

• Infrastructure and cloud computing considerations
• Scaling predictive systems across regions
• Capacity building and workforce requirements
• Sustainability and operational integration
• Exercise: Develop an AI implementation roadmap
• Case Study: National digital surveillance transformation

Module 10: Simulation, Innovation, and Future Trends

• End-to-end outbreak forecasting simulation
• Evaluating predictive system performance
• Emerging AI technologies in epidemic intelligence
• Continuous learning and adaptive systems
• Capstone Exercise: Develop a predictive outbreak modeling strategy
• Case Study: Future directions in AI-driven public health surveillance