nettracesim

ABM/DES modeling of COVID epidemic on social networks

https://github.com/figlerg/nettracesim

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (6.3%) to scientific vocabulary

Keywords

agent-based-simulation discrete-event-simulation modeling sars-cov-2
Last synced: 6 months ago · JSON representation ·

Repository

ABM/DES modeling of COVID epidemic on social networks

Basic Info
  • Host: GitHub
  • Owner: figlerg
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 57.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
agent-based-simulation discrete-event-simulation modeling sars-cov-2
Created about 5 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Citation

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

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

GitHub Events

Total
Last Year

Dependencies

setup.py pypi
  • ffmpeg-python *
  • matplotlib *
  • networkx *
  • numpy *
  • pandas *
  • scipy *
  • tqdm *