surv_lineage_prevalence: Estimate lineage prevalence with design weights

In survinger: Design-Adjusted Inference for Pathogen Lineage Surveillance

Estimate lineage prevalence with design weights

Description

Estimates the prevalence of a specified pathogen lineage over time, correcting for unequal sequencing rates across strata.

Usage

## S3 method for class 'surv_prevalence'
print(x, ...)

## S3 method for class 'surv_prevalence'
as.data.frame(x, ...)

surv_lineage_prevalence(
  design,
  lineage,
  time = "epiweek",
  method = c("hajek", "horvitz_thompson", "poststratified"),
  conf_level = 0.95,
  min_obs = 5L
)

Arguments

x	Object to convert.
...	Additional arguments (unused).
design	A surv_design object.
lineage	Character. Target lineage name.
time	Character. Time aggregation: "epiweek", "month", "date", or a column name. Default "epiweek".
method	Character. Estimation method: "hajek" (default), "horvitz_thompson", or "poststratified".
conf_level	Numeric. Confidence level. Default 0.95.
min_obs	Integer. Minimum observations per time period. Default 5.

Value

Invisibly returns the input object.

A data.frame.

A surv_prevalence object.

Examples

sim <- surv_simulate(n_regions = 3, n_weeks = 10, seed = 1)
d <- surv_design(sim$sequences, ~ region,
                 sim$population[c("region", "seq_rate")], sim$population)
prev <- surv_lineage_prevalence(d, "BA.2.86")
print(prev)

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