The goal of the imcover package is to provide access to data download,
processing and modelling tools to support a Bayesian statistical
modelling approach to generate national estimates of immunization
coverage from multiple time series of data on coverage. imcover is
built as part of the open-source statistical computing and modelling
language, R (https://cran.r-project.org/). This package is designed
to support broadly replicable and reproducible analyses of immunization
coverage.
The core of imcover is the functionality to fit a Bayesian statistical
model of multiple time series. The sources of coverage data (in this
example administrative, official and surveys) are taken as multiple,
partial estimates of the true, unobserved immunization coverage in a
country. A Bayesian estimation approach allows us to incorporate these
multiple datasets, place prior beliefs on which sources are more
reliable, share information between countries, and to quantify
uncertainty in our estimate of the latent immunization coverage.
imcover provides an interface to Stan
(https://mc-stan.org/) for statistical
computation. This means that, in addition to imcover, many of the
tools for assessing model performance and visualizing results from
Stan will work for imcover results. However, users must have Stan
installed and linked with R in order to use imcover
To install Stan, please follow the instructions for your operating
system described here:
https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
The imcover package is not yet on CRAN and can be installed from
Github using the following command:
devtools::install_github('wpgp/imcover@main')The build process takes some time because it is compiling the C++ code
for the Stan models. It may also ask you to install some additional
dependencies.
Feedback and contributions are welcome. Please raise or respond to an issue, or create a new branch to develop a feature/modification and submit a pull request.
This work was funded by WHO and carried out by members of the WorldPop project at the University of Southampton, United Kingdom. The authors gratefully acknowledge the WHO-UNICEF immunization coverage working group for their valuable inputs and feedback during model and software development.