ringbp is an R package that provides methods to simulate infectious disease transmission in the presence of contact tracing. It was initially developed to support a paper written in early 2020 to assess the feasibility of controlling COVID-19. For more details on the methods implemented here, see the associated paper.
The current development version of ringbp can be installed from GitHub using the pak
package.
if(!require("pak")) install.packages("pak")
pak::pak("epiforecasts/ringbp")
The main functionality of the package is in the scenario_sim()
function. Here is an example for running 10 simulations of a given scenario:
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
All contributions to this project are gratefully acknowledged using the allcontributors
package following the all-contributors specification. Contributions of any kind are welcome!
seabbs, sbfnk, jhellewell14, timcdlucas, amygimma, joshwlambert, Bisaloo, actions-user