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Get Signals

get_signals.Rd

This function analyzes surveillance data to detect signals using the specified method.

Usage

get_signals(
  data,
  method = "farrington",
  intervention_date = NULL,
  stratification = NULL,
  date_start = NULL,
  date_end = NULL,
  date_var = "date_report",
  number_of_weeks = 6
)

Arguments

data

A data frame containing the surveillance data preprocessed with [preprocess_data()].

method

A character string specifying the signal detection method to use. Available options include: `"farrington"`, `"ears"`, `"cusum"`, `"glm mean"`, `"glm timetrend"`, `"glm harmonic"`, `"glm harmonic with timetrend"`, `"glm farrington"`, `"glm farrington with timetrend"`. You can retrieve the full list using [available_algorithms()].

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.

stratification

A character vector specifying the columns to stratify the analysis. Default is NULL.

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.

See also

[available_algorithms()]

Examples

if (FALSE) { # \dontrun{
results <- input_example %>%
  preprocess_data() %>%
  get_signals(
    method = "farrington",
    stratification = c("county", "sex")
  )
} # }