Abstract

Accurate parameter estimation and validation are fundamental for developing reliable epidemic models that guide public health policies.  This is a series of  six-hour talks which provide a comprehensive overview of epidemic modeling, focusing on parameter estimation and validation techniques.  The talks begin with an introduction to classical mathematical models of epidemics (SIR, SEIR, and their extensions) and their role in understanding disease dynamics.  It then explores real-life scenarios and modeling of epidemics, highlighting how data-driven approaches improve predictions. A key component includes mathematical simulations of epidemic models using MATLAB, demonstrating numerical techniques for solving differential equations.

Participants will learn model validation techniques with real data, including parameter estimation using the Least Squares method.  Additionally, the talks cover sensitivity analysis of parameters, emphasizing their impact on disease spread. Special attention is given to the estimation of the basic reproduction number (R0) and the effective reproduction number (Rt) from data and assessing the risk of infection in various outbreak scenarios.  Practical case studies will illustrate these concepts, providing participants with the skills to build, analyze, and validate epidemic models effectively.

 

Expected Participants

Undergraduate and graduate students, early career PhD researchers, and professionals in epidemiology, public health, and mathematical modeling.

 

Basic Skills of Participants

Fundamental knowledge of differential equations, mathematical modeling, and public health, along with familiarity with MATLAB.

 

Basic Needs of Participants

A computer with MATLAB installed and basic proficiency in handling datasets and simulations.

 

Organizers: Feng-Bin Wang (CGU), Chang-Hong Wu (NYCU) , Chang-Yuan Cheng (NKNU)