This repository contains all code for the Bayesian counterfactual analysis of potential MERS-CoV vaccine impact, developed by Daniel J. Laydon, and Neil M. Ferguson of the MRC Centre for Global Infectious Disease Analysis at Imperial College, London, and Simon Cauchemez of the Institut Pasteur, Paris. The model assesses vaccine impact by inferring transmission trees, ("who-infected-whom") analysis, and then "pruning" these trees to generate counterfactuals. The repository also contains code to assess and compare various vaccination campaign strategies. Full details are available at https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(23)00117-1/fulltext.
The model is written in C++. Source and header files are located in the subdirectory MERS_VacTrees, ready for loading and building from Visual Studio. R output processing code can be found in the subdirectory R, which also contains scripts to build batch files of large cluster jobs.
The directory Data contains the anonymised line list data required to replicate our results.
If running elsewhere (e.g. a cluster), then please ensure there is a folder named Data within the directory
where the executable is located.
The target executable of the C++ source code is named MERS_VacTrees.exe, which takes a single
command line argument specifying a parameter file. The syntax for running the executable is
MERS_VacTrees.exe Params_ParticularModelSettings.txt.
The code runs reasonably fast on a single core (between approximately 4 and 6 minutes
for 50,000 iterations when run on the dataset provided).
However the results of our analysis comprise several thousand
individual model runs, and so running multiple jobs in parallel on a high-performance cluster is strongly recommended, although
the model can also be run locally if required.
If the source code is recompiled having
set one variable, UseCommandLine in Structs::ModelRun::UseCommandLine to false, then a parameter file is not
required (this is mostly useful for debugging). Otherwise if UseCommandLine is set to true, then
parameter values in Structs::ModelRun will be overwritten by values in the parameter file.
Compiled executables will output to the x64/Release folder (although this can be changed by specifying a different platform and configuration in Visual Studio, or by changing the project properties). The actual executable is not tracked by git.
The executable reads in a parameter file, named Params_ParticularModelSettings.txt,
that govern which features are turned on or off.
Parameter files are generated using the script MakeBatchAndParamFiles.R.
The features specified in the parameter files
produce an output string that identifies the features present in that model run.
This output string is then included within all output file names.
The output strings cannot be interpretted without world-class guesswork, or reference to either the C++ function
ChooseScenarioName in InputOutput.cpp,
or the R function ChooseOutputString in DirectoryFunctions.R.
We have provided example batch (.bat) files to reproduce "proactive" scenarios (Proactive_WithCamels_Many.bat)
and "reactive" scenarios (Reactive_WithCamels_Many.bat) in the directory ParamFiles, and others can
be made in the script MakeBatchAndParamFiles.R
All model outputs are .txt files.
If run locally, model outputs are by default stored in
Output, although outputs are untracked by git as collectively
they can be quite large. If running elsewhere (e.g. a cluster), then ensure there is a folder named Output within the directory
where the executable is located.
The folder R contains scripts to process and plot model output. By default, these scripts
expect model output to be located in Output.
They further use the R package here and expect the working
directory to be the top level of the git repository
(i.e. whereever the .git is located and the top-level of the Visual studio project).
Plots will be stored in Plots, although these are again untracked by git.
- Compile executable (
MERS_VacTrees.exe). - Create batch file and parameter files for collection of jobs/model runs using MakeBatchAndParamFiles.R.
- Ensure above batch file is located in same folder as executable. If it isn't, copy it there. Run batch file.
- When finished, summarise model output using MakeOutputSummaryTable.R.
- If desired, plot posteriors and chains of counterfactuals for individual model runs using MakeIndividualRunPlots.R.
- Plot results using R scripts with
Fig_,Figs_orPlotprefixes.
The following papers are relevant to the model. Please note that some of them may require a subscription.
- https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(23)00117-1/fulltext
- https://www.pnas.org/content/113/32/9081
- https://www.science.org/doi/10.1126/sciadv.aba8399
Source code is licensed under the GPLv3.
It is Copyright Imperial College of Science, Technology and Medicine. The lead developers are Daniel J. Laydon, Simon Cauchemez and Neil M. Ferguson.
This repository includes code modified from RANLIB which is licensed under the LGPLv3.