Training Course on Infectious Disease Modelling And Application Training Course

Module 1: Introduction to Infectious Disease Modelling
Foundations of Infectious Disease Modelling
Definition, purpose, and scope of infectious disease models.
Historical examples and lessons from past outbreaks.
Applications of Modelling in Public Health
Guiding intervention strategies, outbreak preparedness, and resource allocation.
Evaluating policy decisions using model predictions.
Types of Models
Deterministic vs. stochastic models.
Compartmental vs. agent-based vs. network models.
Activity: Discuss real-world examples of outbreak modelling and identify how modelling informed public health responses.

Module 2: Basic Epidemiological Concepts for Modelling
Key Epidemiological Measures
Incidence, prevalence, attack rates, and reproductive number (R0).
Transmission Dynamics
Infection periods, latency, and immunity.
Contact patterns and their role in disease spread.
Population Structure
Age-stratified, geographic, and social network considerations in modelling.
Workshop: Calculate epidemiological parameters using sample outbreak datasets and interpret results for modelling purposes.

Module 3: Data Collection and Preparation for Modelling
Data Sources for Modelling
Surveillance systems, hospital records, lab results, demographic and mobility data.
Data Cleaning and Validation
Handling missing data, inconsistencies, and biases.
Ethical and Privacy Considerations
Data protection and informed consent.
Exercise: Prepare a real or simulated dataset for modelling, perform data cleaning, and ensure quality standards are met.

Module 4: Deterministic and Compartmental Models
SIR, SEIR, and Variations
Model structure, assumptions, and parameter definitions.
Disease Spread Simulations
Predicting outbreak trajectories, peak infections, and herd immunity thresholds.
Intervention Impact Modelling
Evaluating vaccination, quarantine, and social distancing strategies.
Simulation: Build and run a compartmental model using sample data and interpret outcomes.

Module 5: Stochastic and Agent-Based Models
Stochastic Models
Introducing randomness and variability into disease spread.
Simulating rare events and outbreak uncertainty.
Agent-Based Modelling (ABM)
Modelling individual behaviours, interactions, and heterogeneity.
Capturing complex dynamics in populations.
Exercise: Implement a simple stochastic or agent-based model and observe differences from deterministic results.

Module 6: Model Calibration, Validation, and Sensitivity Analysis
Parameter Estimation
Using outbreak data to fit model parameters.
Model Validation
Comparing model outputs with observed data or historical outbreaks.
Sensitivity Analysis
Identifying parameters that most influence model outcomes.
Workshop: Calibrate a model with real or simulated data, validate results, and perform sensitivity analysis to guide interventions.

Module 7: Predictive Modelling and Scenario Planning
Forecasting Disease Spread
Short-term and long-term projections.
Scenario Analysis
Evaluating outcomes under different intervention strategies (e.g., vaccination coverage, social distancing).
Resource Planning
Using predictive models to allocate healthcare resources and plan outbreak response.
Exercise: Create multiple scenarios for a hypothetical outbreak and analyze projected outcomes.

Module 8: Software Tools for Infectious Disease Modelling
Introduction to Modelling Software
R (EpiModel, deSolve), Python (PyRoss, SimPy), AnyLogic, NetLogo.
Hands-On Training
Loading datasets, coding models, running simulations, and generating outputs.
Visualization of Results
Graphs, heat maps, dashboards, and interactive outputs for stakeholders.
Lab: Build and run models using software tools, visualize outputs, and interpret results.

Module 9: Communication of Model Results and Decision Support
Translating Models to Actionable Insights
Communicating uncertainty, limitations, and confidence intervals.
Stakeholder Engagement
Preparing reports, dashboards, and presentations for policymakers and health managers.
Evidence-Based Decision Making
Integrating model findings into public health strategies and outbreak response plans.
Simulation: Prepare a policy brief or presentation summarizing model findings for decision-makers.

Module 10: Capstone Project: Applied Infectious Disease Modelling
Integrated Project Design
Participants select a real or hypothetical outbreak scenario.
Develop a complete model: data preparation, model selection, calibration, validation, scenario planning, and output visualization.
Presentation and Peer Review
Present findings and recommendations for interventions.
Receive feedback for refinement and application in real-world contexts.
Actionable Public Health Planning
Translate model outcomes into operational response strategies and preparedness plans.
Capstone Project: Develop a comprehensive infectious disease model for outbreak prediction, intervention evaluation, and decision support.