Optimal control of multi-strain epidemic processes in complex networks

Elena Gubar, Quanyan Zhu, Vladislav Taynitskiy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationGame Theory for Networks - 7th International EAI Conference, GameNets 2017, Proceedings
PublisherSpringer Verlag
Pages108-117
Number of pages10
Volume212
ISBN (Print)9783319675398
DOIs
StatePublished - 2017
Event7th EAI International Conference on Game Theory for Networks, GameNets 2017 - Knoxville, United States
Duration: May 9 2017May 9 2017

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume212
ISSN (Print)1867-8211

Other

Other7th EAI International Conference on Game Theory for Networks, GameNets 2017
CountryUnited States
CityKnoxville
Period5/9/175/9/17

Fingerprint

Complex networks
Disease control
Vaccines
Public health
Pathogens

Keywords

  • Bi-virus models
  • Epidemic process
  • Optimal control
  • Structured population

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

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 Verlag. https://doi.org/10.1007/978-3-319-67540-4_10

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 Verlag, 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 proceedingConference contribution

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 Verlag, pp. 108-117, 7th EAI International Conference on Game Theory for Networks, GameNets 2017, Knoxville, United States, 5/9/17. https://doi.org/10.1007/978-3-319-67540-4_10
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 Verlag. 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
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 Verlag, 2017. pp. 108-117 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).
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