Simulates a time series of daily counts in the absence of outbreaks. The counts are drawn from a Poisson or negative binomial model following the approach of Noufaily et al. (2019). The baseline frequency, linear trend, seasonal pattern and day-of-the-week pattern are all controlled through the function arguments.
Usage
simulate_baseline_data(
start_date,
end_date,
seasonal_pattern_n,
weekly_pattern_n,
alpha,
beta,
gamma_1,
gamma_2,
gamma_3,
gamma_4,
phi,
shift_1
)Arguments
- start_date
Starting date of the simulation period. Date is in the format of 'yyyy-mm-dd'.
- end_date
Ending date of the simulation period. Date is in the format of 'yyyy-mm-dd'.
- seasonal_pattern_n
Number of seasonal patterns. For no seasonal pattern seasonal_pattern_n = 0. Seasonal_pattern_n = 1 represents annual pattern. Seasonal_pattern_n = 2 indicates biannual pattern.
- weekly_pattern_n
Number of weekly patterns. For no specific weekly pattern, weekly_pattern_n = 0. Weekly_pattern_n = 1 represents one weekly peak.
- alpha
The parameter is used to specify the baseline frequencies of reports.
- beta
The parameter is used to specify to specify linear trend.
- gamma_1
The parameter is used to specify the seasonal pattern.
- gamma_2
The parameter is used to specify the seasonal pattern.
- gamma_3
The parameter is used to specify day-of-the week pattern.
- gamma_4
The parameter is used to specify day-of-the week pattern.
- phi
Dispersion parameter. If phi =0, a Poisson model is used to simulate baseline data.
- shift_1
Horizontal shift parameter to help control over week/month peaks.
Value
A csfmt_rts_data_v1 (data.table) holding one row per day over the
simulation period, including the columns:
- date
Calendar date of the observation.
- wday
Day of the week.
- mu
Expected count from the baseline model.
- n
Simulated count.
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
)
print(baseline[, .(date, wday, mu, n)])
#> date wday mu n
#> <Date> <num> <num> <int>
#> 1: 2018-01-01 2 66.95830 64
#> 2: 2018-01-02 3 31.40319 32
#> 3: 2018-01-03 4 22.23175 19
#> 4: 2018-01-04 5 30.85457 37
#> 5: 2018-01-05 6 65.65786 72
#> ---
#> 726: 2019-12-27 6 64.73862 68
#> 727: 2019-12-28 7 119.96484 109
#> 728: 2019-12-29 1 121.67202 139
#> 729: 2019-12-30 2 66.95830 60
#> 730: 2019-12-31 3 31.40319 41