This is a version of the multi-strain ABMlux stochastic agent-based model of COVID-19, which is in turn a version of the original ABMlux model.
Acting on a config, the multi-strain ABMlux model can be used to generate a state file, representing a world of agents and locations. In the case of the provided config, the world represents Luxembourg, exactly as in the original ABMlux model and as used in the publication Thompson, J. and Wattam, S. "Estimating the impact of interventions against COVID-19: from lockdown to vaccination", 2021, PLOS ONE, https://doi.org/10.1371/journal.pone.0261330.
A selection of state files, corresponding to different random seeds used by the stochastic world builder, are provided. They are zipped, and must therefore be unzipped and placed in the states folder before use.
The file abm_sim_extractor.py extracts data from these state files and converts them into a new format. Taking this formatted data as input, the C++ code contained in the file multi_strain_abm.cpp is then able to run a basic version of the multi-strain model featuring the baseline and lockdown scenarios. The choice of state file is made within the C++ code, as is the random seed used by the stochastic simulator.
The performance improvements reduce the runtime of each simulation to only a few minutes, allowing for high resolution parameter sweeps, for example of the strain parameters. The files abm_curve_plotter.py and abm_heat_map_plotter.py contain plotting tools able to visualize the output of the agent-based model.
Together with the agent-based model, this repository also contains code for an analagous multi-strain compartmental model. This can be found contained in the file multi_strain_sir.py, together with associated plotting tools.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Read the full text for details, but basically this means:
- No commercial exploitation (contact us for another license in this case);
- You must re-publish the source if you modify the application.
We would like this work to be useful to non-profit and academic users without significant effort. If the license is an impediment to you using the work, please get in touch with us to discuss other licensing options.