Aims and scope
The module will provide students with an understanding of the relevance of spatial statistical methods to the natural, social and health sciences. It will outline the application of the general statistical modelling paradigm (formulation, estimation, diagnostic checking, prediction, scientific interpretation) in the specific context of geostatistical methods. It will also convey the ability to use specialised R packages (eg geoR, PrevMap) to conduct geostatistical analyses.
Learning Outcomes
General
On successful completion of this module, students will be able to:
• explain how spatial statistical methods can help to understand scientific processes that exhibit spatial variation
• write a report that is accessible to statisticians and to subject-matter scientists
Specific
On successful completion of this module, students will be able to:
• recognise the essential characteristics of a geostatistical problem
• diagnose regression residuals for evidence of spatial correlation
• formulate and fit stochastic models to geostatistical data that show evidence of spatial correlation
• use these models to construct predictive maps of spatially varying phenomena
Module convenor: Mr Barry Rowlingson
Aims and scope
The purpose of this module is to bridge the gap between the computer science of representing, storing, and
handling geospatial data, and modern statistical algorithms designed to infer quantities of interest from spatial
datasets. On completion, students will be able to work flexibly with spatial data at the level of computer code,
going beyond the capabilities of GUI packages such as ArcGIS, and be able to construct bespoke pipelines to
analyse and display spatial analytic results. The knowledge, understanding, and skills offered by this module
are therefore essential to those working in all quantitative aspects of spatial data, from geography and geology
to medical sciences.
CHIC402 Computer Programming with R
Module convenor: Dr Frank Dondelinger
Timetable: Weeks 1–3
Wednesday 9am – 11am Computer lab
Wednesday 2pm – 5pm Computer lab
Aims and scope
The aim of this module is to give students an understanding of the R programming language, and how it can
aid them in their scientific work and research. There will be an emphasis on good practice, rather than an
enumeration of specific aspect of the programming language. Examples will be drawn from the biomedical
health sciences including epidemiology and genomics, and will have some emphasis on statistics and data
science.
CHIC403 Introduction to Translational Research in Global Health
Module convenor: D. Prof. Peter Diggle
Timetable: Weeks 1–3
Friday 10am – 5pm Seminar room
Aims and scope
The module will provide students with the knowledge, understanding and skills of how basic concepts are
developed into solutions resulting in the improvement of health and well-being of patients in resource poor
settings. These key principles can be applied to any solution in any setting and are therefore entirely
transferable.
CHIC401 Statistics and Scientific Method
Module convenor: D. Prof. Peter Diggle
Timetable: Weeks 1–3
Thursday 9am – 11am Seminar room
Thursday 2pm – 5pm Computer lab
Aims and scope
The aim of this module is to give students an understanding of how statistical method contributes to scentific
research. The emphasis will be on fundamental concepts rather than speciifc techniques. Examples will be
drawn largely from the biomedical and health sciences but the underlying ideas are generically applicable. The
module will also include a gentle introduction to using the R software environment for graphical presentation
and simple statistical calculations.
This module will use the rigorous analytical apparatus of economics to provide a social sciences view of important problems in global health for students with no economics background to complement their skills acquired in core and optional courses focussing on advanced quantitative methods.
Learning Outcomes
General
On successful completion of this module, students will be able to:
• communicate complex arguments with clarity and succinctness
• extract the essential features of complex systems to facilitate problem solving and decision making
• engage in policy debates as an informed critic
Specific
On successful completion of this module, students will be able to:
• apply microeconomic concepts to issues in global health
• assess the appropriateness of different economic evaluation methods for different contexts
• critically evaluate the theoretical literature and assess the quality of empirical studies in health economics
• understand the implications of theoretical and empirical work in health economics for global health and translational research
• critically appraise published economic evaluations