Sama Shrestha, Department of Applied Mathematics, University of Colorado Boulder
Bayesian Inference in coupled epizootic disease models
An important aspect of epizootic disease transmission modeling is to infer the extent to which small-scale processes affect large-scale dynamics. Models that couple the disease dynamics at the population level with the host-pathogen dynamics at the within-host level can help gain new insights into the relationship between multiple scales. Empirical data collected from experiments conducted at these multiple scales can be used to make inference about the parameters in the coupled dynamical system, and quantify joint uncertainty about the coupled system. We present our approach to parameter inference and uncertainty quantification under a Bayesian framework which takes advantage of the separation of time scales (fast, slow) in the coupled system and a state-space representation of the models.
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David Bortz, Department of Applied Mathematics, University of Colorado Boulder
Mathematical Modeling of the Endocannabinoid System
In recent years, there has been significant interest in the design, impact, and efficacy of phytocannabinoids-based therapies on the human endocannabinoid (EC) system.Ìý However, despite significant clinical and anecdotal evidence in support of these therapies, there has been surprising little mathematical modeling of the system.Ìý In particular, while there has been substantial work on the impact of tetrahydrocannabinol, there are at least 112 other cannabinoids which have not received anywhere near the same level of study.Ìý We will present the current state of mathematical models for a subset of the EC system as well as some analysis of our preliminary mathematical model.