Run a specified number of simulations with identical parameters

Usage

scenario_sim(
  n,
  initial_cases,
  offspring,
  delays,
  event_probs,
  interventions,
  sim
)

Arguments

n: a positive integer scalar: number of simulations to run
initial_cases: a non-negative integer scalar: number of initial or starting cases which are all assumed to be missed by contact tracing (i.e. tracing ascertainment = 0).
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 4 elements: incubation_period, onset_to_isolation, latent_period and onset_to_self_isolation
event_probs: a list with class <ringbp_event_prob_opts>: the event probabilities for the ringbp model, returned by event_prob_opts(). Contains 5 elements: asymptomatic, presymptomatic_transmission, alpha, symptomatic_traced and symptomatic_self_isolate
interventions: a list with class <ringbp_intervention_opts>: the intervention settings for the ringbp model, returned by intervention_opts(). Contains 2 elements: quarantine and test_sensitivity
sim: a list with class <ringbp_sim_opts>: the simulation control options for the ringbp model, returned by sim_opts()

Value

A data.table object returning the results for multiple simulations using the same set of parameters. The table has columns

week: The week in the simulation.
weekly_cases: The number of new cases that week.
cumulative: The cumulative cases.
effective_r0: The effective reproduction rate for the whole simulation
cases_per_gen: A list column with the cases per generation. This is repeated each row.
sim: Index column for which simulation.

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 = 2.5, 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 = 2.5, scale = 5)
)
event_probs <- event_prob_opts(
  asymptomatic = 0,
  presymptomatic_transmission = 0.3,
  symptomatic_traced = 0
)
interventions <- intervention_opts(quarantine = TRUE)
sim <- sim_opts(
  cap_max_days = 365,
  cap_cases = 2000
)
res <- scenario_sim(
  n = 5,
  initial_cases = 5,
  offspring = offspring,
  delays = delays,
  event_probs = event_probs,
  interventions = interventions,
  sim = sim
)
res
#>        sim  week weekly_cases cumulative effective_r0
#>      <int> <int>        <int>      <int>        <num>
#>   1:     1     0            4          4     2.820762
#>   2:     1     1           17         21     2.820762
#>   3:     1     2           36         57     2.820762
#>   4:     1     3          198        255     2.820762
#>   5:     1     4          488        743     2.820762
#>  ---                                                 
#> 261:     5    48            0       2860     2.609450
#> 262:     5    49            0       2860     2.609450
#> 263:     5    50            0       2860     2.609450
#> 264:     5    51            0       2860     2.609450
#> 265:     5    52            0       2860     2.609450
#>                       cases_per_gen
#>                              <list>
#>   1:    18,  30, 138, 382, 832,1757
#>   2:    18,  30, 138, 382, 832,1757
#>   3:    18,  30, 138, 382, 832,1757
#>   4:    18,  30, 138, 382, 832,1757
#>   5:    18,  30, 138, 382, 832,1757
#>  ---                               
#> 261:  33, 43, 96,136,332,700,...[7]
#> 262:  33, 43, 96,136,332,700,...[7]
#> 263:  33, 43, 96,136,332,700,...[7]
#> 264:  33, 43, 96,136,332,700,...[7]
#> 265:  33, 43, 96,136,332,700,...[7]