simulate_serosurvey_age_time: Simulate serosurvey data based on an age-and-time-varying...

In serofoi: Bayesian Estimation of the Force of Infection from Serological Data

Simulate serosurvey data based on an age-and-time-varying Force-of-Infection (FoI) model.

Description

This function generates binned serosurvey data based on an age-and-time-varying FoI model, optionally including seroreversion. This function allows construction of serosurveys with binned age groups, and it generates uncertainty in the distribution of a sample size within an age bin through multinomial sampling.

Usage

simulate_serosurvey_age_time(foi, survey_features, seroreversion_rate = 0)

Arguments

foi	A dataframe containing the FoI values. For time-varying models the columns should be: year Calendar years starting at the birth year of the oldest person and up to the time of the serosurvey foi Corresponding values of the FoI by year For age-varying models the columns should be:. age Ages starting at 1 and up to the age of the oldest person in the serosurvey foi Corresponding values of the FoI by age For age-and-time-varying models the columns should be: age Ages starting at 1 and up to the age of the oldest person in the serosurvey time Calendar years starting at the birth year of the oldest person and up to the time of the serosurvey foi Corresponding values of FoI by age and year
survey_features	A dataframe containing information about the binned age groups and sample sizes for each. It should contain columns: age_min Left limits of the age groups to be considered in the serosurvey age_max Right limits of the age groups to be considered in the serosurvey n_sample Number of samples by age group The resulting age intervals are closed to the left [ and open to the right ⁠)⁠.
seroreversion_rate	A non-negative value determining the rate of seroreversion (per year). Default is 0.

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

# specify FOIs for each year
foi_df <- expand.grid(
  year = seq(1990, 2009, 1),
  age = seq(1, 20, 1)
)
foi_df$foi <- rnorm(20 * 20, 0.1, 0.01)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey_age_time(
foi_df, survey_features)

serofoi documentation built on April 3, 2025, 11:40 p.m.