Temporal variation in transmission during the COVID-19 outbreak

For the underlying estimates see here. See here for documentation on the methodology used. See here for our data back-end.

Usage

Clone the website

git clone --depth 1 https://github.com/epiforecasts/covid.git

Update results

This repository uses Azure blob storage to store results. To download these first install azcopy (see here for a script to do this for Linux) and then run the following.

Rscript utils/update_estimates.R

Warning when run for the first time this will download several GB of data. See covid-rt-estimates for further support accessing estimates.

Update the website

Update

Update the website with the following:

bash bin/update_website.sh

See the bin folder for other updating scripts.

Docker

This analysis was developed in a docker container based on the rocker/geospatial docker image.

To build the docker image run (from the covid directory):

docker build . -t covid

To run the docker image run:

docker run -d -p 8787:8787 --name covid -e USER=covid -e PASSWORD=covid covid

The RStudio client can be found on port :8787 at your local machines ip. The default username:password is time_vary:time_vary, set the user with -e USER=username, and the password with - e PASSWORD=newpasswordhere. The default is to save the analysis files into the user directory.

To mount a folder (from your current working directory - here assumed to be tmp) in the docker container to your local system use the following in the above docker run command (as given mounts the whole covid directory to tmp).

--mount type=bind,source=$(pwd)/tmp,target=/home/covid

To access the command line run the following:

docker exec -ti covid bash