serosv (version 1.1.0)

correct_prevalence: Estimate the true sero prevalence using Bayesian estimation

Description

Estimate the true sero prevalence using Bayesian estimation

Usage

correct_prevalence(
  data,
  bayesian = TRUE,
  init_se = 0.95,
  init_sp = 0.8,
  study_size_se = 1000,
  study_size_sp = 1000,
  chains = 1,
  warmup = 1000,
  iter = 2000
)

Value

a list of 2 items

info

estimated parameters

corrected_sero

data.frame containing age, the corresponding estimated seroprevalance, adjusted tot and pos

Arguments

data

the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)

bayesian

whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.

init_se

sensitivity of the serological test

init_sp

specificity of the serological test

study_size_se

(applicable when `bayesian=TRUE`) study size for sensitivity validation study (i.e., number of confirmed infected patients in the study)

study_size_sp

(applicable when `bayesian=TRUE`) study size for specificity validation study (i.e., number of confirmed non-infected patients in the study)

chains

(applicable when `bayesian=TRUE`) number of Markov chains

warmup

(applicable when `bayesian=TRUE`) number of warm up runs

iter

(applicable when `bayesian=TRUE`) number of iterations

Examples

data <- rubella_uk_1986_1987
correct_prevalence(data)