Get Signals Stratified — get_signals_stratified • SignalDetectionTool

This function stratifies and aggregates surveillance data by specified columns and analyzes each stratum separately using the specified method.

get_signals_stratified(
  data,
  fun,
  model = "",
  intervention_date = NULL,
  time_trend = FALSE,
  stratification_columns,
  date_start = NULL,
  date_end = NULL,
  date_var = "date_report",
  number_of_weeks = 6
)

Arguments

data: A data frame containing the surveillance data.
fun: The signal detection function to apply to each stratum.
model: character, default empty string which is the choice if farrington, ears or cusum are used and if a glm method was chosen as outbreak detection method then one of c("mean","sincos", "FN")
intervention_date: A date object or character of format yyyy-mm-dd specifying the date for the intervention in the pandemic correction models. After this date a new intercept and possibly time_trend is fitted.
time_trend: boolean default TRUE setting time_trend in the get_signals_glm(). This parameter is only used when an the glm based outbreak detection models are used, i.e. for the models c("mean","sincos", "FN")
stratification_columns: A character vector specifying the columns to stratify the data by.
date_start: A date object or character of format yyyy-mm-dd specifying the start date to filter the data by. Default is NULL.
date_end: A date object or character of format yyyy-mm-dd specifying the end date to filter the data by. Default is NULL.
date_var: a character specifying the date variable name used for the aggregation. Default is "date_report".
number_of_weeks: integer, specifying number of weeks to generate signals for.

Value

A tibble containing the results of the signal detection analysis stratified by the specified columns.

Examples

if (FALSE) {
data <- read.csv("../data/input/input.csv")
categories <- c("county", "sex", "age_group") # Replace with actual column names
results <- get_signals_stratified(
  data,
  fun = get_signals_farringtonflexible,
  stratification_columns = categories
)
print(results)
}