MSc Epidemiology

You choose six optional modules from below.

 

 

 

Bayesian Statistics

 

 

 

The module introduces the students to the concepts of Bayesian models and inference commonly used in Biostatistics. Throughout the entire module several examples from epidemiology, social science, and clinical trials will be introduced to complement the theory. The students will also become familiar with the software package OpenBUGS, which will be used to implement the Bayesian models presented. 

 

 

 

Spatial Analysis

 

 

 

This module will introduce students to the main statistical methods used in spatial epidemiology and provide them with the theoretical and practical skills to analyse and interpret geo-referenced health data.

 

 

 

Molecular and Genetic Epidemiology

 

 

 

This module provides students with a fundamental understanding of the core concepts of molecular and genetic epidemiology and the application of molecular and genetic epidemiologic findings to public health and translational medicine. The module will encompass the appropriate study design for molecular and genetic epidemiologic investigations, biomarker development and the integration of biomarkers into epidemiologic studies, and the application of new and emerging molecular technologies in epidemiologic research.

 

 

 

Genetics of Infectious Disease Pathogens

 

 

 

The aim of this module is to introduce students to both the theoretical concepts and the practical methodology used in the genetic epidemiology of infectious pathogens. The module content includes pathogen population genetics, phylogenetics, phylodynamics and antimicrobial resistance. 

 

 

 

Further Methods in Infectious Disease Modelling

 

 

 

The module aims to provide students with the ability to interpret key evidence generated by modern infectious disease modelling methods that appears in non-specialist high impact journals. By the end of the course, students will also be able to design, execute and interpret results from streamlined versions of those same models. Although students will be given the opportunity to implement complex models using mathematical techniques and basic programming tools, they will not be expected to independently generate results from novel complex models. 

 

 

 

Outbreaks

 

 

 

Building on the skills gained by students in earlier modules (including the prerequisite module Further Methods in Infectious Disease Modelling), this module aims to give a broad and “real-life” view of using epidemiological research (specifically mathematical models and statistical analysis) to address key public health questions relating to the control of outbreaks. The module includes important contemporary topics such as the broader ecological context of infectious disease emergence and transmission, as well as state-of-the-art techniques required to calibrate mathematical models and make the best use of data. 

 

 

 

Advanced Regression

 

 

 

This module will allow students to become familiar with the principles of advanced regression for high-dimensional data so that they are able to apply such techniques on real data problems (e.g. complex omics data). In particular, students will learn how to perform advanced statistical analyses, including penalised likelihood and nonparametric regression models using R. 

 

 

 

Advanced Topics in Biostatistics

 

 

 

The module will introduce some of the issues faced while analysing complex datasets in advanced epidemiology. From these illustrations, methodological developments to address the resulting technical/computational challenges will be described, assessed and compared. Each of these established methods will be associated with a practical session during which students will implement the method.

 

In Term 3, individual research projects are carried out under supervision.

It is possible that the projects may be carried out in collaboration with other universities and research institutions, but this is not common and not always recommended.

Projects are expected to take four months of full-time study, with one (or more) member(s) of Imperial academic staff assigned to advise and monitor students. There may also be external supervisors.