Move forward one generation in the branching process

Usage

outbreak_step(case_data, offspring, delays, event_probs, interventions)

Arguments

case_data: a data.table: cases in outbreak so far; initially generated by outbreak_setup()
offspring: a list with class <ringbp_offspring_opts>: the offspring distribution functions for the ringbp model, returned by offspring_opts(). Contains three elements: community, isolated, and asymptomatic
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
interventions: a list with class <ringbp_intervention_opts>: the intervention settings for the ringbp model, returned by intervention_opts(). Contains one element: quarantine

Value

A list with three elements:

$cases: a data.table with case data
$effective_r0: a numeric with the effective reproduction number
$cases_in_gen: a numeric with the number of new cases in that generation

Examples

offspring <- offspring_opts(
  community = \(n) rnbinom(n = n, mu = 2.5, size = 0.16),
  isolated = \(n) rnbinom(n = n, mu = 0, size = 1),
  asymptomatic = \(n) rnbinom(n = n, mu = 1.25, size = 0.16)
)
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
)
interventions <- intervention_opts(quarantine = FALSE)

# generate initial cases
case_data <- outbreak_setup(
  initial_cases = 5,
  delays = delays,
  event_probs = event_probs
)
case_data
#> Index: <asymptomatic>
#>    exposure asymptomatic caseid infector isolated missed     onset new_cases
#>       <num>       <lgcl>  <int>    <num>   <lgcl> <lgcl>     <num>    <lgcl>
#> 1:        0        FALSE      1        0    FALSE   TRUE  5.388645        NA
#> 2:        0        FALSE      2        0    FALSE   TRUE  3.260320        NA
#> 3:        0        FALSE      3        0    FALSE   TRUE 12.919082        NA
#> 4:        0        FALSE      4        0    FALSE   TRUE  6.080151        NA
#> 5:        0        FALSE      5        0    FALSE   TRUE  3.669456        NA
#>    isolated_time
#>            <num>
#> 1:      8.854622
#> 2:     11.988717
#> 3:     21.825460
#> 4:      6.251773
#> 5:      5.438428
# generate next generation of cases
out <- outbreak_step(
  case_data = case_data,
  offspring = offspring,
  delays = delays,
  event_probs = event_probs,
  interventions = interventions
)
case_data <- out[[1]]
case_data
#>     exposure asymptomatic caseid infector isolated missed     onset new_cases
#>        <num>       <lgcl>  <int>    <num>   <lgcl> <lgcl>     <num>     <num>
#> 1:  0.000000        FALSE      1        0     TRUE   TRUE  5.388645         0
#> 2:  0.000000        FALSE      2        0     TRUE   TRUE  3.260320         0
#> 3:  0.000000        FALSE      3        0     TRUE   TRUE 12.919082         1
#> 4:  0.000000        FALSE      4        0     TRUE   TRUE  6.080151         0
#> 5:  0.000000        FALSE      5        0     TRUE   TRUE  3.669456         1
#> 6: 12.554511        FALSE      6        3    FALSE   TRUE 18.124106        NA
#> 7:  4.489831        FALSE      7        5    FALSE   TRUE  7.724711        NA
#>    isolated_time
#>            <num>
#> 1:      8.854622
#> 2:     11.988717
#> 3:     21.825460
#> 4:      6.251773
#> 5:      5.438428
#> 6:     23.208225
#> 7:      8.588712