surv_simulate: Simulate genomic surveillance data

In survinger: Design-Adjusted Inference for Pathogen Lineage Surveillance

Simulate genomic surveillance data

Description

Generates synthetic surveillance datasets with realistic features: multiple regions with unequal sequencing rates, multiple lineages with time-varying prevalence, configurable reporting delays, and multiple sample sources.

Usage

surv_simulate(
  n_regions = 5L,
  n_weeks = 26L,
  total_positive_per_week = 1000L,
  sequencing_rates = NULL,
  lineage_dynamics = NULL,
  delay_params = list(mu = 10, size = 3),
  sources = c("clinical", "wastewater", "sentinel"),
  source_weights = c(0.7, 0.2, 0.1),
  seed = NULL
)

Arguments

n_regions	Integer. Number of geographic regions. Default 5.
n_weeks	Integer. Number of epiweeks. Default 26.
total_positive_per_week	Integer. Mean total positive cases per week across all regions. Default 1000.
sequencing_rates	Numeric vector of length n_regions. Per-region sequencing probability. If NULL, generated from a Beta distribution with realistic inequality. Default NULL.
lineage_dynamics	Named list of functions, each taking a week number and returning a positive weight. If NULL, uses a default four-lineage scenario. Default NULL.
delay_params	List with mu and size for negative binomial reporting delay. Default list(mu = 10, size = 3).
sources	Character vector of sample source types. Default c("clinical", "wastewater", "sentinel").
source_weights	Numeric vector (same length as sources). Default c(0.7, 0.2, 0.1).
seed	Integer or NULL. Random seed. Default NULL.

Value

A named list with elements:

sequences

Tibble of individual sequence records.

population

Tibble with one row per region.

truth

Tibble of true lineage prevalence by region and week.

parameters

List of all input parameters.

Examples

sim <- surv_simulate(n_regions = 3, n_weeks = 8, seed = 42)
head(sim$sequences)
sim$population

survinger documentation built on April 27, 2026, 9:10 a.m.