Overview of the courses

The courses, in a nutshell

Our two courses will be running in parallel ahead of ESCAIDE 2019 in Stockholm, on the 25-26th November 2019. See the practical information section for details on venue and timings.

Course #1 /// Introduction to epidemiological analysis using R

Outline

  • Level: accessible to R beginners; see pre-requisites on the registration page.
  • Capacity: 40 participants
  • Summary: an introduction to using R for epidemiological analysis.

Course content

A more detailed programme will be communicated closer to the event. The course will include a mixture of lectures and hands-on practicals. The following topics will be covered:

  • introduction to R and Rstudio
  • reading data from common formats (text files, Excel spreadsheets)
  • introduction to graphics using ggplot2
  • good practices for data science
  • introduction to automated reports using rmarkdown
  • hands on practical: the Stegen case study

Learning outcomes will include:

  • understanding of R basics
  • ability to structure a project
  • use of RECON deployer
  • ability to import and export data
  • methods for fast data cleaning
  • building meaningful graphics
  • ability to use automated data analysis reports
  • ability to generate simple tables and summaries
  • basics statistics including risk ratios and Fisher’s exact test
  • ability to generate static, and interactive maps
  • R packages used: rio, ggplot2, rmarkdown, incidence, epitrix, dplyr, sf

Course #2 /// Advanced course: tools for emergency outbreak response

Outline

  • Level: aimed at regular / experienced R users; see pre-requisites on the registration page.
  • Capacity: 20 participants
  • Summary: an introduction to new R tools for emergency outbreak response, currently used by the Ebola analytical cell in North Kivu, DRC.

Course content

A more detailed programme will be communicated closer to the event. The course will include a mixture of lectures and hands-on practicals

  • good practices for data science
  • refresher on Rmarkdown and some advanced practices
  • introduction to the linelist package
  • introduction to the reportfactory
  • primer on statistics for outbreak response
  • estimating transmissibility from case data
  • incidence forecasting
  • practical: simulated Ebola outbreak response

Learning outcomes will include:

  • use of RECON deployer
  • advanced customisation of rmarkdown reports
  • advanced data cleaning using dictionaries
  • efficient data handling using dplyr
  • consolidated good practices for data science
  • handling multiple automated reports simultaneously
  • understanding of some essential statistical concepts for outbreak response, including models used for predicting future dynamics of cases
  • ability to handle, visualise and analyse transmission chains
  • R packages used: rio, ggplot2, dplyr, rmarkdown, incidence, epitrix, linelist, reportfactory, earlyR, EpiEstim, projections
Menu