sim.cs: Simulate a cross-sectional serosurvey with noise

In serocalculator: Estimating Infection Rates from Serological Data

Simulate a cross-sectional serosurvey with noise

Description

Makes a cross-sectional data set (age, y(t) set) and adds noise, if desired.

Usage

sim.cs(
  lambda = 0.1,
  n.smpl = 100,
  age.rng = c(0, 20),
  age.fx = NA,
  antigen_isos,
  n.mc = 0,
  renew.params = FALSE,
  add.noise = FALSE,
  curve_params,
  noise_limits,
  format = "wide",
  verbose = FALSE,
  ...
)

Arguments

lambda	a numeric() scalar indicating the incidence rate (in events per person-years)
n.smpl	number of samples to simulate
age.rng	age range of sampled individuals, in years
age.fx	specify the curve parameters to use by age (does nothing at present?)
antigen_isos	Character vector with one or more antibody names. Values must match curve_params.
n.mc	how many MCMC samples to use: when n.mc is in 1:4000 a fixed posterior sample is used when n.mc = 0, a random sample is chosen
renew.params	whether to generate a new parameter set for each infection renew.params = TRUE generates a new parameter set for each infection renew.params = FALSE keeps the one selected at birth, but updates baseline y0
add.noise	a logical() indicating whether to add biological and measurement noise
curve_params	a data.frame() containing MCMC samples of parameters from the Bayesian posterior distribution of a longitudinal decay curve model. The parameter columns must be named: antigen_iso: a character() vector indicating antigen-isotype combinations iter: an integer() vector indicating MCMC sampling iterations y0: baseline antibody level at $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration of infection alpha: antibody decay rate (1/days for the current longitudinal parameter sets) r: shape factor of antibody decay
noise_limits	biologic noise distribution parameters
format	a character() variable, containing either: "long" (one measurement per row) or "wide" (one serum sample per row)
verbose	logical: if TRUE, print verbose log information to console
...	additional arguments passed to simcs.tinf()

Value

a tibble::tbl_df containing simulated cross-sectional serosurvey data, with columns:

• age: age (in days)

• one column for each element in the antigen_iso input argument

Examples

# Load curve parameters
curve <-
  typhoid_curves_nostrat_100

# Specify the antibody-isotype responses to include in analyses
antibodies <- c("HlyE_IgA", "HlyE_IgG")

# Set seed to reproduce results
set.seed(54321)

# Simulated incidence rate per person-year
lambda <- 0.2;

# Range covered in simulations
lifespan <- c(0, 10);

# Cross-sectional sample size
nrep <- 100

# Biologic noise distribution
dlims <- rbind(
  "HlyE_IgA" = c(min = 0, max = 0.5),
  "HlyE_IgG" = c(min = 0, max = 0.5)
)

# Generate cross-sectional data
csdata <- sim.cs(
  curve_params = curve,
  lambda = lambda,
  n.smpl = nrep,
  age.rng = lifespan,
  antigen_isos = antibodies,
  n.mc = 0,
  renew.params = TRUE,
  add.noise = TRUE,
  noise_limits = dlims,
  format = "long"
)

serocalculator documentation built on April 3, 2025, 7:35 p.m.