Infectious Disease Modelling And Application Training Course

Introduction

Infectious diseases are disorders caused by organisms such as bacteria, viruses, fungi, protozoa, helminths, prions or parasites and they include SARS-CoV-2, Zika, Ebola, HIV/AIDS, swine flu, MERS CoV, ringworm, trichinosis, influenza, rabies, measles, rubella, tuberculosis and malaria among others. With the increased emergence and re-emergence of these diseases, there has been equally increased use of mathematical modelling to support relevant infectious diseases stakeholders (public health, pharmaceutical industry professionals, policy makers, infectious diseases researchers) in understanding the transmission and control of these diseases. This is possible when professionals are capable of interpreting and effectively evaluating both epidemiological data and the findings of mathematical modelling studies. This course will equip participants with knowledge on infectious diseases and hands on skills on use of R studio software in mathematical modelling of infectious diseases. Participants will be equipped with the skills needed to create, analyze, and apply models to real-world infectious disease scenarios, enabling them to make informed decisions and develop effective intervention strategies.

Course Objectives

1. Understand Core Principles of Infectious Disease Modelling: Gain a solid foundation in the basic concepts and principles of infectious disease modelling.
2. Develop Mathematical Models: Learn to develop various types of mathematical models (deterministic, stochastic, agent-based, and network models) to simulate infectious disease spread.
3. Analyze Epidemiological Data: Master techniques for analyzing epidemiological data to inform model parameters and validate model outputs.
4. Assess Public Health Interventions: Evaluate the impact of public health interventions through model simulations to optimize disease control strategies.
5. Advanced Modelling Techniques: Explore and apply advanced modelling techniques to address complex epidemiological questions.
6. Communicate Results Effectively: Develop skills to effectively communicate modelling results and their implications to stakeholders, policymakers, and the public.
7. Implement Software Tools: Gain proficiency in using various software tools (e.g., R, Python, EpiModel) for modelling and data analysis.
8. Apply Models to Real-World Scenarios: Engage in practical applications and case studies to apply modelling techniques to real-world infectious disease scenarios.

Who Should Attend?

• Public Health Professionals: Epidemiologists, health policy makers, and public health officials involved in infectious disease control and prevention.
• Researchers and Academics: Researchers and students in epidemiology, public health, and related disciplines.
• Healthcare Providers: Medical professionals and healthcare providers interested in understanding disease dynamics and intervention impacts.
• Data Scientists and Statisticians: Professionals working with health data and involved in quantitative analysis and modelling.
• Government and NGO Officials: Decision-makers and advisors involved in public health policy and infectious disease management.

Course Outline

Model 1: Introduction to Infectious Disease Modelling

• Welcome and Course Overview
• Basics of Infectious Disease Epidemiology
• Types of Infectious Disease Models
• Historical Case Studies of Infectious Disease Modelling
• Key Concepts: R0, Transmission Rates, Herd Immunity
• Hands-on Session: Introduction to Modelling Software

Model 2: Fundamentals of Mathematical Modelling

• Introduction to Deterministic Models: SIR and SEIR Models
• Compartmental Models: Structure and Dynamics
• Mathematical Formulation of Basic Models
• Parameter Estimation and Model Fitting
• Hands-on Session: Building Basic Models in R/Python

Model 3: Data Analysis for Infectious Disease Modelling

• Epidemiological Data: Sources and Types
• Data Cleaning and Preprocessing
• Statistical Methods for Data Analysis
• Model Calibration Techniques
• Hands-on Session: Data Analysis and Parameter Estimation in R/Python

Model 4: Advanced Modelling Techniques - Stochastic Models

• Introduction to Stochastic Models
• Differences between Deterministic and Stochastic Models
• Building Stochastic SIR Models
• Hands-on Session: Developing Stochastic Models in R/Python
• Case Study: Application of Stochastic Models in Real-world Scenarios

Model 5: Advanced Modelling Techniques - Agent-Based Models

• Introduction to Agent-Based Modelling
• Key Concepts and Applications of Agent-Based Models
• Building Basic Agent-Based Models
• Hands-on Session: Developing Agent-Based Models in NetLogo/AnyLogic
• Case Study: Agent-Based Modelling of Disease Spread

Model 6: Network Models in Infectious Disease

• Introduction to Network Models
• Understanding Network Structure and Dynamics
• Building Network Models for Disease Spread
• Hands-on Session: Developing Network Models in R/Gephi
• Case Study: Network Modelling of Contact Tracing

Model 7: Public Health Interventions and Control Strategies

• Introduction to Public Health Interventions
• Modelling Vaccination Strategies
• Evaluating Social Distancing and Quarantine Measures
• Hands-on Session: Modelling Intervention Strategies in R/Python
• Case Study: Impact of Interventions on Disease Dynamics

Model 8: Advanced Data Analysis Techniques

• Advanced Statistical Methods for Infectious Disease Data
• Bayesian Inference and MCMC Methods
• Introduction to Survival Analysis
• Hands-on Session: Applying Advanced Data Analysis Techniques in R
• Case Study: Bayesian Modelling of Epidemics

Model 9: Real-time Modelling and Forecasting

• Introduction to Real-time Modelling
• Techniques for Short-term and Long-term Forecasting
• Using Time Series Analysis for Epidemic Forecasting
• Hands-on Session: Real-time Modelling and Forecasting in R
• Case Study: Forecasting COVID-19 Dynamics

Model 10: Economic Impact and Cost-Effectiveness Analysis

• Introduction to Health Economics and Cost-Effectiveness
• Modelling the Economic Impact of Infectious Diseases
• Evaluating Cost-Effectiveness of Interventions
• Hands-on Session: Economic Modelling in R/Python
• Case Study: Cost-Effectiveness Analysis of Vaccination Programs

Model 11: Modelling Emerging Infectious Diseases

• Challenges in Modelling Emerging Infectious Diseases
• Techniques for Rapid Response Modelling
• Case Studies: SARS, MERS, Ebola, Zika
• Hands-on Session: Modelling Emerging Infectious Diseases in R/Python

Model 12: Policy and Decision-Making

• Using Models for Policy Formulation and Decision-Making
• Risk Assessment and Management in Public Health
• Communicating Modelling Results to Stakeholders
• Hands-on Session: Policy Scenario Modelling in R/Python
• Case Study: Model-Informed Policy Making for Influenza

Model 13: Real-world Modelling Challenges

• Identification of Real-world Challenges in Infectious Disease Modelling
• Group Discussion and Brainstorming
• Group Projects and Presentations on Real-world Challenges
• Expert Feedback and Discussion

Model 14: Practical Applications and Case Studies

• Practical Applications of Modelling in Disease Control and Prevention
• Case Studies: Application of Modelling in Various Epidemics
• Hands-on Session: Applying Modelling Techniques to Case Studies

Model 15: Course Review, Future Directions, and Certification

• Review of Key Concepts and Techniques
• Emerging Trends in Infectious Disease Modelling
• Future Research and Application Areas
• Course Evaluation and Feedback
• Certification Ceremony

Methodology

• The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

Key Notes

• The participant must be conversant with English.
• Course duration is flexible and the contents can be modified to fit any number of days.
• One-year post-training support Consultation and Coaching provided after the course.