README.md

Update 16/01/2024

serosolver is in the midst of an overhaul. Please use the published branch to ensure continued compatibility with existing projects.

devtools::install_github("seroanalytics/serosolver",ref="published")

serosolver

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

serosolver uses a hierarchical model with a custom Markov chain Monte Carlo sampler to simultaneously infer antibody kinetics and infection histories from cross-sectional or longitudinal serological data. serosolver is a time-since-infection serodynamics model, meaning that infection times are back-calculated from one or more antibody measurements through an antibody kinetics model. serosolver can be used to infer infection timings during a study period using longitudinal measurements against a single antigen, or lifetime infection histories using multi-antigen serology panels. The package and model are described by Hay et al. here.

Recent changes

serosolver is back in active development to fix bugs, standardize variable names and add new features.

List of recent changes:

Installation

  1. Install R

  2. Install the development version of serosolver from GitHub:

devtools::install_github("seroanalytics/serosolver")
library(serosolver)

Guide and vignettes

Read the guide to set up and run a simple implementation with a simulation model.

Additional vignettes:

Example

This is a basic example of simulating some serological data and fitting the model using the MCMC framework.

library(serosolver)
library(ggplot2)
library(plyr)
library(dplyr)
library(tidyr)
library(data.table)
library(doParallel)
library(coda)

## Load in example parameter values and antigenic map
data(example_par_tab)
data(example_antigenic_map)
data(example_antibody_data)
data(example_inf_hist)
plot_antibody_data(example_antibody_data,example_antigenic_map$inf_times,n_indivs=1:4,example_inf_hist)

## Run the MCMC
# This example uses prior version 2 (i.e. beta prior on phi with parameters shape1 and shape2)
output <- serosolver::serosolver(example_par_tab, example_antibody_data, example_antigenic_map,
                filename="readme", prior_version=2,n_chains=3,parallel=TRUE,
                mcmc_pars=c(adaptive_iterations=100000, iterations=500000),verbose=FALSE)
# Plot model predicted titres for a subset of individuals
chains <- load_mcmc_chains(location=getwd(),par_tab=example_par_tab,burnin = 100000,unfixed=TRUE)
plot_model_fits(chain = chains$theta_chain,
                infection_histories = chains$inf_chain,
                known_infection_history = example_inf_hist,
                antibody_data = example_antibody_data,individuals=1:4,
                antigenic_map=example_antigenic_map,
                par_tab=example_par_tab,
                orientation="cross-sectional")



seroanalytics/serosolver documentation built on July 11, 2024, 4:41 p.m.