Generate an outbreak case count series defined by growth rates using a poisson model.

Usage

sim_poisson_model(
  changes = tibble::tibble(t = c(0, 20, 40, 60, 80), growth = c(0.1, 0, -0.1, 0, 0.1)),
  kappa = 1,
  max_time = 104,
  seed = Sys.time(),
  fn_growth = cfg_step_fn(changes),
  fn_imports = ~ifelse(.x == 0, 100, 0),
  time_unit = "1 day"
)

Arguments

changes: a dataframe holding change time points (t) and growth rate per week (growth) columns
kappa: a dispersion parameter. 1 is no dispersion (compared to poisson), smaller values mean more dispersion.
max_time: the desired length of the time series,
seed: a random seed
fn_growth: a function that takes input vector t and returns the growth rates at times t
fn_imports: a function that takes input vector t and returns the number of imported cases at times t.
time_unit: e.g. a daily or weekly time series: "1 day", "7 days"

Value

A dataframe containing the following columns:

count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a time_period

Ungrouped.

Examples


tmp2 = sim_poisson_model(seed=100, fn_imports = ~ ifelse(.x %in% c(0,50),100,0))

if (interactive()) {
  ggplot2::ggplot(tmp2)+ggplot2::geom_point(ggplot2::aes(x=time,y=count))
}