Add seasonal outbreaks to simulated daily counts

Adds outbreaks inside a season window to the output of simulate_baseline_data(), after Noufaily et al. (2019). It adds n_season_outbreak outbreaks in each of a random number of years.

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

The output of simulate_baseline_data().

week_season_start

The ISO week that starts the season window.

week_season_peak

Not used: the start day is drawn with random weights, not near the peak.

week_season_end

The ISO week, in the next year, that ends the window.

n_season_outbreak

The number of outbreaks in each outbreak year.

m

The size factor of an outbreak.

Value

A copy of data with the cases added to n, and these columns:

• sd and weight,

• seasonal_outbreak: 1 on an outbreak day,

• seasonal_outbreak_n: the cases,

• seasonal_outbreak_n_rw: the weighted cases, which are added to n.

Details

The candidate years are those in calyear, except the last, and the function prints the years it picks. The window runs from ISO week week_season_start of a year to week_season_end of the next. An outbreak starts on a day of the window drawn with random weights.

An outbreak adds a Poisson number of cases with mean 10 * m * sd, where sd = sqrt(mu * phi) on the start day. The draw repeats until it is 2 or more, and it calls set.seed(), which resets the random stream. The cases spread over the next days by a lognormal delay. Each day is then weighted: 0.5 on Sunday, 2 on Friday and Saturday, and 1 on other days.

It needs calyear. Where calyear is NA, as from simulate_baseline_data() with cstidy 2026.8.21, it adds no outbreak.

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

vignette("csalert", package = "csalert"), which simulates a series with a known outbreak.

Other data simulation functions: add_holiday_effect(), simulate_baseline_data(), simulate_spike_outbreak_data()

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