Modeling and data analysis of obesity, insulin resistance, and diabetes

National Institute Of Diabetes And Digestive And Kidney Diseases

Investigators

Linked publications & trials

Abstract

For the past decade and a half I have been interested in developing mechanistic models of human metabolism and diseases including modeling how the body partitions the macronutrients of the diet into lean and fat body tissue, the action of insulin on lipolysis, and cortical spreading depression. These models involve fitting systems of differential equations of the mean dynamics of the various compartments to data using Bayesian inference methods. I have now turned these techniques towards modeling the COVID-19 pandemic. This involves population modeling of how the disease is progression and in particular trying to estimate the fraction of the population that is infected with SARS-COV-2 but not recorded as official cases using available epidemiological data. Our model estimated that the case ascertainment ratio (ratio of recorded cases to total infected) ranges from 5 to 10 for most regions and matches the subsequent serological measurements. I am also modifying a previously developed model for sepsis to model the time dependent progression of COVID-19 within individuals. The model considers the immune-inflammatory response to a novel infectious agent where none or only partial immune memory exists.