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; see the companion analysis code and paper for more details.
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.
Installation
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")Quick start
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
)
)Plot of weekly cases
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()
Estimate extinction probability
extinct_prob(res, cap_cases = 4500)
#> [1] 0.7Contributors
All contributions to this project are gratefully acknowledged using the allcontributors package following the all-contributors specification. Contributions of any kind are welcome!
Code
seabbs, sbfnk, jhellewell14, timcdlucas, amygimma, joshwlambert, Bisaloo, actions-user