Introduction to Outbreak Analytics using R

overview - Introduction to Outbreaks Analytics using R

Course dates: 2 - 6 November 2020

This course will provide the students with an in-depth, practical introduction to outbreak analytics, a new data science dedicated to informing the response to epidemics in real time. Essential aspects of the analysis of outbreak data will be covered, including fundamental principles of statistics, good coding practices, scientific reproducibility and report automation, estimation of key epidemiological delays, mortality and transmissibility, short term forecasting, and the analysis of genetic data to reconstruct transmission trees.

Due to the COVID-19 situation, all teaching will be done online, and involve a mixture of lectures, group discussions, and hands-on practical sessions using the R software. Worked examples will be based on direct experience of outbreak responses, including the Ebola epidemic in Eastern Democratic Republic of the Congo (2018-2020), and the response to COVID-19 in the UK. Lectures will be recorded and made available online to participants.

Course objectives

Day 1

  • General introduction to the course
  • Lecture: on the emergence of outbreak analytics and its role in outbreak response
  • Group discussion: what are the key questions in early outbreak responses?
  • Practical: setting up R, Rstudio, using the RECON deployer
  • Lecture/practical: introduction to data handling and visualisation using dplyr and ggplot2
  • Lecture: principles of reproducible data science
  • Practical: reproducibility using R

Day 2

  • Lecture: a primer on key statistical concepts for outbreak response
  • Lecture: descriptive epidemiology; epidemic curves; estimating mortality; characterising delay distributions; analysing contact data
  • Practical: simulated Ebola outbreak response – data handling and visualisation

Day 3

  • Lecture: primer on infectious disease modelling; estimating transmissibility (reproduction numbers, growth rates, doubling time); short term forecasting
  • Practical: simulated Ebola outbreak response - transmissibility and forecasting; real-life application to publicly available COVID-19 data

Day 4

  • Lecture: primer on genetic data analysis for outbreak response
  • Lecture: reconstructing transmission trees using epidemiological and genetic data
  • Practical: simulated Ebola outbreak response – inferring who infected whom

Day 5

  • Lecture: advanced tools for reproducible data science
  • Practical: open data session, build-your-own automated reporting infrastructure

Who should apply?

This course is open to field epidemiologists, public health practitioners, data managers, statisticians, computer scientists, data scientists, modellers with an interest in infectious disease outbreak response. A basic knowledge of R is encouraged, but the course will start at a beginner level.

Methods of assessment

Students will not be formally assessed. The course will be assessed using anonymous online forms, circulated.