covid19model

Code for modelling estimated deaths and cases for COVID19 from Report 13 published by MRC Centre for Global Infectious Disease Analysis, Imperial College London: Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries

Version 3 Release

In this update, we first extended our model from version 2 to have 'partial-pooling' for lockdown across all countries. This means now we have a global effect of lockdown along with each country having its own different lockdown effect. We also made our code modular, stan code faster (with help from the community) and now we create CSV outputs too for usage.

You can directly get csv files here and new model description here

Notice

Python code is right now not updated and won't work. Python code is good for only version 1 model and data.
base_general.r and base_general.stan, base_general_speed.stan and base_general_speed2.stan are now valid models for only version2

Version 2 Release

In this update we extend our original model to include (a) population saturation effects, (b) prior uncertainty on the infection fatality ratio and (c) a more balanced prior on intervention effects. We also (d) included another 3 countries (Greece, the Netherlands and Portugal). The updated technical detail is available here.

You can directly look at our results here

This repository has code for replication purposes. The bleeding edge code and advancements are done in a private repository. Ask report authors for any collaborations.

Contributing

We welcome all potential collaborators and contributors from the wider community. Please see contributing for more details.

Installing dependencies

Using Conda

An environment.yml file is provided and can be used to build a virtual environment containing all model dependencies. Create the environment using:

conda env create -f environment.yml

Then activate the environment for use:

conda activate covid19model

Using Docker

A Docker image providing all model dependencies is available. See docker/README.md for details of running the model with Docker.

Other

If you wish to install packages into your native R environment or with a system package manager please see environment.yml for a full list of dependencies.

How to run the code

There are two ways to run our code:-

Open the rstudio project covid19model.Rproj file in rstudio and run/source base.r file
To run from commandline please enter the cloned directory and type Rscript base.r base in terminal

Please note to not make you wait for long we have by default set run sampling to a short period. For proper estimates please run it in FULL mode either by setting the flag --full or the environment variable FULL=TRUE. This will run sampling for 4000 iterations with 2000 warmups and 4 chains. The run time for 14 countries using new faster code is around 50 mins/1hr for the version 3 code.

Run mode settings

Three different run modes are supported:

DEBUG which can either be enabled by setting the flag --debug when running the base.r file as such:
- Rscript base.r base --debug or by setting the environment variable DEBUG to TRUE.
DEFAULT which will run if neither full nor debug are set. Please note that for proper estimates FULL should always be set.
FULL which must always be used if you want to obtain reliable results and can be enabled by setting the flag --full on the command line:
- Rscript base.r base --full or by setting the environment variable FULL to TRUE.

Results

The results are stored in two folders results and figures.
Results has the stored stan fits and data used for plotting
Figures have the images with daily cases, daily death and Rt for all countries.

avalind / covid19model