R for Mathematical Modelling and Analysis of Infectious Disease

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.

• 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.

• 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.

• Proficiency in R Programming: Participants will become proficient in using R for modeling and analyzing infectious diseases.
• Ability to Apply Mathematical Models: Attendees will gain the ability to apply mathematical models like SIR and SEIR to real-world epidemiological scenarios.
• Enhanced Data Interpretation Skills: Participants will develop strong skills in analyzing and interpreting epidemiological data for effective decision-making.
• Practical Experience in Disease Simulation: Participants will acquire hands-on experience in simulating the spread of infectious diseases and evaluating various intervention strategies.
• Improved Understanding of Model Calibration: Attendees will understand how to validate, calibrate, and improve the accuracy of disease models.
• Contribution to Public Health Solutions: Participants will be equipped to use data-driven modeling to contribute to public health decision-making and strategy formulation.