This release is a pre-release and may not be stable for production use.
The Spatial Agent-Based Covid-19 Model (SABCOM)
SABCOM is an open source, easy-to-use-and-adapt, spatial network, multi-agent, model that can be used to simulate the effects of different lockdown policy measures on the spread of the Covid-19 virus in several (South African) cities.
Installation
Using Pip
$ pip install sabcom
or, alternatively
$ pip3 install sabcom
Manual
$ git clone https://github.com/blackrhinoabm/sabcom
$ cd sabcom
$ python setup.py install
Usage
The application can be used to simulate the progression of Covid-19 over a city of choice. Before running the application, the user needs that make sure that all dependencies are installed. This can be done by installing the files in the requirements.txt file on Github or on your system if you did a manual installation. Given that you are in the folder that contains this file use:
$ python -m pip install -r requirements.txt
Next, there are two options. Simulating the model (using an existing initialisation) or initialising a new model environment that can be used for the simulation.
Simulation
Five arguments need to be provided to simulate the model: a path for the input folder (-i), a path for the output folder (-o), a seed (-s), a data output mode (-d), and a scenario (-sc).
simulate -i <input folder path> -o <output folder path> -s <seed> -d <data output mode> -sc <scenario>
For example, say you want to simulate the model using input folder example_data,
output folder example_data/output_data, seed 2, data output mode csv-light, and scenario no-intervention.
First, make sure that all the files and folders are in your current location. Next, you type in the command line:
$ sabcom simulate -i example_data -o example_data/output_data -s 2 -d csv-light -sc no-intervention
This will simulate a no_intervention scenario for the seed_2.pkl initialisation. input files for the city of your choice, and output a csv light data file in the specified output folder.
Note how this assumes that there is already an initialisation file. If this is not the case, sabcom can be used to produce one given the input files.
Initialisation
initialise <input folder path> <seed number>
If an initialisation file is not present, you can create one using the sabcom initialise function.
For example, if you want to create an initialisation with the files in input folder (assumed to be in your current working directory) example_data,
Monte Carlo seed 3, the following command can be used:
$ sabcom initialise -i example_data -s 3
As a rule, creating a model initialisation takes much longer than simulating one.
Requirements
The program requires Python 3, and the packages listed in the requirements.txt file.
Website and Social Media
Disclaimer
This software is intended for educational and research purposes. Despite best efforts, we cannot fully rule out the possibility of errors and bugs. The use of SABCoM is entirely at your own risk.
1 maintainer
Release files for sabcom 0.44a0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sabcom-0.44a0.tar.gz | 27.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sabcom-0.44a0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 57.6 kB
Release files / sabcom-0.44a0.tar.gz
| Download URL | sabcom-0.44a0.tar.gz |
|---|---|
| Size | 27.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / sabcom-0.44a0-py3-none-any.whl
| Download URL | sabcom-0.44a0-py3-none-any.whl |
|---|---|
| Size | 30.3 kB |
| Tags | Python 3 |
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| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.6.8
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