`OpenABM-Covid19-model-paper`

Analysis repo to generate tables, figures for manuscript on the OpenABM-Covid19 model (Hinch, Probert et al., 2020).

Software requirements

To generate the data, one needs to install OpenABM-Covid19, the requirements for which are listed on the README.md of the OpenABM-Covid19 repo. For generation of the figures and tables in this repo, one needs Python>3.6 (other package requirements should be satisfied if OpenABM is already running) and R >3.4.

The scripts herein assume OpenABM-Covid19 is cloned as a subdirectory of this repo (or it could be pulled using git submodule, for instance).

model_dir="OpenABM-Covid19"
release="0.3"

cd OpenABM-Covid19-model-paper
git clone https://github.com/BDI-pathogens/OpenABM-Covid19.git "$model_dir"
(cd "$model_dir"; git checkout "$release")

We recommend running these analyses with a Python virtual environment. A virtual environment can be set up, activated, and prerequisites installed in the following manner:

python3 -m venv venv
source venv/bin/activate
pip install -r "$model_dir/tests/requirements.txt"
(cd $model_dir/src; make clean; make)

The virtual environment can be deactivated using deactivate.

Usage

make all: Will generate the simulated data, and generate all figures and tables used in the paper.
make all_output: Generate all figures and tables used in the paper (without generating the simulated data).

Additional commands

make data: Generate simulation data for a population of 1M with UK-like demographics and controls (self-isolation on symptoms, self-isolate on positive test result, lockdown when prevalence reaches 2% in the population).

All figures and tables can be generated individually in the following manner (after the data have been generated):

make figure1: Generate figure 1, etc
make figureS1: Generate figure S1, etc
make table1: Generate table 1, etc

Output figures

Figure 1

Figure 1 is a schematic of the networks used in the model.

Figure 2

Figure 2 is a composite figure, the subpanels are produced in this repository

Figure 3

Heatmap of transmission events between different age groups for different infectiousness states of the source of infection. Data is from a single simulation in a population of 1 million individuals with UK-like demographics and COVID19 control interventions.

Figure 4

Age-stratified infection fatality ratio as output from a single simulation in a population of 1 million with UK-like demography and control interventions.