Event

LEVEL: Intermediate

TRACK: Methodological & Statistical Research

LENGTH: 4 Hours | Course runs 2 consecutive days, 2 hours each day

08:00AM – 10:00AM (PDT)

11:00AM – 1:00PM (EDT)

15:00PM – 17:00PM (UTC)

In this course, we will cover the steps and decisions involved in calibrating a mathematical model in R. We will begin the course with an overview of when model calibration is necessary and will introduce a general model calibration framework. We will then engage students in an extensive hands-on exercise where they will implement the calibration of a simple mathematical model in R using a simple random search. We will then introduce more advanced calibration approaches, including Latin hypercube sampling, directed search algorithms (eg, Nelder-Mead), Bayesian calibration, and other iterative calibration approaches (eg, genetic algorithms). We will discuss the tradeoffs of different calibration approaches and will identify scenarios when one approach may be more appropriate than others. Participants who wish to gain hands-on experience are required to bring their laptops with R and RStudio installed.

 

Faculty

Associate Professor

Division of Health Policy and Management

University of Minnesota School of Public Health

Minneapolis, MN, USA

 

Department of Health Policy, School of Medicine, and

Stanford Health Policy, Freeman-Spogli Institute for International Studies

Stanford University

Stanford, CA, USA

 

Basic Schedule:
4 Hours | Course runs 2 consecutive days, 2 hours each day