# American Institute of Mathematical Sciences

January  2015, 20(1): 215-230. doi: 10.3934/dcdsb.2015.20.215

## Interaction of media and disease dynamics and its impact on emerging infection management

 1 College of Transport & Communications, Shanghai Maritime University, Shanghai, 201306, China 2 Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, China 3 School of Administrative Studies, York University, Toronto, M3J 1P3, Canada 4 School of Mathematics, Shandong Normal University, Jiannan, 250014, China 5 Center for Disease Modeling, Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada

Received  November 2012 Revised  January 2014 Published  November 2014

The 2002-2003 SARS outbreaks exhibited some distinct features such as rapid spatial spread, massive media reports, and fast self-control. These features were shared by the 2009 pandemic influenza and will be experienced by other emerging infectious diseases. We focus on the dynamic interaction of media reports, epidemic outbreak and behavior change in the population and formulate a compartmental model, that tracks the evolution of the human population. Such population is characterized by the disease progression (susceptible, infected, hospitalized, and recovered) and by the extent to which the media has impacted, so individuals have modified their behaviors to reduce their transmissibility and infectivity. The model also describes the dynamics of media reports by considering how media is influenced by the disease statistics (numbers of infected and hospitalized individuals, for example). We then conduct linear stability analysis and numerical simulations to study how interaction of media reports and disease progress affects the disease transmission dynamics, so as to shed light on what type of media will be the most effective for the control of an epidemic.
Citation: Qin Wang, Laijun Zhao, Rongbing Huang, Youping Yang, Jianhong Wu. Interaction of media and disease dynamics and its impact on emerging infection management. Discrete & Continuous Dynamical Systems - B, 2015, 20 (1) : 215-230. doi: 10.3934/dcdsb.2015.20.215
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##### References:
 [1] Xuejuan Lu, Shaokai Wang, Shengqiang Liu, Jia Li. An SEI infection model incorporating media impact. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1317-1335. doi: 10.3934/mbe.2017068 [2] Hui Cao, Yicang Zhou. The basic reproduction number of discrete SIR and SEIS models with periodic parameters. Discrete & Continuous Dynamical Systems - B, 2013, 18 (1) : 37-56. doi: 10.3934/dcdsb.2013.18.37 [3] Jing Ge, Ling Lin, Lai Zhang. A diffusive SIS epidemic model incorporating the media coverage impact in the heterogeneous environment. Discrete & Continuous Dynamical Systems - B, 2017, 22 (7) : 2763-2776. doi: 10.3934/dcdsb.2017134 [4] Nicolas Bacaër, Xamxinur Abdurahman, Jianli Ye, Pierre Auger. On the basic reproduction number $R_0$ in sexual activity models for HIV/AIDS epidemics: Example from Yunnan, China. Mathematical Biosciences & Engineering, 2007, 4 (4) : 595-607. doi: 10.3934/mbe.2007.4.595 [5] Gerardo Chowell, R. Fuentes, A. Olea, X. Aguilera, H. Nesse, J. M. Hyman. The basic reproduction number $R_0$ and effectiveness of reactive interventions during dengue epidemics: The 2002 dengue outbreak in Easter Island, Chile. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1455-1474. doi: 10.3934/mbe.2013.10.1455 [6] Zhisheng Shuai, P. van den Driessche. Impact of heterogeneity on the dynamics of an SEIR epidemic model. Mathematical Biosciences & Engineering, 2012, 9 (2) : 393-411. doi: 10.3934/mbe.2012.9.393 [7] Yoshiaki Muroya, Toshikazu Kuniya, Yoichi Enatsu. Global stability of a delayed multi-group SIRS epidemic model with nonlinear incidence rates and relapse of infection. Discrete & Continuous Dynamical Systems - B, 2015, 20 (9) : 3057-3091. doi: 10.3934/dcdsb.2015.20.3057 [8] Jianquan Li, Xiaoqin Wang, Xiaolin Lin. Impact of behavioral change on the epidemic characteristics of an epidemic model without vital dynamics. Mathematical Biosciences & Engineering, 2018, 15 (6) : 1425-1434. doi: 10.3934/mbe.2018065 [9] Tomás Caraballo, Mohamed El Fatini, Roger Pettersson, Regragui Taki. A stochastic SIRI epidemic model with relapse and media coverage. Discrete & Continuous Dynamical Systems - B, 2018, 23 (8) : 3483-3501. doi: 10.3934/dcdsb.2018250 [10] Tianhui Yang, Lei Zhang. Remarks on basic reproduction ratios for periodic abstract functional differential equations. Discrete & Continuous Dynamical Systems - B, 2019, 24 (12) : 6771-6782. doi: 10.3934/dcdsb.2019166 [11] Zhenyuan Guo, Lihong Huang, Xingfu Zou. Impact of discontinuous treatments on disease dynamics in an SIR epidemic model. Mathematical Biosciences & Engineering, 2012, 9 (1) : 97-110. doi: 10.3934/mbe.2012.9.97 [12] Cameron Browne, Glenn F. Webb. A nosocomial epidemic model with infection of patients due to contaminated rooms. Mathematical Biosciences & Engineering, 2015, 12 (4) : 761-787. doi: 10.3934/mbe.2015.12.761 [13] Yan-Xia Dang, Zhi-Peng Qiu, Xue-Zhi Li, Maia Martcheva. Global dynamics of a vector-host epidemic model with age of infection. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1159-1186. doi: 10.3934/mbe.2017060 [14] Mamadou L. Diagne, Ousmane Seydi, Aissata A. B. Sy. A two-group age of infection epidemic model with periodic behavioral changes. Discrete & Continuous Dynamical Systems - B, 2020, 25 (6) : 2057-2092. doi: 10.3934/dcdsb.2019202 [15] Sukhitha W. Vidurupola, Linda J. S. Allen. Basic stochastic models for viral infection within a host. Mathematical Biosciences & Engineering, 2012, 9 (4) : 915-935. doi: 10.3934/mbe.2012.9.915 [16] Yuming Chen, Junyuan Yang, Fengqin Zhang. The global stability of an SIRS model with infection age. Mathematical Biosciences & Engineering, 2014, 11 (3) : 449-469. doi: 10.3934/mbe.2014.11.449 [17] Chengxia Lei, Jie Xiong, Xinhui Zhou. Qualitative analysis on an SIS epidemic reaction-diffusion model with mass action infection mechanism and spontaneous infection in a heterogeneous environment. Discrete & Continuous Dynamical Systems - B, 2020, 25 (1) : 81-98. doi: 10.3934/dcdsb.2019173 [18] Scott W. Hansen. Controllability of a basic cochlea model. Evolution Equations & Control Theory, 2016, 5 (4) : 475-487. doi: 10.3934/eect.2016015 [19] Tom Burr, Gerardo Chowell. The reproduction number $R_t$ in structured and nonstructured populations. Mathematical Biosciences & Engineering, 2009, 6 (2) : 239-259. doi: 10.3934/mbe.2009.6.239 [20] Yukihiko Nakata, Yoichi Enatsu, Yoshiaki Muroya. On the global stability of an SIRS epidemic model with distributed delays. Conference Publications, 2011, 2011 (Special) : 1119-1128. doi: 10.3934/proc.2011.2011.1119

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