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getTBinR 0.5.4 is now on CRAN and should be available on a mirror near you shortly! This update includes an additional data set for 2016 containing variables related to drug resistant Tuberculosis, some aesthetic updates to mapping functionality and a new summarise_tb_burden function for summarising TB metrics. Behind the scenes there has been an extensive test overhaul, with vdiffr being used to test images, and several bugs fixes. See below for a full list of changes and some example code exploring the new functionality.

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Introduction I recently attended the Public Health Research and Science Conference, run by Public Health England (PHE), at the University of Warwick. I was mainly there to present some work that I have been doing (along with my co-authors) estimating the direct effects of the 2005 change in BCG vaccination policy on Tuberculosis (TB) incidence rates (slides) but it was also a great opportunity to see what research is being done within, and partnered with, PHE.

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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.

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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.

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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.

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Projects

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.

Show Me Perseus

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

getTBinR

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.

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

Property Partner Portfolio Visualisation

A series of Tableau dashboards visualising the Property Partner portfolio

FC Dashboard

FC dashboard is a simple tool for exploratory data analysis on the funding circle loanbook. The source code can be found here. This is an independant project and is in no way associated with funding circle. Since the original development of this dashboard Funding Circle has stopped publishing it’s full loanbook, this means that some functionality will no longer work as originally intended and that data will no longer be updated.

idmodelr

R package that contains utility functions for infectious disease modelling. It’s main functionality is to facilitate the use of other available modelling packages such as pomp. Although, it does offer some basic modelling functionality itself. The source code is available on here

The Pebble Game

The Pebble Game is a shiny application that simulates the pebble game. This is a simple game that has been developed by BIDD at the University of Bristol, to help a general audience understand the role of vaccination in preventing the onward transmission of infectious disease. The source code can be found here.

Funding Circle Data Anaylsis

Visualisation and analysis of the Funding Circle loanbook.

PhD: Research Aims

Summary of the research aims for my PhD thesis

prettypublisher

R package to help in formatting R markdown documents into a publishable format. The source code is available on here

tbinenglanddataclean

R package containing the scripts required to clean data from the Enhanced Tuberculosis Surveillance system, and the Labour Force Survey, and to then calculate Tuberculosis incidence. The source code is available here

Teaching

I am helping to develop practicals for a short course at the School of Social and Community Medicine at the University of Bristol. As part of this I am developing a shiny app to allow exploration of a range of simple infectious disease models.

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