- This repository is under active development and is subject to change as the analysis evolvesThis repositoty contains code for running branching process models of COVID-19. The code has diverged quite a lot from the original (repo)[https://github.com/epiforecasts/ringbp] and we are not trying to keep the two code bases in sync.
The main functionality added here is a model of testing that is used alongside the contat tracing.
Set your working directory to the home directory of this project (or use the provided Rstudio project). Install the analysis and all dependencies with:
remotes::install_github("timcdlucas/ringbp", dependencies = TRUE)Run a single scenario for a 100 simulations.
library(ringbp)
library(ggplot2)
res <- ringbp::scenario_sim(n.sim = 10, num.initial.cases = 1,prop.asym=0,
prop.ascertain = 0.2, cap_cases = 4500, cap_max_days = 350,
r0isolated = 0, r0community = 2.5, disp.com = 0.16, disp.iso = 1, delay_shape = 1.651524,
delay_scale = 4.287786,k = 0, quarantine = FALSE)
# Plot of weekly cases
ggplot2::ggplot(data=res, ggplot2::aes(x=week, y=cumulative, col = as.factor(sim))) +
ggplot2::geom_line(show.legend = FALSE, alpha=0.3) +
ggplot2::scale_y_continuous(name="Number of cases") +
ggplot2::theme_bw()
ringbp::extinct_prob(res,cap_cases = 4500)There are a number of scripts in inst/scripts that run the actual analyses.
I'll get round to adding descriptions of them at somepoint.
The original repo was used for the analysis that underlies Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
Our additions have been used for the paper An imperfect tool: COVID-19 'test & trace' success relies on minimising the impact of false negatives and continuation of physical distancing.. This paper used the code up to df7dc743 or this release. Any further changes to this analysis such as responded to peer review comments should happen in the branch imperfect_tool