Robert J. Fischer(1*), Dylan H. Morris(2), Neeltje van Doremalen(1), Shanda Sarchette(1), M. Jeremiah Matson(1), Claude Kwe Yinda(1), Stephanie N. Seifert(1), Amandine Gamble(3), Brandi N. Williamson(1), Seth D. Judson(4), Emmie de Wit1, James O. Lloyd-Smith3, Vincent J. Munster1
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
- Dept. of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Dept. of Ecology & Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Dept. of Medecine, University of Washington, Seattle, WA, USA.
This repository accompanies the article "Effectiveness of N95 Respirator Decontamination and Reuse against SARS-CoV-2 Virus" (R.J. Fischer et al.). It provides code and data for reproducing all data analysis from the paper and recreating all associated display figures.
This repository was last updated March 2021 to add bibliographic information for the published article and to ensure that plotting code exactly matches published figure captions. This results in a cosmetic change to Appendix Figure 1 relative to the published version. There are no other changes, and no conclusions change.
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:
Fischer RJ, Morris DH, van Doremalen N, Sarchette S, Matson M, Bushmaker T, et al. Effectiveness of N95 Respirator Decontamination and Reuse against SARS-CoV-2 Virus. Emerg Infect Dis. 2020;26(9):2253-2255. https://dx.doi.org/10.3201/eid2609.201524
Bibtex record:
@electronic{fischer2020n95,
title={Effectiveness of N95 respirator decontamination and reuse against SARS-CoV-2 virus},
author={Fischer, Robert J and
Morris, Dylan H and
van Doremalen, Neeltje and
Sarchette, Shanda and
Matson, M Jeremiah and
Bushmaker, Trenton and
Yinda, Claude Kwe and
Seifert, Stephanie N and
Gamble, Amandine and
Williamson, Brandi N and
Judson, Seth D and
de Wit, Emmie and
Lloyd-Smith, James O and
Munster, Vincent J},
journal={Emerging infectious diseases},
volume={26},
number={9},
pages={2253},
year={2020},
DOI = {10.3201/eid2609.201524}
URL = {https://github.com/dylanhmorris/n95-decontamination}
}
The coronavirus pandemic has created worldwide shortages of N95 respirators. We analyzed 4 decontamination methods for effectiveness in deactivating severe acute respiratory syndrome coronavirus 2 virus and effect on respirator function. Our results indicate that N95 respirators can be decontaminated and reused, but the integrity of respirator fit and seal must be maintained.
src: all code, including data preprocessing, Bayesian model definition and fitting, and results post-processing and figure generation:src/parameters: parameters specification for models and style specification for plots
dat: data files in comma-separated values (.csv) formatsdat/raw: raw data files (semicolon-separated)dat/cleaned: data files processed and prepared for model fittingdat/fonts: freely available fonts for figures
out: output filesout/mcmc_chains: Markov Chain Monte Carlo (MCMC) output, as serialized R data (.Rds) files.out/figures: figures generated from resultsout/tables: tables generated from resultsout/chain_diagnostics.csv: diagnostic tests for MCMC convergence.
A guide to reproducing the analysis from the paper follows.
First download this repository. The recommended way is to git clone it from the command line:
git clone https://github.com/dylanhmorris/n95-decontamination.git
Downloading it manually via Github's download button or from OSF 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 files found in the directory, src/parameters.
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.