Exploring Estimates of the Tuberculosis Case Fatality Ratio - with getTBinR

This is a quick post exploring estimates of the case fatality ratio for Tuberculosis (TB) from data published by the World Health Organisation (WHO). It makes use of getTBinR (which is now on CRAN), pacman for package management, hrbrthemes for plot themes, and pathwork for combining multiple plots into a storyboard. For an introduction to using getTBinR to explore the WHO TB data see this post. It is estimated that in 2016 there was more than 10 million cases of active TB, with 1.

Explore Global Tuberculosis

A shiny app showcasing the functionality of getTBinR. It allows the exploration of the global burden of Tuberculosis. Any metric in the WHO data can be explored, with country selection using the built in map, and animation possible by year. Source code available here.

BIDD Modelling Course

The course practicals for the modelling infectious disease short course `run by BIDD at the University of Bristol. Source code available here.

Explore Infectious Disease Models

This shiny app has been developed to allow the exploration of the parameter space of compartmental infectious disease models. It is designed to be used as a teaching aid when introducing people to the concepts behind infectious disease models without requiring them to interact with the underlying code. A number of infectious disease models are included, such as a Susceptible, Exposed, Infected, Recovered model. Source code available here.

Exploring Global Trends in Tuberculosis Incidence Rates - with GetTBinR

In November I attended Epidemics, which is a conference focused on modelling infectious diseases. There was a lot of great work and perhaps most excitingly a lot of work being offered as R packages. I’ve recently begun wrapping all my analytical work in R packages, as it makes producing reproducible research a breeze! Unfortunately all of this work is still making it’s way towards publication and for a variety of reasons can’t be shared until it has passed this hurdle.

Show Me Perseus

A shiny app to remove potential friction for new R users when using the R perseus package. Source code available here.


Quickly and easily import analysis ready TB burden data, from the World Health Orgnaisation (WHO), into R. The aim of the package is to speed up access to high quality TB burden data, using a simple R interface. Generic plotting functions are provided to allow for rapid graphical exploration of the WHO TB data. This package is inspired by a blog post, which looked at WHO TB incidence rates. See here for the WHO data permissions.

Tuberculosis Incidence and Interventions in England and Wales, 1913-2016

This interactive dashboard uses data on Tuberculosis incidence from 1913-1916 released by Public Health England and combines it with data on the interventions against Tuberculosis that have been discovered/implemented over the last century. The data was cleaned and imported into R using the tbinenglanddataclean R package, which also contains information on how to apply for additional data, scripts to clean data extracts and graphing functions to visualise them. The dashboard is a work in progress and additional interventions, new figures and increased interactivity will be added over time.

Introduction to TB Modelling

A shiny app reproducing the models used in the Introduction to Tuberculosis modelling course practicals, run by TB MAC at the 2017 Union conference. See the TB MAC website for course materials and further resources. The models used in this course, and reproduced in this shiny app, were based on one published by Lin et al.. The source code is available on here

Tuberculosis in England and Wales

A shiny dashboard providing interactive plots of Tuberuclosis Incidence in England and Wales using publicly available data. The source code is available on here