R for Mathematical Modelling and Analysis of Infectious Disease
Mastering R for Effective Mathematical Modeling and Analysis of Infectious Diseases
Skills you will gain:
This workshop focuses on utilizing R programming for mathematical modeling and analysis of infectious diseases. Participants will gain practical skills in applying R to model disease spread, analyze epidemiological data, and understand the dynamics of infectious disease outbreaks. With hands-on sessions, attendees will learn how to create simulations, interpret results, and explore real-world case studies, making this workshop ideal for researchers, public health professionals, and anyone interested in using data-driven approaches to combat infectious diseases.
Aim: This workshop teaches R programming for mathematical modeling and analysis of infectious diseases. Participants will gain hands-on experience in simulating disease spread and analyzing epidemiological data. Ideal for researchers and public health professionals aiming to apply data-driven approaches in infectious disease management.
Program Objectives:
- Introduction to R Programming for Epidemiology
Learn the basics of R programming and its application in infectious disease modeling. - Mathematical Modeling Techniques
Understand key mathematical models used to describe the spread of infectious diseases, including SIR and SEIR models. - Data Analysis and Interpretation
Analyze real-world epidemiological data using R, and interpret the results for better decision-making. - Simulation of Disease Spread
Gain hands-on experience in simulating disease transmission and testing different intervention strategies. - Model Validation and Calibration
Learn how to validate and calibrate models to ensure accuracy and reliability in predicting disease outcomes. - Application to Public Health
Apply the acquired knowledge to assess the impact of diseases and develop informed public health strategies.
What you will learn?
Week 1: Introduction to Infectious Disease Modeling
- Module 1: Overview of infectious disease models, epidemiology terms, and core dynamics.
- Module 2: SIR model fundamentals, key parameters, and hands-on simulation in R.
Week 2: Extending Models and Implementing in R
- Module 3: SEIR model and extensions like asymptomatic infections and time-varying transmission.
- Module 4: Implementing ODE models in R, solving differential equations, and debugging.
- Module 5: Estimating parameters, interpreting R₀/Rt, and model calibration basics.
Week 3: Intervention Strategies and Final Project
- Module 6: Intervention modeling: vaccination, isolation, and non-pharmaceutical strategies.
- Module 7: Data complexities, sensitivity analysis, and model limitations.
- Module 8: Visualization, reporting, and reproducible workflows in R.
Final Project
- Build and calibrate an outbreak model, run intervention scenarios, and submit a report.
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills