Infectious Disease Modelling: Applied Methods in R



Overview

Infectious disease modelling is a growing field and can provide valuable insights into the spread and control of infectious diseases.

By fitting models of disease transmission and recovery to data, we can evaluate potential interventions and scenarios through fixed metrics, such as the basic reproduction number (or R number), or by comparing forward predictions using the outcomes of stochastic simulations.

This course provides an introduction to implementing and summarising models of infectious disease in the statistical programming language R, with a particular focus on modelling for policy and the importance of communicating uncertainty. 

No previous experience with modelling or using R is required. 

Programme details

Courses starts: 17 Jan 2024

Week 0: Course orientation

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

Digital Certification

To complete the course and receive a certificate, you will be required to attend and participate in at least 80% of the live sessions on the course and pass your final assignment. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so.

Fees

Description Costs
Course Fee £280.00
Take this course for CATS points £10.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutor

Dr Emma Davis

Dr Emma Davis is an infectious disease epidemiologist and mathematical modeller, with a particular interest in the low prevalence dynamics that occur around the emergence of new outbreaks and the elimination of transmission.

She trained as a mathematician, with a PhD in mathematical modelling of neglected tropical diseases from the University of Warwick. Her work with the Neglected Tropical Disease Modelling Consortium and the JUNIPER Consortium has interfaced with global and UK health policy, including involvement with SPI-M (the modelling subgroup of UK policy advisory group SAGE) during the COVID-19 pandemic. She received a Rapid Assistive Modelling for the Pandemic Early Career Investigator Award from the Royal Society for her modelling work around COVID-19 contact tracing and isolation adherence.

She has experience teaching and mentoring both undergraduate and postgraduate students in mathematics, data science, infectious disease modelling and biomedical sciences. Aside from formal teaching, she is passionate about outreach, winning 1st place at the Smith Institute Take AIM Awards for articulating the impact of mathematics in 2018 and is the recipient of a Royal Society Outreach Innovation Award for developing popular science YouTube channel EpiWithEmma.

Webpage: www.emmalouisedavis.co.uk

Publications: https://scholar.google.com/citations?user=47c4aMsAAAAJ&hl=en

Course aims

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

Course Objectives:

  • 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.

Teaching methods

The contact hours will be split into a one-hour pre-recorded lecture and a one-hour interactive seminar per week.

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 two pieces of project work for the course, which will be in the format of reports (including mathematical equations). The first is due halfway through your course and does not count towards your final outcome but preparing for it, and the feedback you are given, will help you prepare for your assessed piece of work due at the end of the course. The assessed work is marked pass or fail.

There is no formal word limit on either piece of work, but a reasonable guideline would be 1-2 pages for the initial report and 3-5 pages for the final assessed report.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form - Declaration of Authorship form

Application

We will close for enrolments 7 days prior to the start date to allow us to complete the course set up. We will email you at that time (7 days before the course begins) with further information and joining instructions. As always, students will want to check spam and junk folders during this period to ensure that these emails are received.

To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.

Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an enrolment form (Word) or enrolment form (Pdf).

Level and demands

Students who register for CATS points will receive a Record of CATS points on successful completion of their course assessment.

To earn credit (CATS points) you will need to register and pay an additional £10 fee per course. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online.

Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work at the required standard.

Students who do not register for CATS points during the enrolment process can either register for CATS points prior to the start of their course or retrospectively from the January 1st after the current full academic year has been completed. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.

Most of the Department's weekly classes have 10 or 20 CATS points assigned to them. 10 CATS points at FHEQ Level 4 usually consist of ten 2-hour sessions. 20 CATS points at FHEQ Level 4 usually consist of twenty 2-hour sessions. It is expected that, for every 2 hours of tuition you are given, you will engage in eight hours of private study.

Credit Accumulation and Transfer Scheme (CATS)

Terms & conditions for applicants and students

Information on financial support