Virus spread over networks: Modeling, analysis, and control

Epidemic processes;Virus spread models;Network analysis and control;Networked systems;Stochastic systems;Time-varying systems;Time-varying networks;Network theory (graphs);Diseases;Biological system modeling;Data models;Mathematical model;"John Snows cholera data set";Validation of networked systems

Pare, Philip E.

关键词: Epidemic processes;    Virus spread models;    Network analysis and control;    Networked systems;    Stochastic systems;    Time-varying systems;    Time-varying networks;    Network theory (graphs);    Diseases;    Biological system modeling;    Data models;    Mathematical model;    "John Snows cholera data set";    Validation of networked systems;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/102436/PARE-DISSERTATION-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The spread of viruses inbiological networks,computer networks, and human contact networks can have devastating effects; developing and analyzing mathematical models of these systems can provide insights that lead to long-term societal benefits. Basic virus models have been studied for over three centuries; however, as the world continues to becomeconnected and networked in more complex ways, previous models no longer are sufficient. Therefore virus spread over networks is a newer research topic, which provides a compelling modeling technique to capture real world behavior, and interest from the control field has provided an exciting new outlook on the area.Prior research has focused mainly onnetwork models with static graph structures; however, the systems being modeled typically have dynamic graph structures and have not been validated with real spread data over a network. In this dissertation, we considervirus spread models over networks with dynamic graph structures, and we investigate the behavior of these systems. We performstability analyses ofepidemic processes over time-varying networks, providing sufficient conditions for convergence to the disease free equilibrium (the origin, or healthy state), in both the deterministic and stochastic cases. We also explore the scenario of multiple viruses, in the case of competing viruses, including human awareness, and coupled competing viruses. We analyze the healthy state and the endemic states of these models over static and dynamic graph structures. Various control techniques are also proposed to mitigate virus spread in networks. Illustrative figures and simulations are presented throughout. No previous work has explored identification and validation of network dependent virus spread models, which is considered herein using two datasets: 1) John Snow's fundamental 1854 cholera dataset and 2) a 2009-2012 USDA farm subsidy dataset. We conclude by discussing current work and future research directions.