Interventions and Calibration

This module covers approaches for modeling treatment and vaccination interventions in infectious diseases. Students learn to incorporate additional compartments in SIR models to represent intervention effects, understand leaky vaccines, and model different types of vaccine and treatment effects. The module includes hands-on practice with R programming for implementing these models.

This module focuses on the fundamental relationships between models and data, teaching students how to calibrate SIR models against real-world epidemic data. Through manual calibration exercises, students learn to adjust model parameters to match observed data, gaining practical understanding of model-data relationships.

This module introduces computer-based model calibration techniques, focusing on the least-squares approach. Students learn to construct and maximize goodness-of-fit functions, use computer algorithms for calibration, and implement these approaches in R programming.

This module covers advanced model calibration techniques, focusing on the maximum-likelihood approach. Students learn to construct likelihood functions, understand logarithmic transformations in modeling, and apply these concepts in R programming. The module culminates in a practical modeling project.