serodynamics: Modeling Longitudinal Antibody Responses to Infection

Implements Bayesian hierarchical models for estimating antibody kinetic parameters from longitudinal serological data. Fits two-phase within-host models capturing antibody rise, peak, and decay following pathogen infection, using 'JAGS' for posterior inference. Designed as the upstream companion to the 'serocalculator' package for end-to-end seroepidemiologic analysis. Methods are described in Teunis et al. (2016) doi:10.1016/j.epidem.2016.04.001 and Teunis and van Eijkeren (2020) doi:10.1002/sim.8578 .

See also

Useful links:

• https://github.com/UCD-SERG/serodynamics

• https://ucd-serg.github.io/serodynamics/

• Report bugs at https://github.com/UCD-SERG/serodynamics/issues

Author

Maintainer: Samuel Schildhauer sschildhauer@ucdavis.edu (ORCID)

Authors:

• Samuel Schildhauer sschildhauer@ucdavis.edu (ORCID)

• Peter Teunis p.teunis@emory.edu (ORCID) (Author of the method and original code.) [copyright holder]

• Kwan Ho Lee ksjlee@ucdavis.edu (ORCID)

• Kristen Aiemjoy kaiemjoy@ucdavis.edu (ORCID)

• Douglas Ezra Morrison demorrison@ucdavis.edu (ORCID)