seasonal_onset: Automated and Early Detection of Seasonal Epidemic Onset

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

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

tsd	An object containing time series data with 'time' and 'observation.'
k	An integer specifying the window size for modeling growth rates for the onset.
level	The confidence level for onset parameter estimates, a numeric value between 0 and 1.
disease_threshold	An integer specifying the threshold for considering a disease outbreak. It defines the per time-step disease threshold that has to be surpassed to possibly trigger a seasonal onset alarm. If the total number of cases in a window of size k exceeds disease_threshold * k, a seasonal onset alarm can be triggered.
family	A character string specifying the family for modeling
na_fraction_allowed	Numeric value between 0 and 1 specifying the fraction of observables in the window of size k that are allowed to be NA or zero, i.e. without cases, in onset calculations.
season_start, season_end	Integers giving the start and end weeks of the seasons to stratify the observations by. If set to NULL, it means no stratification by season.
only_current_season	Should the output only include results for the current season?

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
)

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