if(!require("pak")) install.packages("pak")
pak::pak("epiforecasts/ringbp")

library("ringbp")
library("ggplot2")

res <- scenario_sim(
  n = 10, ## 10 simulations
  initial_cases = 1, ## one initial case in each of the simulations 
  offspring = offspring_opts(
    ## non-isolated individuals have R0 of 2.5 and a dispersion parameter
    community = \(n) rnbinom(n = n, mu = 2.5, size = 0.16), 
    ## isolated individuals have R0 of 0.5 and a dispersion  parameter
    isolated = \(n) rnbinom(n = n, mu = 0.5, size = 1)
    ## by default asymptomatic individuals are assumed to have the same R0 
    ## and dispersion as non-isolated individuals
  ), 
  delays = delay_opts(
    incubation_period = \(x) stats::rweibull(n = x, shape = 2.322737, scale = 6.492272), 
    onset_to_isolation = \(x) stats::rweibull(n = x, shape = 1.651524, scale = 4.287786)
  ),
  event_probs = event_prob_opts(
    ## 10% asymptomatic infections
    asymptomatic = 0.1, 
    ## 50% probability of onward infection time being before symptom onset
    presymptomatic_transmission = 0.5,
    ## 20% probability of ascertainment by contact tracing
    symptomatic_ascertained = 0.2
  ),
  ## whether quarantine is in effect
  interventions = intervention_opts(quarantine = FALSE),
  sim = sim_opts(
    ## don't simulate beyond 350 days
    cap_max_days = 350, 
    ## don't simulate beyond 4500 infections
    cap_cases = 4500
  )
)

ggplot(
  data = res, aes(x = week, y = cumulative, col = as.factor(sim))
) +
  geom_line(show.legend = FALSE, alpha = 0.3) +
  scale_y_continuous(name = "Cumulative number of cases") +
  theme_bw()

extinct_prob(res, cap_cases = 4500)
#> [1] 0.7