Negative binomial branching process model

Negative binomial branching process model

run_sim(
  n,
  n_length,
  mean_si,
  sd_si,
  serial_fn = NULL,
  R0,
  k = 0.16,
  tf = 37,
  kept_times = NULL,
  max_potential_cases,
  delay_sample
)

Arguments

n	Numeric, number of initial cases
n_length	Numeric, the number of days in the initial seeding event
mean_si	Numeric, the mean of the serial interval
sd_si	Numeric, the sd of the serial interval
serial_fn	Function from which to sample the serial interval must accepted a number of samples argument followed by a mean and standard deviation. If missing defaults to a normal distribution.
R0	Numeric, the estimated reproduction number
k	Numeric, the dispersion of the negative binomial branching process
tf	Numeric, the end time of the branching process
kept_times	Numeric, a vector of timepoints to keep information on. Defaults to all time points
max_potential_cases	Numeric, the maximum number of cases
delay_sample	A function to sample from reporting delays

Value

A dataframe containing the simulation time and outbreak size

Examples

## Example

run_sim(n = 1, n_length = 7, mean_si = 5, sd_si = 2, R0 = 2,
        k=0.16, tf=37, max_potential_cases= 100,
        delay_sample = function(x) {rnorm(x, 6, 1)})
#>    time size
#> 1     0    0
#> 2     1    0
#> 3     2    0
#> 4     3    0
#> 5     4    0
#> 6     5    0
#> 7     6    0
#> 8     7    0
#> 9     8    0
#> 10    9    1
#> 11   10    1
#> 12   11    1
#> 13   12    1
#> 14   13    1
#> 15   14    2
#> 16   15    3
#> 17   16    9
#> 18   17   13
#> 19   18   14
#> 20   19   22
#> 21   20   28
#> 22   21   41
#> 23   22   46
#> 24   23   57
#> 25   24   62
#> 26   25   66
#> 27   26   71
#> 28   27   76
#> 29   28   83
#> 30   29   88
#> 31   30   96
#> 32   31  102
#> 33   32  106
#> 34   33  108
#> 35   34  113
#> 36   35  118
#> 37   36  120
#> 38   37  123

## Code
run_sim
#> function (n, n_length, mean_si, sd_si, serial_fn = NULL, R0, 
#>     k = 0.16, tf = 37, kept_times = NULL, max_potential_cases, 
#>     delay_sample) 
#> {
#>     if (is.null(kept_times)) {
#>         kept_times <- 0:tf
#>     }
#>     if (is.null(serial_fn)) {
#>         serial_fn <- stats::rnorm
#>     }
#>     t0 <- sample(seq(1, n_length), n, replace = TRUE)
#>     sim <- bpmodels::chain_sim(n, "nbinom", serial = function(x) {
#>         serial_fn(x, mean_si, sd_si)
#>     }, mu = R0, size = k, t0 = t0, tf = tf, infinite = max_potential_cases)
#>     sim$time <- sim$time + delay_sample(length(sim$time))
#>     sim <- sim[sim$time < tf, ]
#>     size <- data.frame(time = kept_times, size = unlist(lapply(kept_times, 
#>         function(x) {
#>             tmp <- sim[sim$time < x, ]
#>             nrow(tmp)
#>         })))
#>     return(size)
#> }
#> <bytecode: 0x55a6b5ec23d0>
#> <environment: namespace:WuhanSeedingVsTransmission>