serojump

serojump hex logo

Project Status: Active – The project has reached a stable, usable state and is being actively developed. R-CMD-check

Schematic of the methods behind the serojump package
Schematic of the methods behind the serojump package

Overview

The serojump package provides tools for fitting serological models to antibody kinetics data using reversible-jump Markov Chain Monte Carlo (RJ-MCMC). It enables researchers to model the dynamics of antibody levels in response to infections, incorporating both observational and antibody kinetics models. The package supports the inclusion of priors and various exposure scenarios, making it highly flexible for serological data analysis.

This package is ideal for researchers looking to:

  • Model antibody kinetics over time
  • Estimate infection rates and antibody waning using serological data
  • Perform MCMC inference on serological models

The best place to get started with serojump is our paper here or our interactive widget here. In this repo, several model templates are provided to simplify usage, while also allowing users to customise models for specific research questions.

Installation

To install the serojump package, follow these steps:

Step 1: Install R

Make sure you have R installed on your system. You can download R from https://cran.r-project.org/.

Step 2: Install serojump from GitHub

You can install the development version of serojump from GitHub using the devtools package. If you don’t already have devtools installed, you can install it with:


install.packages("devtools")
devtools::install_github("seroanalytics/serojump")

Step 3: Load serojump from GitHub

After installation, you can load the serojump package into your R session with:


library(serojump)

Usage

For detailed usage instructions, please refer to the package vignettes and examples. These vignettes explain:

  • The format of the data require for serojump to work, click here.
  • How to define observational models and antibody kinetics models and run the serojump sampler, click here.
  • Specify priors for your models and integrate empirical data [coming soon]

In addition we have several worked examples of serojump on: - Simulated data click here.

Citation

If you use serojump in your research, please cite our paper:

Hodgson D, Hay J, Jarju S, Jobe D, Wenlock R, et al. (2025) serojump: A Bayesian tool for inferring infection timing and antibody kinetics from longitudinal serological data. PLOS Computational Biology 21(9): e1013467. https://doi.org/10.1371/journal.pcbi.1013467

Contributing

We welcome contributions and suggestions! If you’d like to contribute to the serojump package or report issues, please feel free to:

  • Submit a pull request on GitHub.
  • Open an issue on the repository.

If you have any questions or feedback, or would like more informative vignettes, you can contact the package maintainer at:

David Hodgson
Email: david.hodgson@charite.de