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One Health aims to balance and optimise the health of people, animals, and ecosystems. It involves multiple disciplines including ecology, climate science, genomics, epidemiology, public health and social science. This multidisciplinary approach provides access to many types of data, approaches, and analytical tools to explore the factors affecting health. However, it also poses challenges in integrating different approaches and leveraging the strengths of each discipline.
The module will introduce the importance of systems thinking to understand complex One Health systems including ecological, social and economic factors, and the interactions between components of the system. The students will then learn the key forms of data used within each of the disciplines of epidemiology, genomics, ecology and climate science, and public health, including interview and other qualitative assessments. Students will gain an understanding of how these data are collected, the limitations and biases associated with them, the ethics of using them, and the scales and resolutionsÌýat which they are most often available. Students will then learn about analytical approaches used across these disciplines, particularly phylogenetics, epidemiological modelling, spatiotemporal statistical modelling, including machine learning. Crucially, the ways in which these data types and analytical approaches can be combined into a multidisciplinary approach is a key element of the module as well as methods to assess and integrate the role of climate change into modelling approaches. Real-world data is provided to students to analyse in open-source programming environments such as R or Python. The course encourages students to combine different analytical approaches to tackle complex health challenges, such as zoonotic disease transmission, mental health, air and water quality, food insecurity, and climate-related health risks and vulnerabilities.

Key concepts of this module will include:

• Data types and data collection approaches from across disciplines
• Interview techniques and other qualitative data collection methods
• Study design and biases
• Challenges and ethical considerations of working with personal data • An introduction to key methods from across disciplines, the quality of the evidence provided and the practicalities of various methods.
• Epidemiological modelling of disease
• Ecological modelling of disease
• Integration of climate modelling into other methods
• How approaches from across disciplines can be combined for a One Health approach

The aims of the module are to:

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1. Provide students with knowledge of the range of theory, data, and analytical approaches required to tackle complex global health challenges using a One Health approach.
2. Enable students to understand the assumptions and limitations of the different disciplinary approaches and how these might influence modelled outcomes.
3. Provide students with an understanding of the ethical challenges in collecting and analysing health data.
4. Support students in the practical application of these data and analytical approaches using open-source coding environments including R and Python.

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By the end of this module students should be able to:

• DISCUSS specific applications of analytical tools to real world problems and data.
• SUMMARISE the limitations and assumptions of the approaches discussed.
• EXPLAIN how tools from specific disciplines can be linked to gain a more holistic understanding of complex global health challenges.
• DESIGN analytical approaches suited to available data.
• CONSIDER the ethics of specific practical application of health and personal data.