{ringbp} is an R package that provides methods to simulate infectious
disease transmission in the presence of contact tracing. It was based on
an Ebola transmission model with ring vaccination (Kucharski et al.
2016). The {ringbp} model was initially developed
to support a paper written in early 2020 to assess the feasibility of
controlling COVID-19 (Hellewell et al. 2020).
See the companion analysis code to reproduce Hellewell et al. (2020).
For more details on the methods implemented in the {ringbp} R package,
see the Hellewell et al. (2020)
paper, and the package
documentation.
The package can be installed from CRAN using
install.packages("ringbp")You can install the development version of {ringbp} from
GitHub with:
# check whether {pak} is installed
if(!require("pak")) install.packages("pak")
pak::pak("epiforecasts/ringbp")Alternatively, install pre-compiled binaries from the epiforecasts R-universe
install.packages("ringbp", repos = c("https://epiforecasts.r-universe.dev", "https://cloud.r-project.org"))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_traced = 0.2
),
## whether quarantine is in effect
interventions = intervention_opts(quarantine = FALSE),
sim = sim_opts(
## don't simulate beyond 140 days
cap_max_days = 140,
## 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) +
scale_y_continuous(name = "Cumulative number of cases") +
theme_bw()
extinct_prob(res)
#> Calculating extinction using the extinction status from the simulation.
#> [1] 0.6Contributions to {ringbp} are welcomed. Please follow the package
contributing
guide.
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
thimotei, adamkucharski, dcadam, jamesmbaazam
Please note that the {ringbp} project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
Hellewell, Joel, Sam Abbott, Amy Gimma, et al. 2020. “Feasibility of Controlling COVID-19 Outbreaks by Isolation of Cases and Contacts.” The Lancet Global Health 8 (4): e488–96. https://doi.org/10.1016/s2214-109x(20)30074-7.
Kucharski, Adam J., Rosalind M. Eggo, Conall H. Watson, Anton Camacho, Sebastian Funk, and W. John Edmunds. 2016. “Effectiveness of Ring Vaccination as Control Strategy for Ebola Virus Disease.” Emerging Infectious Diseases 22 (1): 105–8. https://doi.org/10.3201/eid2201.151410.