Dynamics of the impact of Twitter with time delay on the spread of infectious diseases

Maoxing liu; Yuting Chang; Haiyan Wang; Benxing Li

doi:10.1142/S1793524518500675

Dynamics of the impact of Twitter with time delay on the spread of infectious diseases

Maoxing liu, Yuting Chang, Haiyan Wang, Benxing Li

Mathematical and Natural Sciences, School of (SMNS)

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

In this paper, a mathematical model to study the impact of Twitter in controlling infectious disease is proposed. The model includes the dynamics of "tweets" which may enhance awareness of the disease and cause behavioral changes among the public, thus reducing the transmission of the disease. Furthermore, the model is improved by introducing a time delay between the outbreak of disease and the release of Twitter messages. The basic reproduction number and the conditions for the stability of the equilibria are derived. It is shown that the system undergoes Hopf bifurcation when time delay is increased. Finally, numerical simulations are given to verify the analytical results.

Original language	English (US)
Article number	1850067
Journal	International Journal of Biomathematics
Volume	11
Issue number	5
DOIs	https://doi.org/10.1142/S1793524518500675
State	Published - Jul 1 2018

Keywords

Hopf bifurcation
Twitter
epidemic
time delay

ASJC Scopus subject areas

Modeling and Simulation
Applied Mathematics

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10.1142/S1793524518500675

Cite this

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title = "Dynamics of the impact of Twitter with time delay on the spread of infectious diseases",

abstract = "In this paper, a mathematical model to study the impact of Twitter in controlling infectious disease is proposed. The model includes the dynamics of {"}tweets{"} which may enhance awareness of the disease and cause behavioral changes among the public, thus reducing the transmission of the disease. Furthermore, the model is improved by introducing a time delay between the outbreak of disease and the release of Twitter messages. The basic reproduction number and the conditions for the stability of the equilibria are derived. It is shown that the system undergoes Hopf bifurcation when time delay is increased. Finally, numerical simulations are given to verify the analytical results.",

keywords = "Hopf bifurcation, Twitter, epidemic, time delay",

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AU - liu, Maoxing

AU - Chang, Yuting

AU - Wang, Haiyan

AU - Li, Benxing

N1 - Funding Information: The Project supported by the National Sciences Foundation of China (Nos. 11571324, 11301491 and 11331009), the National Sciences Foundation of Shanxi Province (2015011009) and the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province. Publisher Copyright: © 2018 World Scientific Publishing Company.

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Y1 - 2018/7/1

N2 - In this paper, a mathematical model to study the impact of Twitter in controlling infectious disease is proposed. The model includes the dynamics of "tweets" which may enhance awareness of the disease and cause behavioral changes among the public, thus reducing the transmission of the disease. Furthermore, the model is improved by introducing a time delay between the outbreak of disease and the release of Twitter messages. The basic reproduction number and the conditions for the stability of the equilibria are derived. It is shown that the system undergoes Hopf bifurcation when time delay is increased. Finally, numerical simulations are given to verify the analytical results.

AB - In this paper, a mathematical model to study the impact of Twitter in controlling infectious disease is proposed. The model includes the dynamics of "tweets" which may enhance awareness of the disease and cause behavioral changes among the public, thus reducing the transmission of the disease. Furthermore, the model is improved by introducing a time delay between the outbreak of disease and the release of Twitter messages. The basic reproduction number and the conditions for the stability of the equilibria are derived. It is shown that the system undergoes Hopf bifurcation when time delay is increased. Finally, numerical simulations are given to verify the analytical results.

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Dynamics of the impact of Twitter with time delay on the spread of infectious diseases

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Keywords

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