The module focuses on training students in the practical implementation of economic evaluations in healthcare. Skills in this area are in high demand, given that in many health systems (where services are free at the point of use) price signals cannot guide resource allocation. Instead, systematic and evidence-based approaches are required to determine how best to allocate limited resources to maximise social welfare.

The module will cover key methods of economic evaluation, with a strong emphasis on their application in practice rather than on methodological theory. Students will learn how to conduct trial-based and model-based evaluations, including techniques such as decision trees and Markov models. Additionally, they will gain experience in characterising uncertainty using tools, such as probabilistic sensitivity analysis.

The module will give students an understanding of the nature of routinely-collected NHS data, including ways of coding it (such as the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)). Furthermore, there will be an exploration of other relevant, routinely-collected data sets such as those held by the UK’s Office for National Statistics.

Such data is very important for health research as it often includes a far more representative cohort of the population than datasets collected for research, which ensures health equity can be explored. Alongside this, the module will cover relevant structures within the NHS with reference to data collection, coding and analysis as well as information governance and ethics. Students will also explore stakeholder engagement in the NHS including business intelligence and clinical staff.

The module will cover approaches to identifying research priorities in the NHS. Methodologically, students will learn about methods such as systematic review and meta-analysis and standard as well as advanced methods for data cleaning, preparation and handling routinely collected data to answer research questions related to population health (including methods for identifying missing data and data engineering, and state-of-the-art machine learning and AI tools).

You will gain the knowledge and skills to conduct and interpret epidemiological analysis on infectious disease secondary data. You will develop and fit mathematical transmission models to critically analyse outbreak data. Building from a foundational understanding of the biology and immunology of infections, students will learn how to develop transmission models appropriate to a specific research question or epidemiological application, and estimate key parameters through model fitting to data.

Students will also be introduced to network science and contact patterns. The students will also learn about key elements of infectious disease epidemiology, such as surveillance and testing, public health interventions and the use of models to inform policy, as well as large-scale spatiotemporal aspects of infectious disease dynamics.

Finally, students will learn how to effectively communicate and present epidemic model outputs and correctly interpret their findings, while appreciating the limitations of the approach.

This module provides and introduction to epidemiology with a strong focus on applied and quantitative topics. The key themes of the course will be the development and applications of regression methods, including generalised linear regression and survival analysis, to address epidemiological problems. All methods will be contextualised by considering some of the most used study designs in epidemiology.

You will begin by learning the principles of epidemiology by defining the notions of health outcomes, exposure and risk. This is followed by an introduction to metrics used to quantify disease in a population: incidence, prevalence, and their relationship. You will then be given an overview of epidemiological study design, including recruitment and sampling, biases: case-control studies, ecological and cohort studies. An important topic of the course will be how to draw inferences from epidemiological studies, defining association, causality and how they differ. In the lab sessions of the course, you will analyse real epidemiological data using R statistical programming language.

This module provides a hands-on introduction to computer science, software engineering principles, and scientific programming with a focus on applications in health data science. Students will develop practical coding skills in both R and Python, two key languages in the field.

Topics include a brief history of computing, modern computer architectures, and the role of scientific computing in ensuring reproducibility. In R, students will learn the fundamentals of programming—such as loops, functions, conditions, and data structures—and apply them to real-world data processing tasks involving both structured and unstructured data. The module also covers the creation of exploratory graphics, tables, and spatial data visualisations (maps) using R's powerful data and mapping packages. You will also explore reproducible reporting through dynamic documents with embedded code and graphics.

The Python component will introduce you to core programming concepts and the use of scientific notebooks. Students will gain experience with key numerical and visualisation libraries, and learn how to run basic machine learning models.

By the end of the module, students will be equipped with essential computational skills for working effectively with health data in a reproducible and insightful way.

You will learn to construct and interpret regression models for health data analysis, with a strong emphasis on real-world research applications. You will begin with exploratory data analysis, using visual tools to identify patterns and relationships.

From there, you will develop an understanding of linear and generalised linear models, including those based on Normal, Poisson, and Binomial distributions. Key topics include model assumptions, coefficient interpretation, performance assessment, and the use of residuals and diagnostics to evaluate model fit. You will also learn to apply both ordinary least squares and maximum likelihood methods for model fitting.

As the module progresses, you will explore advanced techniques such as random intercept and slope models, as well as strategies for addressing overdispersion in count data. The module is delivered through a combination of lectures and hands-on practical sessions in R, equipping you with the skills and confidence to apply statistical methods to real-world health data.

The module is centred on a dissertation project for the MSc in Health Data Science program. It involves conducting independent research focused on health-related data using data science techniques.

You will identify a relevant research question, design a study and perform data collection and analysis. The use of R programming is a key aspect, likely involving data manipulation, visualisation and statistical analysis. The module emphasises the application of statistical methods to draw meaningful conclusions from health data, as well as the ability to present these findings clearly and accurately in a dissertation format.

Ethical considerations and proper data management practices are also integral to the module, ensuring that you can handle sensitive health data responsibly.

Centred on a dissertation project for the MSc in Health Data Science program, this module involves conducting independent research focused on health-related data using data science techniques.

You will identify a relevant research question, design a study and perform data collection and analysis. The use of R programming is a key aspect, likely involving data manipulation, visualisation and statistical analysis. The module emphasises the application of statistical methods to draw meaningful conclusions from health data, as well as the ability to present these findings clearly and accurately in a dissertation format.

Ethical considerations and proper data management practices are also integral to the module, ensuring that you can handle sensitive health data responsibly.

In addition, this module includes participation in the summer school at the Pemba Public Health Laboratory, which focuses on epidemiology and the control of infectious diseases, particularly in low-resource settings. It covers the principles of epidemiology, including:

• The investigation of disease outbreaks
• Surveillance methods
• Strategies for prevention and control

The module likely addresses challenges specific to low-resource environments, such as limited access to healthcare infrastructure and data. Emphasis is placed on practical approaches to managing infectious diseases, possibly through case studies or field-based learning. The goal of this module is to equip you with the knowledge and skills needed to effectively monitor and control infectious diseases in challenging settings.