generate_seasonal_data: Generate Simulated Data of Seasonal Waves as a 'tsd' object

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

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

years	An integer specifying the number of years of data to simulate.
start_date	A date representing the start date of the simulated data.
amplitude	A number specifying the amplitude of the seasonal wave. The output will fluctuate within the range ⁠[mean - amplitude, mean + amplitude]⁠.
mean	A number specifying the mean of the seasonal wave.
phase	A numeric value (in radians) representing the horizontal shift of the sine wave, hence the phase shift of the seasonal wave. The phase must be between zero and 2*pi.
trend_rate	A numeric value specifying the exponential growth/decay rate.
noise_overdispersion	A numeric value specifying the overdispersion of the generated data. 0 means deterministic, 1 is pure poisson and for values > 1 a negative binomial is assumed.
relative_epidemic_concentration	A numeric that transforms the reference sinusoidal season. A value of 1 gives the pure sinusoidal curve, and greater values concentrate the epidemic around the peak.
time_interval	A character vector specifying the time interval. Choose between 'day', 'week', or 'month'.
lower_bound	A numeric value that can be used to ensure that intensities are always greater than zero, which is needed when noise_overdispersion is different from zero.

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")

aedseo documentation built on April 12, 2025, 1:35 a.m.