View source: R/simulated-data.R
generate_snapshots | R Documentation |
Generates case data with snapshots of reported cases at each onset day
generate_snapshots(
days_total,
Rt_function,
serial_parameters,
reporting_parameters,
thinned = FALSE,
kappa = 1000,
days_max_follow_up = 30,
initial_parameters = list(mean = 30, length = 20),
serial_max = 40
)
days_total |
number of days to simulate process for (post-initial period) |
Rt_function |
takes day as an input and outputs an Rt value |
serial_parameters |
named list of 'mean' and 'sd' of gamma distribution characterising the serial interval distribution |
reporting_parameters |
either a named list of 'mean' and 'sd' of gamma distribution indicating a single reporting delay distribution for the whole period or a tibble with columns: 'time_onset', 'mean', 'sd' indicating the reporting delay distribution for each time period |
thinned |
a Boolean indicating whether to thin cases to only informative time points |
kappa |
over-dispersion parameter of negative binomial |
days_max_follow_up |
max days at which to simulate reporting case trajectory |
initial_parameters |
a named list of 'mean' and 'length' which is used to generate seed cases by sampling from a negative binomial distribution |
serial_max |
maximum point at which to truncate sum in renewal process |
a tibble with observed case trajectories for each time of onset
library(incidenceinflation)
# generate case series for 100 days with time-varying Rt
days_total <- 100
# make Rt interpolation function
v_Rt <- c(rep(1.3, 25), rep(1, 25), rep(2, 50))
Rt_function <- stats::approxfun(1:days_total, v_Rt)
# serial interval parameters
s_params <- list(mean=5, sd=1)
# renewal over-dispersion parameter
kappa <- 2
# reporting delays
r_params <- list(mean=10, sd=5)
reported_cases <- generate_snapshots(
days_total, Rt_function,
s_params, r_params)
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