The University of Southampton

GEOG3055 The Geography of Infectious Diseases

Module Overview

To enable students to gain insights into the geography of infectious diseases through the application of advanced spatially explicit quantitative methods for characterising, mapping and modelling the elements of disease systems.

Aims and Objectives

Module Aims

• Appreciate the variety of disease systems that exist, especially in certain geographic areas (i.e. in the tropics), and their differing transmission mechanisms. • Understand how land cover, climate and other environmental variables constrain the distribution of diseases geographically as well as the distribution of vectors (often insects) that transmit some diseases. • Describe some of the spatially explicit quantitative approaches, especially regression, for mapping vector distributions and disease risks and burdens spatially. • Undertake simple statistical analyses and mapping of disease data to provide policy-relevant information. • Appreciate typical health systems and the data that they provide, and consider the challenges and barriers to accessing the services provided, particularly in rural areas of developing countries. • Understand how public health facility (PHF) data can be used to map disease incidence geographically. • Understand the basics of disease dynamics through population-level partial differential equations. • Understand the importance of human and disease mobility quantified through data such as from mobile phones and Twitter. • Understand the basics of agent-based modelling (ABM) as a means for characterising and simulating the disease system. • Discuss the concepts of “one health”, and the interface between health, poverty and development.


Worldwide they are the cause of 1/3 of all deaths, and more than half in developing countries. The burden of disease in those countries in the tropics has received significant attention over the past decade, with billions of pounds invested in research, monitoring and control programs from governments and donors such as the Bill and Melinda Gates Foundation (BMGF). Moreover, the establishment of the Millennium Development Goals and many other disease reduction targets has prompted an explosion in the field of health metrics, with more epidemiological data being collected, assembled and analysed nowadays than at any time in history. Such data, coupled with quantitative methods, are providing unprecedented insights into the epidemiology of disease in the tropics, challenging traditional theories and highlighting significant geographical variations. There exist a huge number of human and animal diseases in the tropics, from those causing significant rates of infections and deaths today, such as malaria, tuberculosis and HIV, to others, such as dengue fever, West Nile virus and chikungunya that have recently caused headlines through emergence in new areas. In 2001, 175 species of pathogens were classified as emerging or re-emerging in humans. According to the World Health Organization (WHO), zoonoses (i.e. diseases of humans originating from animals) represent about 75% of emerging human diseases. In addition, 60% of emerging pathogens have three or more host species in addition to humans and many of them involve insect, tick or rodent vectors. Hence, it is important to understand the geography of diseases, which can best be achieved through the application of quantitative geographical methods. This module aims to provide an introduction to the geographical methods for analysing disease systems. The methods introduced through this module include those which allow: (i) mapping of disease risk (as well as vector abundance) spatially through regression of disease outcomes on environmental covariates, (ii) handling of data from public health facilities (PHFs) (i.e., cases of disease) which when coupled with data on the geographical catchments of PHFs allow mapping of disease incidence, and (iii) simulation of disease dynamics both at the population level and in a spatially disaggregated way (i.e., creating a computer-based model of the real world within which diseases transmit between agents). General issues in relation to disease will be covered (e.g., what is the relation between poverty and disease?, how do interventions work?, how can the environment be protected simultaneously with improving animal and human health?). However, this module focuses on quantitative methods and will, thus, suit those students who have an interest in statistical and dynamic modelling in a health context. Although a range of diseases will be examined, there will be a focus on malaria and trypanosomiasis throughout. Computer labs will provide hands-on introductions to modern statistical methods for mapping and understanding the geography of diseases, using tools such as Google Earth, GIS and the R statistical programming language.

Learning and Teaching

Independent Study150
Total study time150



MethodPercentage contribution
Group presentation 20%
Group report 40%
Individual computer lab 40%


MethodPercentage contribution
Exam 100%

Linked modules

Pre-requisite: GEOG2010 Introductory Geographic Information Systems 2016-17

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