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This function summarises peak timing and seasonal onset from estimates in a tsd_onset object. This can be useful for investigating if the current season falls within estimates from previous seasons or if it is very distinct from previous seasons.

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

seasonal_onset() has to be run with arguments;

  • disease_threshold

  • season_start

  • season_end

  • only_current_season = FALSE

Usage

historical_summary(onset_output)

Arguments

onset_output

A tsd_onset object returned from seasonal_onset().

Value

An object of class historical_summary, containing:

  • Usual time to seasonal peak (weeks after onset)

  • The week in which the peak usually falls

  • Usual peak intensity

  • The week in which the onset usually falls

  • Usual onset intensity and growth rate estimates

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 = 1.1
)

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

# Get historical summary
historical_summary(tsd_onset)
#> # A tibble: 5 × 10
#>   season    onset_time peak_time  peak_intensity lower_growth_rate_onset
#>   <chr>     <date>     <date>              <dbl>                   <dbl>
#> 1 2022/2023 2022-07-21 2022-08-11            202                 0.0119 
#> 2 2023/2024 2023-05-25 2023-08-31            247                 0.00917
#> 3 2024/2025 2024-05-23 2024-08-01            276                 0.0953 
#> 4 2025/2026 2025-05-22 2025-07-24            286                 0.0424 
#> 5 2026/2027 2026-05-21 2026-08-20            325                 0.0777 
#> # ℹ 5 more variables: growth_rate_onset <dbl>, upper_growth_rate_onset <dbl>,
#> #   onset_week <dbl>, peak_week <dbl>, weeks_to_peak <dbl>