epikinetics

Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. R-CMD-check codecov

epikinetics is an R package for Bayesian hierarchical modelling of antibody kinetics.

The underlying model is taken from Russell TW et al., Real-time estimation of immunological responses against emerging SARS-CoV-2 variants in the UK: a mathematical modelling study. See the case study vignette for a replication of some key figures from the paper using epikinetics.

Three publicly available datasets from the above paper are also installed with the package and can be used as test input data:

{r} delta <- data.table::fread(system.file("delta_full.rds", package = "epikinetics")) ba2 <- data.table::fread(system.file("ba2_full.rds", package = "epikinetics")) xbb <- data.table::fread(system.file("xbb_full.rds", package = "epikinetics"))

If running the model with your own data, see the data vignette for an explanation of the input format.

Installing

To interface with cmdstan, this package uses cmdstanr, which isn’t available on cran, so you will first have to install it as follows:

install.packages('cmdstanr', repos = c('https://stan-dev.r-universe.dev', getOption('repos')))

You can then install epikinetics from GitHub:

remotes::install_github("seroanalytics/epikinetics")

Troubleshooting installation

If you don’t already have cmdstan installed, the epikinetics installer will attempt to install it, which can take a few minutes. If you see errors as part of installation, it is probably a good idea to try and install cmdstan first, for easier debugging. You can do this using the cmdstanr package as follows:

{r} cmdstanr::install_cmdstan()

Verify the installation is working with

{r} cmdstanr::cmdstan_version()

Running in Docker

Alternatively, you can run epikinetics via a Docker image, mounting a working directory which contains your input data files:

docker pull seroanalytics/epikinetics:main
docker run -v /path/to/local/workdir:/workdir -it seroanalytics/epikinetics:main

Developing

This package relies on the instantiate package to ship pre-compiled stan models. See the src/install.libs.R file for the logic for compiling and installing the stan models during package installation.

When running via devtools (e.g. test or load_all) the install.libs.R logic is not run, so for this we use an onLoad hook which checks whether the package is being loaded via devtools and if so, copies compiled models into a local bin directory where the system.file shim can access them.

Note that if you are running devtools::load_all or devtool::test and you don’t yet have cmdstan installed, this will trigger an installation of cmdstan which can take a few minutes.

Testing

To run all tests locally:

{r} devtools::test()

To run tests in a single file:

{r} devtools::test(filter="filename")

Some tests are skipped on CI to avoid exorbitantly long build times, but this means it is important to run all tests locally at least once before merging a pull request.

For snapshot testing of stan model outputs, we need the outputs to be exactly reproducible. As well as setting a seed, this requires the machine environment to be exactly the same, so on CI we run these inside a Docker container, via a bash script:

{shell} ./tests/snapshots/test-snapshots

This involves recompiling the model, so takes a while to run.

Note that

Docker

To build a Docker image, run docker/build. To push a new image to Dockerhub, docker/push. An image is built and pushed during CI on merge to main.

References

Russell TW, Townsley H, Hellewell J, Gahir J, Shawe-Taylor M, Greenwood D, Hodgson D, Hobbs A, Dowgier G, Penn R, Sanderson T, Stevenson-Leggett P, Bazire J, Harvey R, Fowler AS, Miah M, Smith C, Miranda M, Bawumia P, Mears HV, Adams L, Hatipoglu E, O’Reilly N, Warchal S, Ambrose K, Strange A, Kelly G, Kjar S, Papineni P, Corrah T, Gilson R, Libri V, Kassiotis G, Gamblin S, Lewis NS, Williams B, Swanton C, Gandhi S, Beale R, Wu MY, Bauer DLV, Carr EJ, Wall EC, Kucharski AJ. Real-time estimation of immunological responses against emerging SARS-CoV-2 variants in the UK: a mathematical modelling study. Lancet Infect Dis. 2024 Sep 11:S1473-3099(24)00484-5. doi: 10.1016/S1473-3099(24)00484-5.