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Create a tsd_growth_warning object to count consecutive significant observations

Source: R/consecutive_growth_warnings.R
consecutive_growth_warnings.Rd

This function calculates the number of consecutive significant ("growth_warning") observations, grouping them accordingly. The result is stored in an S3 object of class tsd_growth_warning.

Uses data from a tsd_onset object (output from seasonal_onset()).

seasonal_onset() has to be run with arguments;

  • season_start

  • season_end

  • only_current_season = FALSE

Usage

consecutive_growth_warnings(onset_output)

Arguments

onset_output

A tsd_onset object returned from seasonal_onset().

Value

An object of class tsd_growth_warning, containing; A tibble of processed observations, the significant_counter column specifies when a sequence of significant observation starts and ends. The first number is how many subsequent observations will be significant.

Examples

# Generate simulated data of seasonal waves
sim_data <- generate_seasonal_data(
  years = 5,
  start_date = as.Date("2022-05-26"),
  trend_rate = 1.002,
  noise_overdispersion = 2,
  relative_epidemic_concentration = 3
)

# Estimate seasonal onset
tsd_onset <- seasonal_onset(
  tsd = sim_data,
  season_start = 21,
  season_end = 20,
  only_current_season = FALSE
)

# Get consecutive significant observations
consecutive_growth_warnings(tsd_onset)
#> # A tibble: 256 × 21
#>    reference_time cases season    population incidence proportion samples
#>    <date>         <dbl> <chr>          <dbl>     <dbl>      <dbl>   <dbl>
#>  1 2022-06-23       101 2022/2023         NA        NA         NA      NA
#>  2 2022-06-30       113 2022/2023         NA        NA         NA      NA
#>  3 2022-07-07       159 2022/2023         NA        NA         NA      NA
#>  4 2022-07-14       152 2022/2023         NA        NA         NA      NA
#>  5 2022-07-21       155 2022/2023         NA        NA         NA      NA
#>  6 2022-07-28       201 2022/2023         NA        NA         NA      NA
#>  7 2022-08-04       197 2022/2023         NA        NA         NA      NA
#>  8 2022-08-11       237 2022/2023         NA        NA         NA      NA
#>  9 2022-08-18       196 2022/2023         NA        NA         NA      NA
#> 10 2022-08-25       235 2022/2023         NA        NA         NA      NA
#> # ℹ 246 more rows
#> # ℹ 14 more variables: growth_rate <dbl>, lower_growth_rate <dbl>,
#> #   upper_growth_rate <dbl>, growth_warning <lgl>,
#> #   average_observations_window <dbl>, average_observations_warning <lgl>,
#> #   seasonal_onset_alarm <lgl>, skipped_window <lgl>, converged <lgl>,
#> #   seasonal_onset <lgl>, counter <dbl>, changeFlag <lgl>, groupID <int>,
#> #   significant_counter <dbl>