Title:	Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels
Version:	0.3.0
Description:	A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://github.com/ssi-dk/aedseo, https://ssi-dk.github.io/aedseo/
BugReports:	https://github.com/ssi-dk/aedseo/issues
Depends:	R (≥ 4.2.0)
Suggests:	grid, ISOweek, kableExtra, knitr, mem, rmarkdown, testthat (≥ 3.0.0), tidyr, withr
Config/testthat/edition:	3
Imports:	base, checkmate, dplyr, ggplot2, lifecycle, lubridate, plyr, purrr, pracma, rlang, scales, stats, stringr, tibble
Config/Needs/website:	rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-04-09 08:57:36 UTC; b246418
Author:	Sofia Myrup Otero [aut], Kasper Schou Telkamp [aut], Lasse Engbo Christiansen [aut, cre], Rasmus Skytte Randløv [rev], Statens Serum Institut, SSI [cph, fnd]
Maintainer:	Lasse Engbo Christiansen <lsec@ssi.dk>
Repository:	CRAN
Date/Publication:	2025-04-09 09:20:02 UTC

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

Description

A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion.

Author(s)

Maintainer: Lasse Engbo Christiansen lsec@ssi.dk (ORCID)

Authors:

• Sofia Myrup Otero smot@ssi.dk (ORCID)

• Kasper Schou Telkamp kasper.telkamp@gmail.com (ORCID)

Other contributors:

• Rasmus Skytte Randløv rske@ssi.dk (ORCID) [reviewer]

• Statens Serum Institut, SSI [copyright holder, funder]

See Also

Useful links:

• https://github.com/ssi-dk/aedseo

• https://ssi-dk.github.io/aedseo/

• Report bugs at https://github.com/ssi-dk/aedseo/issues

Deprecated aedseo function

Description

This function has been renamed to better reflect its purpose. Please use seasonal_onset() instead.

Usage

aedseo(...)

Arguments

Autoplot a tsd object

Description

Generates a complete 'ggplot' object suitable for visualizing time series data in a tsd, tsd_onset or tsd_onset_and_burden object.

autoplot(tsd)

• Generates points for each observation and connects them with a line.

autoplot(tsd_onset)

• The first plot generates a line connecting the observations. The transparency of the points reflects if seasonal onset has occurred.

• The second plot presents the growth rate for each observation along with confidence intervals. The transparency of the points indicates whether a growth warning condition is met.

autoplot(tsd_onset_and_burden)

• Generates a line connecting the observations in the current season, along with colored regions representing different burdens levels and a vertical line indicating outbreak start. The y-axis is scaled with ggplot2::scale_y_log10 to give better visualisation of the burden levels.

Usage

autoplot(object, ...)

## S3 method for class 'tsd'
autoplot(
  object,
  line_width = 0.7,
  obs_size = 2,
  text_family = "sans",
  time_interval_step = "5 weeks",
  y_label = "Weekly observations",
  ...
)

## S3 method for class 'tsd_onset'
autoplot(
  object,
  disease_color = "black",
  line_width = 0.7,
  obs_size = 2,
  alpha_warning = 0.2,
  alpha_ribbon = 0.1,
  text_family = "sans",
  legend_position = "bottom",
  time_interval_step = "5 weeks",
  y_label = "Weekly observations",
  ...
)

## S3 method for class 'tsd_onset_and_burden'
autoplot(
  object,
  y_lower_bound = 5,
  factor_to_max = 2,
  disease_color = "royalblue",
  season_start = 21,
  season_end = season_start - 1,
  time_interval_step = "3 weeks",
  y_label = "Weekly observations",
  text_burden_size = 10/2.8,
  fill_alpha = c(0.45, 0.6, 0.75, 0.89, 1),
  text_family = "sans",
  line_color = "black",
  line_type = "solid",
  vline_color = "red",
  vline_linetype = "dashed",
  y_scale_labels = scales::label_comma(big.mark = ".", decimal.mark = ","),
  theme_custom = ggplot2::theme_bw(),
  legend_position = "right",
  ...
)

## S3 method for class 'tsd_growth_warning'
autoplot(
  object,
  k = 5,
  skip_current_season = TRUE,
  line_width = 1,
  text_family = "sans",
  legend_position = "bottom",
  breaks_y_axis = 8,
  ...
)

Arguments

Value

A 'ggplot' object for visualizing the tsd data.

A 'ggplot' object for visualizing the tsd_onset data.

A 'ggplot' object for visualizing the tsd_onset_and_burden data for the current season.

A 'ggplot' object for visualizing the tsd_growth_warning data.

Examples

set.seed(345)
# Create an example `tsd` object
time_series <- generate_seasonal_data()
autoplot(time_series)

# Create an `tsd_onset` object
time_series_with_onset <- seasonal_onset(
  tsd = time_series,
  k = 3,
  level = 0.95,
  family = "quasipoisson"
)
autoplot(time_series_with_onset)

# Define `disease_threshold`
disease_threshold <- 150

# Create a `tsd_onset_and_burden` object
tsd_onset_burden <- combined_seasonal_output(
  tsd = time_series,
  disease_threshold = disease_threshold
)
autoplot(tsd_onset_burden)

# Create an `tsd_onset` object
tsd_onset <- seasonal_onset(
  tsd = time_series,
  k = 5,
  family = "quasipoisson",
  season_start = 21,
  only_current_season = FALSE
)

tsd_growth_warning <- consecutive_growth_warnings(tsd_onset)

autoplot(tsd_growth_warning)

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 sum of cases. The burden levels use the previous seasons to estimate the levels of the current season.

Usage

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

Arguments

Value

An object containing two lists: onset_output and burden_output:

onset_output:

A seasonal_onset object containing:

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

• 'observation': The observation in the 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?

• 'sum_of_cases': The sum of cases within the time window.

• 'sum_of_cases_warning': Logical. Does the Sum of Cases exceed the disease threshold?

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

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

• 'converged': Logical. Was the IWLS judged to have converged? - 'seasonal_onset': Logical. The first detected seasonal onset in the season?

burden_output:

A list 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.

• '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 minimized 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.

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 <- tsd(
  observation = c(season_1, season_2),
  time = as.Date(weekly_dates),
  time_interval = "week"
)

# 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)

Create a tsd_growth_warning object to count consecutive significant observations

Description

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_threshold.

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

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,
  family = "quasipoisson",
  season_start = 21,
  season_end = 20,
  only_current_season = FALSE
)

# Get consecutive significant observations
consecutive_growth_warnings(tsd_onset)

Determine Epidemiological Season

Description

This function identifies the epidemiological season, (must span new year) to which a given date belongs. The epidemiological season is defined by a start and end week, where weeks are numbered according to the ISO week date system.

Usage

epi_calendar(date, start = 21, end = 20)

Arguments

Value

A character vector indicating the season:

• "out_of_season" if the date is outside the specified season,

• If within the season, the function returns a character string indicating the epidemiological season.

Examples

# Check if a date is within the epidemiological season
epi_calendar(as.Date("2023-09-15"), start = 21, end = 20)
# Expected output: "2023/2024"

epi_calendar(as.Date("2023-05-30"), start = 40, end = 20)
# Expected output: "out_of_season"

try(epi_calendar(as.Date("2023-01-15"), start = 1, end = 40))
# Expected error: "`start` must be greater than `end`!"

epi_calendar(as.Date("2023-10-06"), start = 40, end = 11)
# Expected output: "2023/2024"

Fit a growth rate model to time series observations.

Description

This function fits a growth rate model to time series observations and provides parameter estimates along with confidence intervals.

Usage

fit_growth_rate(
  observations,
  level = 0.95,
  family = c("poisson", "quasipoisson")
)

Arguments

Value

A list containing:

• 'fit': The fitted growth rate model.

• 'estimate': A numeric vector with parameter estimates, including the growth rate and its confidence interval.

• 'level': The confidence level used for estimating parameter confidence intervals.

Examples

# Fit a growth rate model to a time series of counts
# (e.g., population growth)
data <- c(100, 120, 150, 180, 220, 270)
fit_growth_rate(
  observations = data,
  level = 0.95,
  family = "poisson"
)

Fits weighted observations to distribution and returns percentiles

Description

This function estimates the percentiles of weighted time series observations. The output contains the percentiles from the fitted distribution.

Usage

fit_percentiles(
  weighted_observations,
  conf_levels = c(0.5, 0.9, 0.95),
  family = c("lnorm", "weibull", "exp"),
  optim_method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"),
  lower_optim = -Inf,
  upper_optim = Inf
)

Arguments

Value

A list containing:

• 'conf_levels': The conf_levels chosen to fit the percentiles.

• 'percentiles': The percentile results from the fit.

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

  • par_1:

    • For 'weibull': Shape parameter (k).

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

    • For 'exp': Rate parameter (rate).

  • 'par_2':

    • For 'weibull': Scale parameter (scale).

    • 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 minimized 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.

Examples

# Create three seasons with random observations
obs <- 10
season <- c("2018/2019", "2019/2020", "2020/2021")
season_num_rev <- rev(seq(from = 1, to = length(season)))
observations <- rep(stats::rnorm(10, obs), length(season))

# Add into a tibble with decreasing weight for older seasons
data_input <- tibble::tibble(
  observation = observations,
  weight = 0.8^rep(season_num_rev, each = obs)
)

# Use the model
fit_percentiles(
  weighted_observations = data_input,
  conf_levels = c(0.50, 0.90, 0.95),
  family= "weibull"
)

Generate Simulated Data of Seasonal Waves as a tsd object

Description

This function generates a simulated dataset of seasonal waves with trend and noise. This function assumes 365 days, 52 weeks, and 12 months per year. Leap years are not included in the calculation.

Usage

generate_seasonal_data(
  years = 3,
  start_date = as.Date("2021-05-26"),
  amplitude = 100,
  mean = 100,
  phase = 0,
  trend_rate = NULL,
  noise_overdispersion = NULL,
  relative_epidemic_concentration = 1,
  time_interval = c("week", "day", "month"),
  lower_bound = 1e-06
)

Arguments

Value

A tsd object with simulated data containing:

• 'time': The time point for for when the observation is observed.

• 'observation': The observed value at the time point.

Examples

# Generate simulated data of seasonal waves

#With default arguments
default_sim <- generate_seasonal_data()
plot(default_sim)

#With an exponential growth rate trend
trend_sim <- generate_seasonal_data(trend_rate = 1.001)
plot(trend_sim)

#With noise
noise_sim <- generate_seasonal_data(noise_overdispersion = 2)
plot(noise_sim)

#With distinct parameters, trend and noise
sim_data <- generate_seasonal_data(
  years = 2,
  start_date = as.Date("2022-05-26"),
  amplitude = 2000,
  mean = 3000,
  trend_rate = 1.002,
  noise_overdispersion = 1.1,
  time_interval = c("week")
)
plot(sim_data, time_interval = "2 months")

Summarises estimates like seasonal peak and onset from all available seasons

Description

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

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)

Create a complete 'ggplot' appropriate to a particular data type

Description

This function generates a complete 'ggplot' object suitable for visualizing time series data in tsd, tsd_onset, tsd_onset_and_burden or tsd_growth_warning objects.

Usage

## S3 method for class 'tsd'
plot(x, ...)

## S3 method for class 'tsd_onset'
plot(x, ...)

## S3 method for class 'tsd_onset_and_burden'
plot(x, ...)

## S3 method for class 'tsd_growth_warning'
plot(x, ...)

Arguments

Value

A 'ggplot' object for visualizing output from desired method.

See Also

autoplot()

Examples

# set.seed(321)
# Create and plot `tsd` object
tsd_obj <- generate_seasonal_data(
  years = 3,
  phase = 1,
  start_date = as.Date("2021-10-18")
)
plot(tsd_obj)

disease_threshold <- 150

# Create and plot `tsd_onset` object
tsd_onset_obj <- seasonal_onset(
  tsd = tsd_obj,
  k = 3,
  level = 0.95,
  disease_threshold = disease_threshold,
  family = "quasipoisson"
)
plot(tsd_onset_obj)

# Create a `tsd_onset_and_burden` object
tsd_onset_burden_obj <- combined_seasonal_output(
  tsd = tsd_obj,
  disease_threshold = disease_threshold
)
plot(tsd_onset_burden_obj,
     y_lower_bound = ifelse(disease_threshold < 10, 1, 5))

# Create a `tsd_growth_warning` object
tsd_onset_seasons <- seasonal_onset(
  tsd = tsd_obj,
  season_start = 21,
  family = "quasipoisson",
  only_current_season = FALSE
)
tsd_gr_w <- consecutive_growth_warnings(tsd_onset_seasons)
plot(tsd_gr_w)

Predict Observations for Future Time Steps

Description

This function is used to predict future observations based on a tsd_onset object. It uses the time_interval attribute from the tsd_onset object to make predictions.

Usage

## S3 method for class 'tsd_onset'
predict(object, n_step = 3, ...)

Arguments

Value

A tibble-like object called tsd_predict containing the predicted observations, including reference time, lower confidence interval, and upper confidence interval for the specified number of future time steps.

Examples

# Generate predictions of time series data
set.seed(123)
time_series <- generate_seasonal_data(
  years = 1,
  time_interval = "day"
)
# Apply `seasonal_onset` analysis
time_series_with_onset <- seasonal_onset(
  tsd = time_series,
  k = 7
)
# Predict observations for the next 7 time steps
predict(object = time_series_with_onset, n_step = 7)

Compute burden levels from seasonal time series observations of current season.

Description

This function estimates the burden levels of time series observations that are stratified by season. It uses the previous seasons to estimate the levels of the current season. The output is results regarding the current season in the time series observations. NOTE: The data must include data for a complete previous season to make predictions for the current season.

Usage

seasonal_burden_levels(
  tsd,
  family = c("lnorm", "weibull", "exp"),
  season_start = 21,
  season_end = season_start - 1,
  method = c("intensity_levels", "peak_levels"),
  conf_levels = 0.95,
  decay_factor = 0.8,
  disease_threshold = 20,
  n_peak = 6,
  only_current_season = TRUE,
  ...
)

Arguments

Value

A list 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.

• '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 minimized 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.

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 <- tsd(
  observation = c(season_1, season_2),
  time = as.Date(weekly_dates),
  time_interval = "week"
)

# Print seasonal burden results
seasonal_burden_levels(tsd_data, family = "lnorm")

Automated and Early Detection of Seasonal Epidemic Onset

Description

This function performs automated and early detection of seasonal epidemic onsets on a time series dataset. It estimates growth rates for consecutive time intervals and calculates the sum of cases (sum_of_cases).

Usage

seasonal_onset(
  tsd,
  k = 5,
  level = 0.95,
  disease_threshold = NA_integer_,
  family = c("poisson", "quasipoisson"),
  na_fraction_allowed = 0.4,
  season_start = NULL,
  season_end = season_start - 1,
  only_current_season = NULL
)

Arguments

Value

A seasonal_onset object containing:

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

• 'observation': The observation in the 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?

• 'sum_of_cases': The sum of cases within the time window.

• 'sum_of_cases_warning': Logical. Does the Sum of Cases exceed the disease threshold?

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

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

• 'converged': Logical. Was the IWLS judged to have converged? - 'seasonal_onset': Logical. The first detected seasonal onset in the season?

Examples

# Create a tibble object from sample data
tsd_data <- tsd(
  observation = c(100, 120, 150, 180, 220, 270),
  time = as.Date(c(
    "2023-01-01",
    "2023-01-02",
    "2023-01-03",
    "2023-01-04",
    "2023-01-05",
    "2023-01-06"
  )),
  time_interval = "day"
)

# Estimate seasonal onset with a 3-day window and a Poisson family model
seasonal_onset(
  tsd = tsd_data,
  k = 3,
  level = 0.95,
  disease_threshold = 20,
  family = "poisson",
  na_fraction_allowed = 0.4,
  season_start = NULL,
  season_end = NULL,
  only_current_season = NULL
)

Summary method for tsd_burden_levels objects

Description

Summarize key results from a seasonal burden levels analysis.

Usage

## S3 method for class 'tsd_burden_levels'
summary(object, ...)

Arguments

Value

This function is used for its side effect, which is printing the burden levels.

Examples

# Create a `tsd` object
tsd_data <- generate_seasonal_data()

# Create a `tsd_burden_levels` object
tsd_burden_levels <- seasonal_burden_levels(
  tsd = tsd_data
)
# Print the summary
summary(tsd_burden_levels)

Summary method for tsd_onset objects

Description

Summarize key results from a seasonal onset analysis.

Usage

## S3 method for class 'tsd_onset'
summary(object, ...)

Arguments

Value

This function is used for its side effect, which is printing a summary message to the console.

Examples

# Create a `tsd` object
tsd_data <- generate_seasonal_data()

# Create a `tsd_onset` object
tsd_onset <- seasonal_onset(
  tsd = tsd_data,
  k = 3,
  disease_threshold = 100,
  season_start = 21,
  season_end = 20,
  level = 0.95,
  family = "poisson",
  only_current_season = TRUE
)
# Print the summary
summary(tsd_onset)

Create a tibble-like tsd (time-series data) object from observed data and corresponding dates.

Description

This function takes observations and the corresponding date vector and converts it into a tsd object, which is a time series data structure that can be used for time series analysis.

Usage

to_time_series(observation, time, time_interval = c("day", "week", "month"))

Arguments

Value

A tsd object containing:

• 'time': The time point for for when the observation is observed.

• 'observation': The observed value at the time point.

Examples

# Create a `tsd` object from daily data
daily_tsd <- to_time_series(
  observation = c(10, 15, 20, 18),
  time = as.Date(
    c("2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04")
  ),
  time_interval = "day"
)

# Create a `tsd` object from weekly data
weekly_tsd <- to_time_series(
  observation = c(100, 120, 130),
  time = as.Date(
    c("2023-01-01", "2023-01-08", "2023-01-15")
  ),
  time_interval = "week"
)

# Create a `tsd` object from monthly data
monthly_tsd <- to_time_series(
  observation = c(500, 520, 540),
  time = as.Date(
    c("2023-01-01", "2023-02-01", "2023-03-01")
  ),
  time_interval = "month"
)

Deprecated tsd function

Description

This function has been renamed to better reflect its purpose. Please use to_time_series() instead.

Usage

tsd(...)

Arguments