Modeling Oral Glucose Tolerance Test (OGTT) data and its Bayesian Inverse Problem

One common way to test for diabetes is the Oral Glucose Tolerance Test or OGTT. Most common methods for the analysis of the data on this test are wasteful of much of the information contained therein. We propose to model blood glucose during an OGTT using a compartmental dynamic model with a system of ODEs. Our model works well in describing most scenarios that occur during an OGTT considering only 4 parameters. Fitting the model to data is an inverse problem, which is suitable for Bayesian inference. Priors are specified and posterior inference results are shown using real data.