Run a single instance of the branching process model
outbreak_model(
initial_cases,
offspring,
delays,
event_probs,
interventions,
sim
)a non-negative integer scalar: number of initial
or starting cases which are all assumed to be missed.
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
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
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
a list with class <ringbp_intervention_opts>:
the intervention settings for the ringbp model, returned by
intervention_opts(). Contains one element: quarantine
a list with class <ringbp_sim_opts>: the simulation control
options for the ringbp model, returned by sim_opts()
data.table of cases by week, cumulative cases, and the effective
reproduction number of the outbreak. data.table columns are:
$week: numeric
$weekly_cases: numeric
$cumulative: numeric
$effective_r0: numeric
$cases_per_gen: list
set.seed(1)
offspring <- offspring_opts(
community = \(n) rnbinom(n = n, mu = 2.5, size = 0.16),
isolated = \(n) rnbinom(n = n, mu = 0.5, 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 = 1.65, scale = 4.28)
)
event_probs <- event_prob_opts(
asymptomatic = 0,
presymptomatic_transmission = 0.5,
symptomatic_ascertained = 0.2
)
interventions <- intervention_opts(quarantine = FALSE)
out <- outbreak_model(
initial_cases = 1,
offspring = offspring,
delays = delays,
event_probs = event_probs,
interventions = interventions,
sim = sim_opts()
)
out
#> week weekly_cases cumulative effective_r0 cases_per_gen
#> <num> <num> <num> <num> <list>
#> 1: 0 1 1 0 0
#> 2: 1 0 1 0 0
#> 3: 2 0 1 0 0
#> 4: 3 0 1 0 0
#> 5: 4 0 1 0 0
#> 6: 5 0 1 0 0
#> 7: 6 0 1 0 0
#> 8: 7 0 1 0 0
#> 9: 8 0 1 0 0
#> 10: 9 0 1 0 0
#> 11: 10 0 1 0 0
#> 12: 11 0 1 0 0
#> 13: 12 0 1 0 0
#> 14: 13 0 1 0 0
#> 15: 14 0 1 0 0
#> 16: 15 0 1 0 0
#> 17: 16 0 1 0 0
#> 18: 17 0 1 0 0
#> 19: 18 0 1 0 0
#> 20: 19 0 1 0 0
#> 21: 20 0 1 0 0
#> 22: 21 0 1 0 0
#> 23: 22 0 1 0 0
#> 24: 23 0 1 0 0
#> 25: 24 0 1 0 0
#> 26: 25 0 1 0 0
#> 27: 26 0 1 0 0
#> 28: 27 0 1 0 0
#> 29: 28 0 1 0 0
#> 30: 29 0 1 0 0
#> 31: 30 0 1 0 0
#> 32: 31 0 1 0 0
#> 33: 32 0 1 0 0
#> 34: 33 0 1 0 0
#> 35: 34 0 1 0 0
#> 36: 35 0 1 0 0
#> 37: 36 0 1 0 0
#> 38: 37 0 1 0 0
#> 39: 38 0 1 0 0
#> 40: 39 0 1 0 0
#> 41: 40 0 1 0 0
#> 42: 41 0 1 0 0
#> 43: 42 0 1 0 0
#> 44: 43 0 1 0 0
#> 45: 44 0 1 0 0
#> 46: 45 0 1 0 0
#> 47: 46 0 1 0 0
#> 48: 47 0 1 0 0
#> 49: 48 0 1 0 0
#> 50: 49 0 1 0 0
#> 51: 50 0 1 0 0
#> week weekly_cases cumulative effective_r0 cases_per_gen