MSc in Modelling for Global Health
The MSc in Modelling for Global Health is a full-time one-year taught programme that provides interdisciplinary content on modelling, health evidence to inform the modelling, and policy processes to inform decisions. Week-long modules are delivered through a mix of interactive practical sessions and lectures in person at Oxford. Demand for a skilled workforce in this field is on the rise and there is great potential for mathematical and economic modelling to help guide policy for national health systems and international policymaking.
The course will aim to develop your:
- Repertoire of skills in mathematical and economic modelling, scientific programming, global health financing and related cutting-edge bioinformatics and analytics
- Breadth of knowledge of current challenges and issues in global health
- Range of techniques and tools for communication with key stakeholders from policy, implementation, commercial and research sectors
The course provides various mechanisms to support your development as independent learners, team players, problem solvers and effective communicators. As a result, you are expected to be able to:
- Frame global health questions in modelling language
- Understand and accommodate the cultural, social, political and fiscal context of these questions
- Combine multiple techniques to answer such questions in the short, medium and long term
- Aim for, predict and report the impact of your work
- Be innovative and apply your skills to new settings or topics
- Operate and thrive in an interdisciplinary environment
The MSc in Modelling for Global Health will provide interdisciplinary content on:
- Health evidence to inform the modelling and
- Policy processes to inform decisions
You will complete a series of compulsory core modules in Michaelmas Term, which provide the required background knowledge. All students will be able to access the full range of optional modules offered in Hilary Term. More information on the course can be found on the University course webpage.
During your third term (Trinity Term), you will embark on an eight week funded placement that will involve participation in a global health project in (or related to) a resource limited setting.
You could be placed in a; field site, laboratories, international NGO/policymaking institutions, industrial setting or academic research groups.
The placement will call upon the skills and topics you will have covered during the first two terms of the course and will be linked to your final dissertation.
The course will encompass students with a spectrum of previous experience from recent graduates hoping to pursue a research career to experienced professionals seeking professional development. Recent graduates would most likely be looking to embark on a career in modelling in the global health space. This would also apply to those more experienced professionals from the STEM disciplines of mathematics, statistics, economics, computer science, engineering etc.
Graduates from this programme would be highly employable with demand for these skills spanning academia, industry, governmental, non- governmental and consulting sectors. For those experienced professionals from the bio-medical and health policy and governance fields, we would expect this training to support evidence-based policymaking to enhance their impact and professional development.
You should be able to demonstrate quantitative competence (evidenced by degree level qualifications in a relevant subject) and an interest in global health in resource limited contexts, either through previous study, research or other in situ professional experience. One of the key aims of the course is to build modelling capacity beyond groups already well-established in the global north, and with this in mind, we anticipate admitting a geographically diverse cohort. Additionally, with interdisciplinarity inherent within modelling, which combines mathematics, statistics and computing, we expect the cohort to bring together a mix of students reflecting these proficiencies.