Extend the computed threshold and expectation of the signal detection method to the past for visualisation purposes but not for signal generation Inside the function it is computed what the maximum number of timepoints is the signal detection algorithms can be applied for. This depends on the algorithm and the amount of historic data. The already generated signals dataframe is then extended with the expectation and threshold into the past

Extend the computed threshold and expectation of the signal detection method to the past for visualisation purposes but not for signal generation Inside the function it is computed what the maximum number of timepoints is the signal detection algorithms can be applied for. This depends on the algorithm and the amount of historic data. The already generated signals dataframe is then extended with the expectation and threshold into the past

pad_signals(data, signals)

Arguments

data

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

signals

tibble, output of the get_signals function with number of cases and signal per week, year

Value

tibble, with padded signals

Examples

if (FALSE) {
input_example_prepro <- input_example %>%
  preprocess_data()
signals <- input_example_prepro %>%
  get_signals(stratification = c("sex", "county_id"))
pad_signals(input_example_prepro, signals)
}