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Add spiked outbreaks to simulated data

Source: R/simulation_data_baseline.R
simulate_spike_outbreak_data.Rd

Adds spiked outbreaks to a simulated baseline time series, following Noufaily et al. (2019). The method is similar to simulate_seasonal_outbreak_data. The outbreaks are shorter in duration, and are added only within the last year of data (the prediction period). A spiked outbreak can start at any week during that period.

Usage

simulate_spike_outbreak_data(data, n_sp_outbreak = 1, m)

Arguments

data

A csfmt_rts_data_v1 data object, typically the output of simulate_baseline_data.

n_sp_outbreak

Number of spiked 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 spiked outbreak).

Value

A csfmt_rts_data_v1 (data.table) equal to data with the simulated spiked outbreak counts added to column n and additional columns describing the outbreaks (e.g. sp_outbreak, sp_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_spike_outbreak_data(
  baseline,
  n_sp_outbreak = 1,
  m = 2
)
print(d[, .(date, n, sp_outbreak, sp_outbreak_n)])
#>            date     n sp_outbreak sp_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