Software

R packages and other software developed by the group

covid19.nhs.data: NHS Trust Level COVID-19 Data Aggregated to a Range of Spatial Scales.

Facilitates access to data on NHS trust level COVID-19 data aggregated to a range of spatial scales.

covidregionaldata: Subnational Data for COVID-19 Epidemiology.

An interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. Designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. This allows users to inspect and scrutinise the data, and tools used to process it, at every step. For all countries supported, data includes a daily time-series of cases. Wherever available data is also provided for deaths, hospitalisations, and tests. National level data are also supported using a range of sources.

epimixr: Epidemiological analysis using social mixing matrices.

Provides methods to conduct epidemiological analysis using social mixing matrices, such as calculating contact-adjusted immunity levels or age distributions of epidemics.

EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters.

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020)), and current best practices (Gostic et al. (2020)). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is under active development.

epinowcast: Flexible Hierarchical Nowcasting.

Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.

rbi: Interface to ‘LibBi’.

Provides a complete interface to ‘LibBi’, a library for Bayesian inference (see http://libbi.org and Murray, 2015 for more information). This includes functions for manipulating ‘LibBi’ models, for reading and writing ‘LibBi’ input/output files, for converting ‘LibBi’ output to provide traces for use with the coda package, and for running ‘LibBi’ to conduct inference.

rbi.helpers: ‘RBi’ Helper Functions.

Contains a collection of helper functions to use with ‘RBi’, the R interface to ‘LibBi’, described in Murray et al. (2015). It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in ‘LibBi’ results and R time/dates.

scoringutils: Utilities for Scoring and Assessing Predictions.

Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007), Jordan, A., Krüger, F., & Lerch, S. (2019)) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019)) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.

socialmixr: Social Mixing Matrices for Infectious Disease Modelling.

Provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008).