sir-model
Code & Simulink/linear-systems models for “A microscopic-view Infection model based on linear systems” (Information Sciences, 2020)
Science Score: 44.0%
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Low similarity (3.2%) to scientific vocabulary
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Repository
Code & Simulink/linear-systems models for “A microscopic-view Infection model based on linear systems” (Information Sciences, 2020)
Basic Info
- Host: GitHub
- Owner: HAOHE123
- Language: MATLAB
- Default Branch: main
- Homepage: https://doi.org/10.1016/j.ins.2019.09.021
- Size: 7.81 KB
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- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
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Metadata Files
README.md
SIR-Model
The focus is on source localization and network topology discovery in infection networks with a subset of nodes as observers. Additionally, the potential for controlling these networks is explored.
Code and examples for A microscopic-view Infection model based on linear systems
Overview
This repository contains codes used for the paper:
He Hao, Daniel Silvestre, Carlos Silvestre, A microscopic-view Infection model based on linear systems, Information Sciences, Feb 2020.
Owner
- Name: He Hao
- Login: HAOHE123
- Kind: user
- Location: Lisboa, Portugal
- Company: Instituto Superior Técnico
- Repositories: 1
- Profile: https://github.com/HAOHE123
Ph.D. Student at Instituto Superior Técnico in Electrical and Computer Engineering
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite:"
title: "A microscopic-view Infection model based on linear systems"
version: "v1.0"
doi: "10.1016/j.ins.2019.09.021"
url: "https://github.com/HAOHE123/Your-Repo-For-Infection-Model"
authors:
- family-names: "Hao"
given-names: "He"
- family-names: "Silvestre"
given-names: "Daniel"
- family-names: "Silvestre"
given-names: "Carlos"
date-released: "2020-01-01"
license: "MIT"
keywords:
- "Infection networks"
- "Source localization"
- "Topology identification"
- "Linear models"
- "Controllability"
- "Observability"
abstract: |
Understanding the behavior of an infection network is typically addressed from either a microscopic or a macroscopic point-of-view. The trade-off is between following the individual states at some added complexity cost or looking at the ratio of infected nodes. In this paper, we focus on developing an alternative approach based on dynamical linear systems that combines the fine information of the microscopic view without the associated added complexity. Attention is shifted towards the problems of source localization and network topology discovery in the context of infection networks where a subset of the nodes is elected as observers. Finally, the possibility to control such networks is also investigated. Simulations illustrate the conclusions of the paper with particular interest on the relationship of the aforementioned problems with the topology of the network and the selected observer/controller nodes.
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