combined_seasonal_output: Compute seasonal onset and burden levels from seasonal time...

In aedseo: Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels

Compute seasonal onset and burden levels from seasonal time series observations.

Description

This function performs automated and early detection of seasonal epidemic onsets and estimates the burden levels from time series dataset stratified by season. The seasonal onset estimates growth rates for consecutive time intervals and calculates the average sum of cases/incidence in consecutive time intervals (k). The burden levels use the previous seasons to estimate the levels of the current season. Output will be in incidence if population and incidence are assigned in input.

Usage

combined_seasonal_output(
  tsd,
  disease_threshold = 20,
  family = c("quasipoisson", "poisson"),
  family_quant = c("lnorm", "weibull", "exp"),
  season_start = 21,
  season_end = season_start - 1,
  only_current_season = TRUE,
  multiple_waves = FALSE,
  burden_level_decrease = NULL,
  steps_with_decrease = NULL,
  ...
)

Arguments

tsd	A tsd object containing time series data
disease_threshold	A number specifying the threshold for considering a disease outbreak. Should be given as incidence if population and incidence_denominator are in the tsd object else as cases. For seasonal onset it defines the per time-step disease threshold that has to be surpassed to possibly trigger a seasonal onset alarm. If the average observation count in a window of size k exceeds disease_threshold, a seasonal onset alarm can be triggered. For burden levels it defines the per time-step disease threshold that has to be surpassed for the observation to be included in the level calculations.
family	A character string specifying the family for modeling. Choose between 'poisson', or 'quasipoisson'. Must be one of: character, family-generator, or family object. This is passed to 'seasonal_onset()'.
family_quant	A character string specifying the family for modeling burden levels.
season_start, season_end	Integers giving the start and end weeks of the seasons to stratify the observations by.
only_current_season	Should the output only include results for the current season?
multiple_waves	A logical. Should the output contain multiple waves?
burden_level_decrease	A character string specifying the burden breakpoint the observations should decrease under before a new increase in observations can call a new wave onset if seasonal onset criteria are met. Choose between; "very low", "low", "medium", or "high".
steps_with_decrease	An integer specifying in how many time steps (days, weeks, months) the decrease should be observed under the burden_level_decrease (if there is a sudden decrease followed by an increase it could e.g. be due to testing). If multiple_waves are assigned steps_with_decrease defaults to 2.
...	Arguments passed to seasonal_burden_levels(), fit_percentiles() and seasonal_onset() functions.

Value

An object containing two lists: onset_output and burden_output:

onset_output:

A tsd_onset object containing:

• 'reference_time': The time point for which the growth rate is estimated.

• 'cases': The cases at reference time point.

• 'population': The population at reference time point.

• 'incidence': The incidence at reference time point.

• 'season': The stratification of observables in corresponding seasons.

• 'growth_rate': The estimated growth rate.

• 'lower_growth_rate': The lower bound of the growth rate's confidence interval.

• 'upper_growth_rate': The upper bound of the growth rate's confidence interval.

• 'growth_warning': Logical. Is the growth rate significantly higher than zero?

• 'average_observation_window': The average of cases or incidence within the time window.

• 'average_observation_warning': Logical. Does the average observations exceed the disease threshold?

• 'seasonal_onset_alarm': Logical. Is there a seasonal onset alarm?

• 'skipped_window': Logical. Was the window skipped due to missing observations?

• 'converged': Logical. Was the IWLS judged to have converged?

• 'seasonal_onset': Logical. The first detected seasonal onset in the season.

• Attributes: time_interval and incidence_denominator.

If multiple waves is selected the tsd_onset object will also contain:

• 'wave_number': The wave number in the time series data.

• 'wave_starts': Logical. Did a new wave start?

• 'wave_ends': Logical. Did the wave end?

• 'decrease_counter': How many consecutive time intervals have decreased below the selected burden breakpoint.

• 'decrease_level': A character specifying the selected burden breakpoint to fall below for ending the wave.

• 'decrease_value': A numeric specifying the selected burden breakpoint to fall below for ending the wave.

burden_output:

A tsd_burden_levels object containing:

• 'season': The season that burden levels are calculated for.

• 'high_conf_level': (only for intensity_level method) The conf_level chosen for the high level.

• 'conf_levels': (only for peak_level method) The conf_levels chosen to fit the 'low', 'medium', 'high' levels.

• 'values': A named vector with values for 'very low', 'low', 'medium', 'high' levels.

• 'optim' A list containing:

  • 'par': The fit parameters for the chosen family.

    • par_1:

      • For 'weibull': Shape parameter.

      • For 'lnorm': Mean of the log-transformed observations.

      • For 'exp': Rate parameter.

    • 'par_2':

      • For 'weibull': Scale parameter.

      • For 'lnorm': Standard deviation of the log-transformed observations.

      • For 'exp': Not applicable (set to NA).

  • 'obj_value': The value of the objective function - (negative log-likelihood), which represent the minimised objective function value from the optimisation. Smaller value equals better optimisation.

  • 'converged': Logical. TRUE if the optimisation converged.

  • 'family': The distribution family used for the optimization.

    • 'weibull': Uses the Weibull distribution for fitting.

    • 'lnorm': Uses the Log-normal distribution for fitting.

    • 'exp': Uses the Exponential distribution for fitting.

• 'disease_threshold': The input disease threshold, which is also the very low level.

• 'incidence_denominator': The observations per incidence-denominator.

• Attributes: time_interval and incidence_denominator.

Examples

# Generate random flu season
generate_flu_season <- function(start = 1, end = 1000) {
  random_increasing_obs <- round(sort(runif(24, min = start, max = end)))
  random_decreasing_obs <- round(rev(random_increasing_obs))

  # Generate peak numbers
  add_to_max <- c(50, 100, 200, 100)
  peak <- add_to_max + max(random_increasing_obs)

  # Combine into a single observations sequence
  observations <- c(random_increasing_obs, peak, random_decreasing_obs)

 return(observations)
}

season_1 <- generate_flu_season()
season_2 <- generate_flu_season()

start_date <- as.Date("2022-05-29")
end_date <- as.Date("2024-05-20")

weekly_dates <- seq.Date(from = start_date,
                         to = end_date,
                         by = "week")

tsd_data <- to_time_series(
  cases = c(season_1, season_2),
  time = as.Date(weekly_dates)
)

# Run the main function
combined_data <- combined_seasonal_output(tsd_data)
# Print seasonal onset results
print(combined_data$onset_output)
# Print burden level results
print(combined_data$burden_output)

aedseo documentation built on Nov. 18, 2025, 1:07 a.m.