Module 1: Introduction to Epidemiological Modeling and R

• The role of mathematical models in infectious disease control and policy
• Key concepts: Infection, Recovery, Immunity, and the Basic Reproduction Number ()
• Introduction to the R environment, RStudio, and essential packages for modeling
• Review of data types and structures in R for epidemiological data
• Practical session: Setting up the R environment and basic data handling for incidence and vaccination records

Module 2: Fundamentals of Deterministic Compartmental Models

• Deriving and understanding the Susceptible-Infectious-Recovered (SIR) model
• Extending to the Susceptible-Exposed-Infectious-Recovered (SEIR) model for latent periods
• Defining and interpreting the system of Ordinary Differential Equations (ODEs)
• Graphical analysis of model outputs: phase planes and equilibrium points
• Practical session: Calculating the Basic Reproduction Number () for various simple compartmental models

Module 3: Implementing SIR/SEIR Models in R

• Numerical integration of ODEs using the deSolve package in R
• Structuring R code for models: defining parameters, initial conditions, and the ODE function
• Visualizing and interpreting the time-series output of deterministic models (incidence curves)
• Model modification: Incorporating vital dynamics (births and deaths)
• Practical session: Building a fully functional SEIR model in R and simulating the impact of varying transmissibility

Module 4: Parameter Estimation and Model Calibration

• Introduction to Bayesian and Frequentist approaches for parameter estimation
• Using Least Squares and Maximum Likelihood Estimation (MLE) for model fitting to incidence data
• Understanding and minimizing the difference between simulated and observed data (cost functions)
• Techniques for likelihood maximization and goodness-of-fit testing in R
• Practical session: Calibrating an SEIR model to synthetic incidence data to estimate the infection rate ()

Module 5: Modeling the Impact of Vaccination

• Incorporating constant vaccination coverage into compartmental models
• Modeling different vaccine characteristics: Leaky vs. All-or-Nothing efficacy
• Analyzing the concept of Herd Immunity Threshold and its dependence on 
• Modeling waning immunity and the need for booster doses
• Practical session: Simulating the required vaccination coverage to eliminate a VPD and plotting the reduction in incidence

Module 6: Advanced Model Structures and Dynamics

• Developing Age-Structured Models using matrices and coupled ODE systems
• Modeling the dynamics of Maternal Immunity and its impact on infant vaccination
• Incorporating spatial components and metapopulation models (brief overview)
• Modeling non-pharmaceutical interventions (NPIs) like social distancing
• Practical session: Modifying the SEIR model to include two distinct age groups and simulating targeted vaccination

Module 7: Stochastic Modeling and Outbreak Simulation

• When to use stochastic models: accounting for demographic and environmental noise
• Introduction to the Gillespie Algorithm (Stochastic Simulation Algorithm)
• Implementing the Gillespie algorithm for SIR/SEIR models using R packages
• Analyzing the probability and size distribution of minor and major outbreaks
• Practical session: Running multiple stochastic simulations for a small population and calculating the probability of disease extinction

Module 8: Policy Analysis and Economic Evaluation

• Using models to evaluate the effectiveness of different catch-up and routine vaccination strategies
• Introduction to Health Economic Evaluation: Cost-Effectiveness Analysis (CEA)
• Calculating DALYs (Disability-Adjusted Life Years) or QALYs (Quality-Adjusted Life Years) in the model context
• Optimizing the vaccination schedule based on effectiveness and cost criteria
• Practical session: Performing a basic Cost-Effectiveness Analysis to compare two different vaccination schedules

Module 9: Global Sensitivity and Uncertainty Analysis

• Distinguishing between Parameter Uncertainty (input) and Model Uncertainty (structure)
• Local vs. Global Sensitivity Analysis methods (e.g., Latin Hypercube Sampling, Partial Rank Correlation Coefficient)
• Implementing sensitivity analysis in R to identify the most influential parameters
• Visualizing uncertainty using prediction intervals from Monte Carlo simulation
• Practical session: Conducting a comprehensive Global Sensitivity Analysis on the SEIR model to determine parameter influence on peak incidence

Module 10: Case Studies and Advanced Modeling in R

• Detailed case study: Modeling a specific VPD (e.g., Measles or Rubella) with real data
• Overview of advanced R modeling frameworks (e.g., pomp, specialized Bayesian packages)
• Best practices for model validation, reporting, and publication
• Ethical considerations and transparency in epidemiological modeling
• Practical session: Reviewing and critically assessing a published VPD modeling study and replicating key findings in R

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588