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Add seasonal outbreaks to simulated data

Source: R/simulation_data_baseline.R
simulate_seasonal_outbreak_data.Rd

Adds seasonal outbreaks to a simulated baseline time series, for syndromes or diseases that follow seasonal trends. Seasonal outbreaks vary more in size and timing than the underlying seasonal pattern. The number of outbreaks per affected year is set by n_season_outbreak, and week_season_start to week_season_end define the season window. The outbreak start is drawn from the season window, with a higher probability near the peak (week_season_peak). The outbreak size (the excess number of cases) is drawn from a Poisson distribution following Noufaily et al. (2019).

Usage

simulate_seasonal_outbreak_data(
  data,
  week_season_start = 40,
  week_season_peak = 4,
  week_season_end = 20,
  n_season_outbreak = 1,
  m = 50
)

Arguments

data

A csfmt_rts_data_v1 data object, typically the output of simulate_baseline_data.

week_season_start

Starting season week number.

week_season_peak

Peak of the season week number.

week_season_end

Ending season week number.

n_season_outbreak

Number of seasonal outbreaks to be simulated.

m

Parameter to determine the size of the outbreak (m times the standard deviation of the baseline count at the starting day of the seasonal outbreak).

Value

A csfmt_rts_data_v1 (data.table) equal to data with the simulated seasonal outbreak counts added to column n and additional columns describing the outbreaks (e.g. seasonal_outbreak, seasonal_outbreak_n).

References

Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An improved algorithm for outbreak detection in multiple surveillance systems. Statistics in Medicine. 2013.

See also

Neither package vignette covers the data simulators. Use them to generate a series whose truth you already know, then run short_term_trend or signal_detection_hlm on it.

Examples

library(data.table)
set.seed(4)
baseline <- simulate_baseline_data(
  start_date = as.Date("2018-01-01"),
  end_date = as.Date("2019-12-31"),
  seasonal_pattern_n = 1,
  weekly_pattern_n = 1,
  alpha = 3,
  beta = 0,
  gamma_1 = 0.8,
  gamma_2 = 0.6,
  gamma_3 = 0.8,
  gamma_4 = 0.4,
  phi = 4,
  shift_1 = 29
)
d <- simulate_seasonal_outbreak_data(
  baseline,
  week_season_start = 40,
  week_season_peak = 4,
  week_season_end = 20,
  n_season_outbreak = 1
)
#> integer(0)
print(d[, .(date, n, seasonal_outbreak, seasonal_outbreak_n)])
#>            date     n seasonal_outbreak seasonal_outbreak_n
#>          <Date> <num>             <num>               <num>
#>   1: 2018-01-01    64                 0                   0
#>   2: 2018-01-02    32                 0                   0
#>   3: 2018-01-03    19                 0                   0
#>   4: 2018-01-04    37                 0                   0
#>   5: 2018-01-05    72                 0                   0
#>  ---                                                       
#> 726: 2019-12-27    68                 0                   0
#> 727: 2019-12-28   109                 0                   0
#> 728: 2019-12-29   139                 0                   0
#> 729: 2019-12-30    60                 0                   0
#> 730: 2019-12-31    41                 0                   0