nettracesim
ABM/DES modeling of COVID epidemic on social networks
Science Score: 44.0%
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Low similarity (6.3%) to scientific vocabulary
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ABM/DES modeling of COVID epidemic on social networks
Basic Info
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Metadata Files
README.md
Contact Tracing for Disease Containment: a Network-Based Analysis
NetTraceSim - ABM/DES modeling of COVID epidemic on social networks.
Author: Felix Gigler, Martin Bicher
Introduction
The spread of SARS-CoV-2 is modeled by combining discrete events and an agent based approach, where the agents are seen as nodes in a social network. Thus, we can investigate the impact of certain network properties on the epidemic.
Installation
Install miniconda, then (in Anaconda Terminal):
conda install numpy matplotlib networkx scipy ffmpeg
Alternatively, use the setup.py to create an venv.
Set 'kernels = <# of processor cores> -1' in doexperimentparallel.py. This is system dependent.
Use
The latest experiments can be found in 'Experiments/Paper'. Some of them might not work anymore due to changes in the base model. These could be adapted in the future.
Of particular interest to us are the following experiments:
3CvaryC.py : Effect of TI and TTI for different amounts of clustering.
3Evaryp_i.py : Effect of TI and TTI for different amounts of infectiousness.
Owner
- Name: Felix Gigler
- Login: figlerg
- Kind: user
- Location: Vienna
- Company: Austrian Institute of Technology, TU Vienna
- Repositories: 3
- Profile: https://github.com/figlerg
I am a Master student at TU Vienna, currently a part-time researcher at AIT.
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Gigler
given-names: Felix
orcid: https://orcid.org/0000-0002-6495-9048
- family-names: Martin
given-names: Bicher
title: "Contact Tracing for Disease Containment:a Network-Based Analysis"
version: 0.5
date-released: 2021-08-20
url: https://github.com/figlerg/NetTraceSim
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Dependencies
- ffmpeg-python *
- matplotlib *
- networkx *
- numpy *
- pandas *
- scipy *
- tqdm *