Neeltje van Doremalen(1*), Trenton Bushmaker(1*), Dylan H. Morris(2*), Myndi G. Holbrook(1), Amandine Gamble(3), Brandi N. Williamson(1), Natalie J. Thornburg(4), Susan I. Gerber(4), James O. Lloyd-Smith(3,5), Emmie de Wit(1), Vincent J. Munster(1)
* These authors contributed equally
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
- Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Dept. of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
This repository accompanies the article "Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1" (N. van Doremalen et al). It provides code for reproducing Bayesian data analysis from the paper and recreating the associated display figures.
A novel human coronavirus that is now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly called HCoV-19) emerged in Wuhan, China, in late 2019 and is now causing a pandemic. We analyzed the aerosol and surface stability of SARS-CoV-2 and compared it with SARS-CoV-1, the most closely related human coronavirus. We evaluated the stability of SARS-CoV-2 and SARS-CoV-1 in aerosols and on various surfaces and estimated their decay rates using a Bayesian regression model.
If you use the code or data provided here, please make sure to do so in light of the project license and please cite our work as below:
N. van Doremalen, T. Bushmaker, D.H. Morris et. al. "Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1". The New England Journal of Medicine (March 2020). https://doi.org/10.1056/NEJMc2004973
Bibtex record:
@article{vandoremalen2020stability,
author = {van Doremalen, Neeltje and Bushmaker, Trenton and Morris, Dylan H. and Holbrook, Myndi G. and Gamble, Amandine and Williamson, Brandi N. and Tamin, Azaibi and Harcourt, Jennifer L. and Thornburg, Natalie J. and Gerber, Susan I. and Lloyd-Smith, James O. and de Wit, Emmie and Munster, Vincent J.},
title = {Aerosol and Surface Stability of {SARS-CoV-2} as Compared with {SARS-CoV-1}},
journal = {New England Journal of Medicine},
volume = {0},
number = {0},
pages = {null},
day = {17},
month = {03},
year = {2020},
doi = {10.1056/NEJMc2004973}
}
src: all code, including data preprocessing, Bayesian model definition and fitting, and results post-processing and figure generation:dat: data files in comma-separated values (.csv) formatsdat/raw: raw data filesdat/cleaned: data files processed and prepared for model fitting
out: output filesout/mcmc_chains: Markov Chain Monte Carlo (MCMC) output, as serialized R data (.Rds) files.out/figures: figures generated from resultsout/chain_diagnostics.csv: diagnostic tests for MCMC convergence.
A guide to reproducing the analysis from the paper follows. If you encounter issues, see the Troubleshooting section at the end of this README.
Note that there may be minor, non-qualitative differences in MCMC output due to differences in pseudorandom number generation.
First download this repository. The recommended way is to git clone it from the command line:
git clone https://github.com/dylanhmorris/sars-cov-2-stability.git
Downloading it manually via Github's download button should also work.
The analysis can be auto-run from the project Makefile, but you may need to install some external dependencies first. See the Dependency installation guide below for a complete walkthrough. In the first instance, you'll need a working installation of the statistical programming language R, a working C++ compiler, and a working installation of Gnu Make or similar. A few external R packages can then be installed from the command line by typing.
make depend
from within the project directory.
The simplest approach is simply to type make at the command line, which should produce a full set of figures and MCMC output (saved as R Dataset .Rds files in the out/mcmc-chains/ directory as <model_name>_chains.Rds). These can be loaded in any working R installation, as long as the package rstan is also installed.
If you want to do things piecewise, typing make <filename> for any of the files listed in the dat/cleaned or out directories below should run the steps needed to produce that file.
Some shortcuts are available:
make dataproduces cleaned data files.make chainsproduces all MCMC outputmake diagnosticsextracts MCMC diagnostic statisticsmake figuresproduces all figuresmake tablesproduces all tablesmake cleanremoves all generated files, leaving only source code (though it does not uninstall packages)
Examining the raw Stan code is the place to start to understand how models have been specified. But note that parameters for the prior distributions are set at runtime rather than hard-coded into the .stan files, so that recompilation is not required when parameter choices are changed (this makes it easier to try the models using different priors, for sensitivity analysis).
Prior parameter choices are specified in the model fitting file, src/fit_stan_model.R.
Once the full analysis has been run, you should be able to find a full set of figures in out/figures and a table of regression results in out/tables.
You will need a working R installation with the command line interpreter Rscript (macOS and Linux) or Rscript.exe (Windows). On mac and Linux, you can check that you have an accessible Rscript by typing which Rscriptat the command line and seeing if one is found.
If you do not have an R installation, you can install it from the R project website or from the command line using a package manager such as Homebrew on macOS or apt-get on Linux. macOS users may also need to install the macOS "command line tools" by typing xcode-select --install at a command prompt.
Once R is installed, you can automatically install all other dependencies (including the Hamiltonian Monte Carlo software Stan and its R interface rstan) on most systems using make. In the top level project directory, type the following at the command line:
make depend
Alternatively, you can run the script src/install_needed_packages.R manually.
Note that installing Stan and RStan can be time-consuming, Stan is a large program that must be compiled from source. Some of the packages in the very valuable tidyverse may also take some time to install.