serosolver is a modelling and inference package that uses a dynamic model to infer antibody dynamics and infection histories from cross-sectional or longitudinal serological data. The model infers individual-level infection histories, historical attack rates, and patterns of antibody dynamics by accounting for cross-reactive antibody responses and measurement error.

## Installation

1. Install R

2. Install the development version of serosolver from GitHub:

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

## Quick start and vignettes

Read the quick start vignette to set up and run a simple implementation with a simulation model.

There are additional Rmarkdown vignettes for Case Study 1 (longitudinal analysis of influenza A/H1N1p in Hong Kong) and Case Study 2 (cross-sectional analysis of influenza A/H3N2 in Guangzhou Province, China), to accompany the analysis in the serosolver paper.

## Example

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

library(serosolver)
library(plyr)
library(data.table)
library(ggplot2)

## Load in example parameter values and antigenic map
data(example_par_tab)
data(example_antigenic_map)

## Get all possible infection times
strain_isolation_times <- unique(example_antigenic_map$inf_times) ## Vector of strains that have titres (note only one representative strain per time) sampled_viruses <- seq(min(strain_isolation_times), max(strain_isolation_times), by=2) ## Times at which serum samples can be taken sampling_times <- 2010:2015 ## Number of serum samples taken n_samps <- 2 ## Simulate some random attack rates attack_rates <- runif(length(strain_isolation_times), 0.05, 0.15) ## Simulate a full serosurvey with these parameters all_simulated_data <- simulate_data(par_tab=example_par_tab, group=1, n_indiv=50, strain_isolation_times=strain_isolation_times, measured_strains=sampled_viruses, sampling_times=2010:2015, nsamps=n_samps, antigenic_map=example_antigenic_map, age_min=10,age_max=75, attack_rates=attack_rates, repeats=2) ## Pull out the simulated titre data and infection histories titre_dat <- all_simulated_data$data
ages <- all_simulated_data$ages example_inf_hist <- all_simulated_data$infection_histories
example_titre_dat <- merge(titre_dat, ages)

## Run the MCMC
# This example uses prior version 2 (i.e. beta prior on phi with parameters alpha, beta)
# We have to remove the explicit specification of phi in the parameter table
par_tab <- example_par_tab[example_par_tab$names != "phi",] res <- run_MCMC(par_tab, example_titre_dat, example_antigenic_map, filename="test", version=2, mcmc_pars=c(adaptive_period=20000, iterations=80000, inf_propn=0.5,hist_sample_prob=0.5, save_block=10000,thin=10,thin_hist=100)) ## Read in the MCMC chains and plot posteriors chain <- read.csv(res$chain_file)
inf_chain <- data.table::fread(res$history_file) plot(coda::as.mcmc(chain[chain$sampno > 20000,c("mu","wane","lnlike")]))

# Plot model predicted titres for a subset of individuals
plot_infection_histories(chain = chain,infection_histories = inf_chain,
titre_dat = example_titre_dat,individuals=c(1:4),
antigenic_map=example_antigenic_map,par_tab=par_tab)