Why CoMo?
The COVID-19 pandemic has highlighted the importance of pandemic preparedness and demonstrated that academia can deliver highly transformative interventions. The International Modelling Consortium (CoMo) was created in March 2020 by researchers at the University of Oxford, together with academic colleagues at Cornell University, and partners with infectious disease modellers and other public health experts from more than 40 countries across Africa, Asia and South and North America. Using a participatory approach, CoMo provides decision-making support to policymakers, using evidence from epidemiological and economic models adapted to each country’s context. With COVID-19 now endemic, CoMo continues to work with modellers and public health researchers on the challenges of a range of infectious diseases. To prepare for future pandemic challenges, CoMo members have highlighted the need for accessible training in health policy-facing modelling.
We have developed a programme of training on policy-facing public health modelling for a global audience. The overarching purpose is to widen global access and build modelling capacity in in-country settings. The benefits of this include improving pandemic preparedness, particularly in low- and middle-income countries. During the COVID-19 pandemic, modelling expertise was concentrated in high-income countries and CoMo was the only international community that supported LMIC leadership in the field. Also, of key importance is that this programme provides equitable access which aligns with a larger inter-divisional initiative, Global Equitable Oxford (with support from the Vice Chancellor’s Office), which aims to improve inclusivity and access to Oxford University expertise and connect with world-class researchers across the globe.
Who is the programme designed for?
Our courses for 2024 are aimed at entry level modellers, those who are exploring a career in modelling, or those in related professions who wish to know more about the discipline.
The courses are designed for those working in a range of sectors. For example, the public sector - medical professionals, data scientists, epidemiologists, health economists, medical statisticians; NGO staff; academic researchers; and those working in the private sector, the pharmaceutical and consulting industry.
Future courses will offer intermediate and advanced skills for mid-career modellers or related professionals who wish to build on foundational training and/or extend their technical repertoire. We can also provide bespoke versions of this course for organisations. Please contact us if you would like to discuss this opportunity further.
Courses
Introduction to R for Modellers
Tutor: Bo Gao
Course Details
Overview
This course provides an introduction to the basic elements of the R programming language with emphasis on its application in the context of infectious disease modelling. The materials covered in this course underpin the technical aspect of the training programme. R is a popular programming language in the research community especially that of disease modelling. It is open source and freely available cross platforms. On top of the rich statistical computing package library it already has, R also enjoys the support of a vibrant open-source community of researchers and developers who are constantly contributing to enrich the ecosystem around R. Packages such as ggplot and shiny give R users a unique advantage when it comes to communicating research outputs with a wider audience by making it incredibly easy to make graphical representations of data and publish them online.
In this course, we will take the students through key aspects of the R programming language starting from basic notations of the language to the introduction of popular data manipulation and visualisation packages. The course concludes with advanced topics such as code profiling and optimisation. We will give examples of mathematical models during the course to give the students a hands-on experience in model building.
Teaching will take place online and it is the responsibility of each student to ensure that they have suitable computing and AV (e.g. headphones and mic) equipment, internet bandwidth, and relevant software and packages installed prior to the start of the course. Demonstrations will be carried out on the Windows operating system and it is recommended that students also work on the Windows operating system.
Learning Outcomes
By the end of the course, students will be able to:
- Read and write a programming script written in R
- Install and load commonly used packages in the R ecosystem
- Import, manipulate, analyse, and visualise data sets in R
- Implement simple mathematical models in R
Schedule
Tuesdays and Fridays at 09:00 to 12:00, UK time, from 23 April 2024 to 24 May 2024.
Infectious Disease Modelling in Context: Introduction
Tutors: Ricardo Águas, Lisa White, Nathaniel Hupert
Course Details
Overview
This course provides a practical introduction to the mathematical modelling of infectious diseases. Mathematical modelling is being used with increasing frequency and scope to address questions about infectious diseases both in terms of basic science and in terms of national and international policymaking. Mathematical modellers arrive at the discipline from many backgrounds ranging from epidemiology to economics. Mathematical modelling is an interdisciplinary activity which brings together elements from mathematics, computer science, economics, demographics, virology, and immunology.
Throughout the course we will guide the students through the process of building a model suited to address a research/policy question, developing, and implementing the model in mathematical and code forms, and interpreting the short- and long-term dynamics produced. Modelling can improve global health only as far as it impacts policy. But to do so, modellers need to be as effective at communicating their models as they are at making them. This course will include sessions on the critical steps needed to communicate both the model-building process and output of mathematical modelling to specialists and non-specialists alike.
Teaching Approach
In this course, the students will be supported to develop a set of basic mathematical modelling skills though a mix of practical sessions interspersed with theory sessions. They will be given a thorough introduction to the core concepts and techniques that will permeate sessions throughout the course. This foundation will be solidified through key working examples and put into practice in R. Students will be exposed to a repository of different model structures that could be used as a template for future modelling exercises.
Teaching will take place online and it is the responsibility of each student to ensure that they have suitable computing and AV (e.g. headphones and mic) equipment, internet bandwidth, and relevant software and packages installed prior to the start of the course. Demonstrations will be carried out on the Windows operating system and it is recommended that students also work on the Windows operating system.
Learning Outcomes
By the end of the course, students will be able to:
- Write a simple mathematical model that is appropriate for a specific infectious disease and related research/policy question
- Understand how critical transmission related parameters, like the reproduction number, affect infectious disease dynamics
- Critically examine the short- and long-term dynamics of a model
- Compare and contrast different model outputs to evaluate the effectiveness of different public health interventions
- Understand key recent advances in knowledge mobilization in science generally and related to mathematical modelling of infectious diseases in particular
Schedule
Fridays at 09:00 to 12:00 & 13:00 to 16:00, UK time, from 07 June 2024 to 05 July 2024.
Infectious Disease Modelling in Context: Intermediate
Tutors: Ricardo Águas, Lisa White, Nathaniel Hupert
Course Details
Prerequisites
This course is suitable for students who meet the following prerequisites and knowledge:
- Basic programming skills, similar to the level reached on completion of our “Introduction to R for Modellers” course
- Basic knowledge of infectious disease modelling, similar to the level reached on completion of the course on “Infectious Disease Modelling in Context: Introduction” course
Overview
To effectively inform public health policy making, modelling must be, at minimum, fit-for-purpose, context conscientious, ethical, and actionable. This course will focus on how you - the modeller - can ensure that your work meets these three essential criteria. Exploring complex compartmental models and beyond, participants will be exposed to a repertoire of modelling approaches, and guided on how to select the most suitable model for each future question they may encounter. The course will also address the practicalities of fitting models to data, giving students an overview of the approaches available and an indication of options for further advanced study. In line with the course’s policy focus, various modelling communication challenges, particularly those pertaining to policy questions, will be explored along with workshopping ways to meet these challenges.
Teaching Approach
In this course, the students will be supported to extend their basic mathematical modelling skills though a mix of practical sessions interspersed with theory sessions. Moving on from simple compartmental models, students will be exposed, through practical sessions, to a repository of different model typologies that could be used as a template for any future modelling exercise. Taught sessions, practical exercises, roleplays, and other immersive activities will illustrate how translational considerations need to be addressed at every stage in the conceptualization, development, and presentation of mathematical modelling projects. The basic concepts of model fitting and uncertainty will be introduced through a mix of taught and practical sessions.
Teaching will take place online and it is the responsibility of each student to ensure that they have suitable computing and AV (e.g. headphones and mic) equipment, internet bandwidth, and relevant software and packages installed prior to the start of the course. Demonstrations will be carried out on the Windows operating system and it is recommended that students also work on the Windows operating system.
Learning Outcomes
By the end of the course, students will be able to:
- Understand the limits of compartmental models in addressing sources of heterogeneity and how to best address them
- Understand what is meant by the term “knowledge mobilization” especially in relation to mathematical modelling of infectious diseases
- Be able to describe examples of institutional, political, and/or logistical obstacles to the implementation of the output of mathematical models of infectious diseases
- Understand at least two strategies for integrating multiple model results into policy advice
- Describe maximum likelihood and Bayesian approaches to fitting models to data
Schedule
Fridays at 09:00 to 12:00 & 13:00 to 16:00, UK time, from 19 July 2024 to 16 August 2024.
Faculty
Ricardo Águas
Associate Professor of Modelling and Epidemiology
University of Oxford
Tracy Evans
Programme Manager
University of Oxford
Nathaniel Hupert
Associate Professor of Population Health Sciences and of Medicine
Weill Cornell Medicine
Bo Gao
Research Associate in Mathematical Modelling
University of Oxford
Lisa White
Professor of Modelling and Epidemiology
University of Oxford