Infectious Disease Modelling: Applied Methods in R

Programme details

This course begins on the 13 Jan 2027 which is when course materials are made available to students. Students should study these materials in advance of the first live meeting which will be held on 20 Jan 2027, 3:30-4:30pm (UK time).

Week 1: Introduction

• Installing R and RStudio
• Using tidyverse
• Vectors, matrices and data frames
• Writing functions

Week 2: Analysing epidemic data

• Summarising trends
• Visualising data using ggplot2
• Estimating the growth rate and reproduction number, R

Week 3: Epidemic models

• Building a transmission model
• The SI model
• The SIR model

Week 4: Solving models in R

• Using deSolve
• The SI model
• The SIR model

Week 5: Stochastic simulations

• Stochastic vs. deterministic
• Distribution functions in R
• Simulating an epidemic

Week 6: Communicating uncertainty

• Summarising simulation results
• Plotting uncertainty

Week 7: Modelling interventions

• Vaccination
• Mass treatment
• Social distancing
• Comparing interventions

Week 8: Individual-based modelling

• Individual-based SIR model
• Modelling an epidemic on a square lattice

Week 9: Fitting to data

• Fitting methods in R
• Fitting vs. testing
• Making predictions

Week 10: Interfacing science and policy

• Transparency and reproducibility in science
• Communicating assumptions and uncertainty
• Examples from the COVID-19 pandemic

Level and demands

This course is open to all and no prior knowledge is required.

This course is offered at FHEQ Level 4 (i.e. first year undergraduate level), and you will be expected to engage in independent study in preparation for your assignments and for the weekly webinar. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class. Our 10-week Short Online Courses come with an expected total commitment of 100 study hours, including those spent in live webinars.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link: https://www.conted.ox.ac.uk/about/english-language-requirements

Course aims

The course provides a foundation in R programming for infectious disease modelling.

• Students will use the R language to construct and analyse models of infectious disease transmission.
• Students will build and run model simulations and forecast population-level disease outcomes.

IT requirements

Any standard web browser can be used to access course materials on our virtual learning environment, but we recommend Google Chrome. We also recommend that students join the live webinars on Microsoft Teams using a laptop or desktop computer rather than a phone or tablet due to the limited functionality of the app on these devices.

Programme details

This course begins on the 13 Jan 2027 which is when course materials are made available to students. Students should study these materials in advance of the first live meeting which will be held on 20 Jan 2027, 3:30-4:30pm (UK time).

Week 1: Introduction

• Installing R and RStudio
• Using tidyverse
• Vectors, matrices and data frames
• Writing functions

Week 2: Analysing epidemic data

• Summarising trends
• Visualising data using ggplot2
• Estimating the growth rate and reproduction number, R

Week 3: Epidemic models

• Building a transmission model
• The SI model
• The SIR model

Week 4: Solving models in R

• Using deSolve
• The SI model
• The SIR model

Week 5: Stochastic simulations

• Stochastic vs. deterministic
• Distribution functions in R
• Simulating an epidemic

Week 6: Communicating uncertainty

• Summarising simulation results
• Plotting uncertainty

Week 7: Modelling interventions

• Vaccination
• Mass treatment
• Social distancing
• Comparing interventions

Week 8: Individual-based modelling

• Individual-based SIR model
• Modelling an epidemic on a square lattice

Week 9: Fitting to data

• Fitting methods in R
• Fitting vs. testing
• Making predictions

Week 10: Interfacing science and policy

• Transparency and reproducibility in science
• Communicating assumptions and uncertainty
• Examples from the COVID-19 pandemic

Teaching methods

This course takes place over 10 weeks, with a weekly learning schedule and weekly live webinar held on Microsoft Teams. Shortly before a course commences, students are provided with access to an online virtual learning environment, which houses the course content, including video lectures, complemented by readings or other study materials. Any standard web browser can be used to access these materials, but we recommend Google Chrome. Working through these materials over the course of the week will prepare students for a weekly 1-hour live webinar you will share with your expert tutor and fellow students. All courses are structured to amount to 100 study hours, so that on average, you should set aside 10 hours a week for study. Although the course finishes after 10 weeks, all learning materials remain available to all students for 12 months after the course has finished.

All courses are led by an expert tutor. Tutors guide students through the course materials as part of the live interactions during the weekly webinars. Tutors will also provide individualised feedback on your assignments. All online courses are taught in small student cohorts so that you and your peers will form a mutually supportive and vibrant learning community for the duration of the course. You will learn from your fellow students as well as from your tutor, and they will learn from you.

Learning outcomes

By the end of the course students will be expected to:

• be able to use R to solve, simulate, analyse and visualise basic models of infectious disease transmission;
• understand the difference between mean-field and stochastic models;
• have a basic understand of methods of fitting models to data;
• be able to effectively communicate uncertainty in their results and understand how this uncertainty can impact policy decisions.

Assessment methods

You will be set independent formative and summative work for this course. Formative work will be submitted for informal assessment and feedback from your tutor, but has no impact on your final grade. The summative work will be formally assessed as pass or fail.

Yumi Naito

Assessment methods

You will be set independent formative and summative work for this course. Formative work will be submitted for informal assessment and feedback from your tutor, but has no impact on your final grade. The summative work will be formally assessed as pass or fail.

Level and demands

This course is open to all and no prior knowledge is required.

This course is offered at FHEQ Level 4 (i.e. first year undergraduate level), and you will be expected to engage in independent study in preparation for your assignments and for the weekly webinar. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class. Our 10-week Short Online Courses come with an expected total commitment of 100 study hours, including those spent in live webinars.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link: https://www.conted.ox.ac.uk/about/english-language-requirements

Fees

Module code: O26P728COZ

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