dot-generate_delay_data: Generate artificial delay data.

In covid-19-Re/estimateR: Reproductive number estimation from delayed observations of infections

Generate artificial delay data.

Description

This utility can be used to build toy examples to test functions dealing with empirical delay data. It is very basic in what it simulates. A random walk is simulated over n_time_steps, representing the incidence through time. The result of this simulation is offset so that all values are positive. Then, for each time step, n samples from a delay distribution are taken, with n being the incidence value at this time step. The random draws are then multiplied by a factor (>1 or <1) to simulate a gradual shift in the delay distribution through time. This multiplication factor is calculated by linearly interpolating between 1 (at the first time step), and delay_ratio_start_to_end linearly, from 1 at the first time step to ratio_delay_end_to_start at the last time step.

Usage

.generate_delay_data(
  origin_date = as.Date("2020-02-01"),
  n_time_steps = 100,
  time_step = "day",
  ratio_delay_end_to_start = 2,
  distribution_initial_delay = list(name = "gamma", shape = 6, scale = 5),
  seed = NULL
)

Arguments

origin_date	Date of first infection.
n_time_steps	interger. Number of time steps to generate delays over
time_step	string. Time between two consecutive incidence datapoints. "day", "2 days", "week", "year"... (see seq.Date for details)
ratio_delay_end_to_start	numeric value. Shift in delay distribution from start to end.
distribution_initial_delay	Distribution in list format.
seed	integer. Optional RNG seed.

Value

dataframe. Simulated delay data.

covid-19-Re/estimateR documentation built on Sept. 14, 2024, 5:49 a.m.