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Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19

Disclaimer: currently undergoing changes due to incorporating reviewer comments. Please get in touch if you have any issues.

This repository contains code for simulating COVID-19 dynamics in a range of scenarios across a real-world social network. The epidemic model is based conceptually on a branching-process model of contact-tracing and COVID-19, which can be accessed here.

Abstract

Case isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 in a real-world social network generated from high resolution GPS data3,4. We found that tracing contacts-of-contacts reduced the size of simulated outbreaks more than tracing of only contacts, but resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals led to increases in outbreak size, suggesting that contact tracing and quarantine may be most effective when it acts as a ‘local lockdown’ when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our approach highlights the importance of network structure and social dynamics in evaluating the potential impact of SARS-CoV-2 control.

Usage

Set up

Set your working directory to the parent directory of the project folder. Install the analysis and all dependencies with:

devtools::install("covidhm", dependencies = TRUE) #or whatever your folder name is

Run a single scenario and plot a network

Run a single instance of the outbreak model over 20 days and overlay the infection data onto a network. See ?plot_network for details of parameters

library(covidhm)

#Load association matrices
load("data-raw/am_list.RData")

#First item in the list is data across all days
m <- am_list[[1]]

#Plot network
plot_network(
am = m,
day = 20,
num.initial.cases = 1,
prop.asym = 0.4,
delay_shape =  1,
delay_scale = 1.4,
prop.ascertain = 0.9,
presymrate = 0.2,
R = 0.8,
outside = 0.001,
testing = FALSE,
s = 333,
isolation = FALSE,
secondary = FALSE,
tracing = FALSE,
quarantine = FALSE)

Run a single scenario multiple times

Run a single scenario for 10 simulations. Use ?scenario_sim for an explanation of parameters.

library(covidhm)
library(ggplot2)

res <- scenario_sim(net = m, n.sim = 10, num.initial.cases = 1,prop.asym=0.4,
                             prop.ascertain = 0.9, cap_max_days = 70,
                             delay_shape = 1, delay_scale = 1.4, R = 0.8, presymrate = 0.2, scenario = "nothing",
                             testing = FALSE, outside = 0.001, distancing = 0)

# Plot of raw cumulative cases
ggplot(data=res, aes(x=week, y=cumcases,col = sim)) +
geom_line(show.legend = FALSE, alpha=0.6, aes(group = sim)) +
scale_y_continuous(name="Weekly number of cases") +
  theme_bw()

Run the full analysis

Run the analyses in the terminal with the following commands (NB - these take several hours):

Rscript inst/scripts/scenarios.R
Rscript inst/scripts/network.R
Rscript inst/scripts/distancing.R
Rscript inst/scripts/outside.R
Rscript inst/scripts/testing.R
Rscript inst/scripts/sensitivity_testing.R

Generate figures

Render figures with the following:

Rscript inst/scripts/figures.R