Set up initial cases for branching process

Usage

outbreak_setup(initial_cases, delays, event_probs)

Arguments

initial_cases: a non-negative integer scalar: number of initial or starting cases which are all assumed to be missed.
delays: a list with class <ringbp_delay_opts>: the delay distribution functions for the ringbp model, returned by delay_opts(). Contains two elements: incubation_period and onset_to_isolation
event_probs: a list with class <ringbp_event_prob_opts>: the event probabilities for the ringbp model, returned by event_prob_opts(). Contains three elements: asymptomatic, presymptomatic_transmission and symptomatic_ascertained

Value

data.table of cases in outbreak so far. data.table columns are:

$exposure: numeric
$asymptomatic: logical
$caseid: integer
$infector: numeric
$missed: logical
$onset: numeric
$new_cases: integer
$isolated_time: numeric
$sampled: logical

Examples

delays <- delay_opts(
  incubation_period = \(n) rweibull(n = n, shape = 2.32, scale = 6.49),
  onset_to_isolation = \(n) rweibull(n = n, shape = 1.65, scale = 4.28)
)
event_probs <- event_prob_opts(
  asymptomatic = 0,
  presymptomatic_transmission = 0.15,
  symptomatic_ascertained = 0
)

# generate initial cases
case_data <- outbreak_setup(
  initial_cases = 5,
  delays = delays,
  event_probs = event_probs
)
case_data
#> Index: <asymptomatic>
#>    exposure asymptomatic caseid infector missed    onset new_cases
#>       <num>       <lgcl>  <int>    <num> <lgcl>    <num>     <int>
#> 1:        0        FALSE      1        0   TRUE 5.494867        NA
#> 2:        0        FALSE      2        0   TRUE 4.273768        NA
#> 3:        0        FALSE      3        0   TRUE 9.144926        NA
#> 4:        0        FALSE      4        0   TRUE 4.549269        NA
#> 5:        0        FALSE      5        0   TRUE 5.327302        NA
#>    isolated_time sampled
#>            <num>  <lgcl>
#> 1:     13.630713   FALSE
#> 2:     12.000550   FALSE
#> 3:      9.839796   FALSE
#> 4:      7.850404   FALSE
#> 5:      7.479154   FALSE