BioMathematics Short Course

Speaker: Ben Swallow, School of Mathematics and Statistics, University of St Andrews

Short Course

13th December | 9:30-12:30 and 14:00-16:00 (with coffee-break) | Room Samsung, Building I

15th December | 10:00-13:00 | Room Samsung, Building I

Title: Sensitivity, uncertainty and inference in mathematical models of infectious diseases.

Abstract: Models of infectious disease dynamics are frequently based on differential equations, ordinary or stochastic in nature, and based on parameters that govern important epidemiological, ecological and environmental processes. Often, these parameters will be unknown a-priori, however they are vitally important in understanding the system of interest. In particular, knowing how or when to intervene in will vary depending on the sensitivity of the system to those processes. In this course, we will study statistical approaches to studying systems of this type, with particular focus on compartmental models of infectious diseases. We will begin by studying approaches for determining sensitivity in the system to the parameters the models are based on, and then progress to some methods for estimating those parameters from observable data. The course will be a mix of taught lectures with considerable hands-on computational practials in R to develop computational expertise that can be easily applied to other model formulations or systems of interest.

Registration form for the short course: https://forms.gle/5wwwJuc4BPqnp8M38

Participants to have access to their own laptop with R/Rstudio installed.

Short Bio: Ben Swallow is currently a Lecturer (Research and Education) in Statistics, School of Mathematics and Statistics, University of St Andrews. His research interests lie largely in Bayesian statistical inference, particularly Markov chain Monte Carlo (MCMC) methods, including model selection with reversible jump MCMC. BS is also interested in inference for stochastic dynamical systems of biological and environmental processes, such as those found in population ecology, cell-signalling and infectious diseases. These are often represented using state-space models and variants. Additionally, BS worked extensively in models for continuous data with many exact zeros and integrating multivariate data from varying sources and supports, which incorporate those interests above. Finally, BS has interests in real-world applications of the above spanning biological and ecological sciences. His work frequently involves collaborations with domain experts and data collectors in these fields.