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Duration: 1 year full-time

ECTS: 90 credits



This course offers training in the methodology, design, conduct and interpretation of epidemiological studies on chronic and infectious diseases.

Epidemiology is the study of the distribution, causes, and possible prevention and control, of diseases in populations.

The MSc in Epidemiology offers training in the methodology, design, conduct and interpretation of epidemiological studies on chronic and infectious diseases.

It is particularly suitable for students who wish to acquire skills in epidemiology and biostatistics, and to get involved with research projects.


Study programme


In the first term, all students follow a common core pathway covering epidemiological methods, biostatistics, and infectious and chronic disease epidemiologyTerm two modules aim to reflect areas of emerging research as well as providing extended tutoring in core epidemiological and statistical concepts and skills, building upon the knowledge, insight and skills gained in term one.

The third term consists of a four-month research project carried out under supervision, possibly in collaboration with other universities and research institutions.




Upon completion of this course, students usually develop an academic career by beginning a PhD, or move on to work for public health organisations, pharmaceutical companies or non-governmental agencies.



Modules shown are for the 2019-20 academic year and are subject to change depending on your year of entry.

Please note that the curriculum of this course is currently being reviewed as part of a College-wide process to introduce a standardised modular structure. As a result, the content and assessment structures of this course may change for your year of entry. We therefore recommend that you check this course page before finalising your application and after submitting it as we will aim to update this page as soon as any changes are ratified by the College.

Find out more about the limited circumstances in which we may need to make changes to or in relation to our courses, the type of changes we may make and how we will tell you about changes we have made.


You take all of the core modules below.


Introduction to Infectious Disease Modelling


This module provides an overview of infectious disease modelling and develops skills in designing and analysing infectious disease models for public health policy. Students will be taught how to represent the characteristics of an infectious disease using a mathematical model, how to simulate that model using a computer, and how to analyse that model. Students will also learn how models have been applied in public health policy, and how models are designed to address a research question. 


Principles and Methods of Epidemiology


This module ensures students will be familiar with the core concepts of epidemiology and acquire the skills necessary to describe, analyse, interpret and appraise epidemiological studies. Further modules and projects require such knowledge, and a good grasp of these basics is thus essential for successful completion of the degree. 


Disease Masterclass


This module aims to provide students with an overview of core health challenges and lines of epidemiological research being undertaken, across a range of infectious and non-communicable diseases.    


Introduction to Statistical Thinking and Data Analysis


The aim of this module is to give students an understanding of the importance of statistical thinking in epidemiology, randomised trials and public health, to enable them to critically evaluate the results of standard statistical analyses published in journal articles and to carry out a range of statistical analyses using 

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. 








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.

Teaching and assessment


Teaching methods


  • Class tutorials
  • Computer-based practical workshops
  • Final research project (dissertation)
  • Formative and summative assessment via Blackboard e.g. in-class quizzes
  • Group work sessions
  • Group workshops and revision sessions
  • Lectures
  • Mentimeter
  • Seminars and practicals
  • Small group tutorials
  • Teaching materials published via Blackboard

Assessment methods


  • Articles and case study reviews
  • Computer based tests
  • Essays
  • Individual and group presentations
  • MCQs and online quizzes
  • Mini research project
  • Reports and paper reviews
  • Written examinations

Entry requirements


We welcome students from all over the world and consider all applicants on an individual basis.

Minimum academic requirement

Our minimum requirement is a 2.1 degree in mathematics, statistics, medicine (human or veterinary) or biological sciences.

International qualifications

We also accept a wide variety of international qualifications.

The academic requirement above is for applicants who hold or who are working towards a USA qualification.

For guidance see our Country Index though please note that the standards listed here are the minimum for entry to the College, and not specifically this Department.

If you have any questions about admissions and the standard required for the qualification you hold or are currently studying then please contact the relevant admissions team.

English language requirement (all applicants)

All candidates must demonstrate a minimum level of English language proficiency for admission to the College.

For admission to this course, you must achieve the standard College requirement in the appropriate English language qualification. For details of the minimum grades required to achieve this requirement, please see the English language requirements for postgraduate applicants.

How to apply



Making an application

All applicants to our Master’s courses must apply online.