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Optimal Control of Multi-strain Epidemic Processes in Complex Networks. / Gubar, Elena; Zhu, Quanyan; Taynitskiy, Vladislav.

Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings. Vol. 212 Springer International Publishing : Springer Nature, 2017. p. 108-117 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 212).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Gubar, E, Zhu, Q & Taynitskiy, V 2017, Optimal Control of Multi-strain Epidemic Processes in Complex Networks. in Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings. vol. 212, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 212, Springer Nature, Springer International Publishing, pp. 108-117, 7th EAI International Conference on Game Theory for Networks, GameNets 2017, Knoxville, United States, 8/05/17. https://doi.org/10.1007/978-3-319-67540-4_10

APA

Gubar, E., Zhu, Q., & Taynitskiy, V. (2017). Optimal Control of Multi-strain Epidemic Processes in Complex Networks. In Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings (Vol. 212, pp. 108-117). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 212). Springer Nature. https://doi.org/10.1007/978-3-319-67540-4_10

Vancouver

Gubar E, Zhu Q, Taynitskiy V. Optimal Control of Multi-strain Epidemic Processes in Complex Networks. In Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings. Vol. 212. Springer International Publishing: Springer Nature. 2017. p. 108-117. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). https://doi.org/10.1007/978-3-319-67540-4_10

Author

Gubar, Elena ; Zhu, Quanyan ; Taynitskiy, Vladislav. / Optimal Control of Multi-strain Epidemic Processes in Complex Networks. Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings. Vol. 212 Springer International Publishing : Springer Nature, 2017. pp. 108-117 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

BibTeX

@inproceedings{228f23af551d49d288f664d56d28b6da,
title = "Optimal Control of Multi-strain Epidemic Processes in Complex Networks",
abstract = "The emergence of new diseases, such as HIV/AIDS, SARS, and Ebola, represent serious problems for the public health and medical science research to address. Despite the rapid development of vaccines and drugs, one challenge in disease control is the fact that one pathogen sometimes generates many strains with different spreading features. Hence it is of critical importance to investigate multi-strain epidemic dynamics and its associated epidemic control strategies. In this paper, we investigate two controlled multi-strain epidemic models for heterogeneous populations over a large complex network and obtain the structure of optimal control policies for both models. Numerical examples are used to corroborate the analytical results.",
keywords = "Bi-virus models, Epidemic process, Optimal control, Structured population",
author = "Elena Gubar and Quanyan Zhu and Vladislav Taynitskiy",
year = "2017",
doi = "10.1007/978-3-319-67540-4_10",
language = "English",
isbn = "978-3-319-67539-8",
volume = "212",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Nature",
pages = "108--117",
booktitle = "Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings",
address = "Germany",
note = "7th EAI International Conference on Game Theory for Networks, GameNets 2017 ; Conference date: 08-05-2017 Through 08-05-2017",

}

RIS

TY - GEN

T1 - Optimal Control of Multi-strain Epidemic Processes in Complex Networks

AU - Gubar, Elena

AU - Zhu, Quanyan

AU - Taynitskiy, Vladislav

PY - 2017

Y1 - 2017

N2 - The emergence of new diseases, such as HIV/AIDS, SARS, and Ebola, represent serious problems for the public health and medical science research to address. Despite the rapid development of vaccines and drugs, one challenge in disease control is the fact that one pathogen sometimes generates many strains with different spreading features. Hence it is of critical importance to investigate multi-strain epidemic dynamics and its associated epidemic control strategies. In this paper, we investigate two controlled multi-strain epidemic models for heterogeneous populations over a large complex network and obtain the structure of optimal control policies for both models. Numerical examples are used to corroborate the analytical results.

AB - The emergence of new diseases, such as HIV/AIDS, SARS, and Ebola, represent serious problems for the public health and medical science research to address. Despite the rapid development of vaccines and drugs, one challenge in disease control is the fact that one pathogen sometimes generates many strains with different spreading features. Hence it is of critical importance to investigate multi-strain epidemic dynamics and its associated epidemic control strategies. In this paper, we investigate two controlled multi-strain epidemic models for heterogeneous populations over a large complex network and obtain the structure of optimal control policies for both models. Numerical examples are used to corroborate the analytical results.

KW - Bi-virus models

KW - Epidemic process

KW - Optimal control

KW - Structured population

UR - http://www.scopus.com/inward/record.url?scp=85030167420&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-67540-4_10

DO - 10.1007/978-3-319-67540-4_10

M3 - Conference contribution

AN - SCOPUS:85030167420

SN - 978-3-319-67539-8

VL - 212

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 108

EP - 117

BT - Game Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings

PB - Springer Nature

CY - Springer International Publishing

T2 - 7th EAI International Conference on Game Theory for Networks, GameNets 2017

Y2 - 8 May 2017 through 8 May 2017

ER -

ID: 9170251