Simulate baseline data ---- Simulation of baseline data.

This function simulates a time series of daily counts in the absence of outbreaks. Data is simulated using a poisson/negative binomial model as described in Noufaily et al. (2019). Properties of time series such as frequency of baseline observations, trend, seasonal and weekly pattern can be specified in the simulation.

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 containing a time series of counts

wday

day-of-the week

n

cases

Examples

baseline  <- simulate_baseline_data(
start_date = as.Date("2012-01-01"),
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 )