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R for Mathematical Modelling and Analysis of Infectious Disease

Artificial Intelligence(ai)

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Aim

This course teaches how to build, simulate, and interpret mathematical models of infectious diseases using R. Participants will learn to translate real epidemiological questions into compartment models (SIR/SEIR and extensions), estimate key parameters, simulate outbreaks, evaluate interventions (vaccination, isolation, behavior change), and communicate results clearly using plots and reproducible reports—ending with a mini modeling project using realistic data.

Program Objectives

Build Modeling Foundations: Understand compartment models, assumptions, and why models behave the way they do.
Implement Models in R: Write and run simulations using standard R workflows for ODE-based models.
Estimate Key Parameters: Learn how to interpret and estimate transmission rate, recovery rate, and R₀/Rt.
Evaluate Interventions: Model vaccination, quarantine, contact reduction, and seasonality effects.
Connect Models to Data: Fit simple models to incidence data and assess uncertainty.
Communicate Results: Create clear visuals and scenario summaries for public health decision-making.
Hands-on Outcome: Build an outbreak model and present a short scenario-based report.

Program Structure

Module 1: Infectious Disease Modeling — The Big Picture

What models are for: explanation, prediction, and planning (and their limits).
Basic epidemiology terms: incidence, prevalence, attack rate, case fatality (overview).
Core dynamics: transmission, recovery, immunity, and feedback effects.
Why assumptions matter: when models mislead and how to stay honest.

Module 2: SIR Model Fundamentals

SIR compartments: Susceptible–Infectious–Recovered.
Model equations (conceptual understanding) and interpretation of each term.
Key parameters: β (transmission), γ (recovery), and basic reproduction number R₀.
Hands-on: simulate an SIR outbreak and visualize curves in R.

Module 3: SEIR and Realistic Extensions

Adding exposed (latent) stage: when SEIR is needed.
Asymptomatic infections and under-reporting (conceptual extension).
Time-varying transmission: seasonality and behavior change.
Hands-on: simulate SEIR with time-varying contact rates.

Module 4: Implementing ODE Models in R

Setting up state variables, parameters, and time grids.
Solving differential equations in R (workflow approach).
Debugging models: checking conservation, sanity checks, and stability.
Reusable code structure: building a clean modeling function.

Module 5: Parameter Estimation and R₀/Rt Interpretation

Estimating β and γ from epidemic curves (simple practical approach).
R₀ vs Rt: what changes over time and why.
Model calibration basics: matching model outputs to observed incidence.
Uncertainty thinking: why one “best curve” is not enough.

Module 6: Intervention Modeling (Policy and Planning Scenarios)

Vaccination strategies: coverage, efficacy, and herd immunity concept.
Isolation/quarantine: reducing infectious contacts.
Non-pharmaceutical interventions: masking, distancing, school closure (as contact reduction).
Scenario comparison: trade-offs and how to communicate them responsibly.

Module 7: Data, Noise, and Real-World Complexity

Reporting delays, testing changes, and why data is messy.
Observation models: cases vs infections (conceptual mapping).
Basic sensitivity analysis: which parameters drive outcomes most.
Model limitations: what your model cannot claim.

Module 8: Visualization, Reporting, and Reproducible Workflows

Clear plots: incidence, cumulative cases, Rt trends, scenario comparisons.
Creating interpretable dashboards-style summaries (simple R outputs).
Reproducible reporting: RMarkdown/Quarto concept and clean outputs.
How to write a model report: assumptions, methods, results, limitations.

Final Project

Build an outbreak model (SIR/SEIR or an extension) for a chosen disease scenario.
Calibrate parameters using a sample incidence dataset (provided) and run 2–3 intervention scenarios.
Deliverables: code, plots, scenario comparison, and a short report with assumptions + limitations.
Example projects: vaccination impact simulation, school reopening scenarios, seasonal influenza vs baseline, outbreak control with isolation policies.

Participant Eligibility

UG/PG/PhD students in Public Health, Epidemiology, Biology, Statistics, Mathematics, or Data Science
Researchers and analysts working on disease surveillance and outbreak planning
Healthcare and public health professionals interested in modeling basics
Basic R knowledge is helpful (beginners can follow with guided practice)

Program Outcomes

Modeling Skills: Ability to build and simulate SIR/SEIR models in R.
Interpretation Confidence: Understand parameters, R₀/Rt, and what results mean in practice.
Scenario Planning: Ability to compare intervention impacts using simulation-based evidence.
Data-Model Connection: Basic ability to calibrate models and discuss uncertainty.
Portfolio Deliverable: A complete infectious disease modeling project in R.

Program Deliverables

Access to e-LMS: Full access to course materials, example datasets, and code templates.
R Code Templates: SIR/SEIR model scripts, scenario runner, plotting functions, report template.
Hands-on Exercises: Guided simulation tasks and parameter exploration activities.
Project Guidance: Mentor support for final project modeling and reporting.
Final Assessment: Certification after assignments + final project submission.
e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

Epidemiology Data Analyst (Entry-level)
Public Health Modeling Assistant
Biostatistics / Health Analytics Associate
Disease Surveillance & Reporting Associate
Research Assistant (Epidemiology / Mathematical Biology)

Job Opportunities

Public Health Agencies: Outbreak monitoring, scenario planning, and surveillance analytics.
Research Institutes & Universities: Epidemiology and infectious disease modeling labs.
Healthcare Organizations: Infection control analytics and hospital epidemiology support.
NGOs & Foundations: Health program planning, impact assessment, and disease response projects.
Healthtech Companies: Forecasting, surveillance tools, and population health analytics teams.

Category	E-LMS, E-LMS+Videos, E-LMS+Videos+Live

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Artificial Intelligence(ai)

R for Mathematical Modelling and Analysis of Infectious Disease

Aim

Program Objectives

Program Structure