| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
|
|
|
Impact of environmental factors on the coevolution of information-emotions-epidemic dynamics in activity-driven multiplex networks |
| Liang'an Huo(霍良安)1,2, Bingjie Liu(刘炳杰)1, and Xiaomin Zhao(赵晓敏)3,† |
1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
3 School of Management, Shanghai University, Shanghai 200444, China |
|
|
|
|
Abstract During public health emergencies, the diffusion of negative information can exacerbate the transmission of adverse emotions, such as fear and anxiety. These emotions can adversely affect immune function and, consequently, influence the spread of the epidemic. In this study, we established a coupled model incorporating environmental factors to explore the coevolution dynamic process of information-emotions-epidemic dynamics in activity-driven multiplex networks. In this model, environmental factors refer to the external conditions or pressures that affect the spread of information, emotions, and epidemics. These factors include media coverage, public opinion, and the prevalence of diseases in the neighborhood. These layers are dynamically cross-coupled, where the environmental factors in the information layer are influenced by the emotional layer; the higher the levels of anxious states among neighboring individuals, the greater the likelihood of information diffusion. Although environmental factors in the emotional layer are influenced by both the information and epidemic layers, they come from the factors of global information and the proportion of local infections among surrounding neighbors. Subsequently, we utilized the microscopic Markov chain approach to describe the dynamic processes, thereby obtaining the epidemic threshold. Finally, conclusions are drawn through numerical modeling and analysis. The conclusions suggest that when negative information increases, the probability of the transmission of anxious states across the population increases. The transmission of anxious states increases the final size of the disease and decreases its outbreak threshold. Reducing the impact of environmental factors at both the informational and emotional levels is beneficial for controlling the scale of the spread of the epidemic. Our findings can provide a reference for improving public health awareness and behavioral decision-making, mitigating the adverse impacts of anxious states, and ultimately controlling the spread of epidemics.
|
Received: 12 May 2024
Revised: 01 August 2024
Accepted manuscript online: 23 September 2024
|
|
PACS:
|
89.75.-k
|
(Complex systems)
|
| |
87.23.Ge
|
(Dynamics of social systems)
|
| |
87.19.X-
|
(Diseases)
|
|
| Fund: This work was partially supported by the National Natural Science Foundation of China (Grant No. 72174121), the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the Natural Science Foundation of Shanghai (Grant No. 21ZR1444100). |
Corresponding Authors:
Xiaomin Zhao
E-mail: zhaoxiaomin@shu.edu.cn
|
Cite this article:
Liang'an Huo(霍良安), Bingjie Liu(刘炳杰), and Xiaomin Zhao(赵晓敏) Impact of environmental factors on the coevolution of information-emotions-epidemic dynamics in activity-driven multiplex networks 2024 Chin. Phys. B 33 128903
|
[1] Mahase E 2020 Bmj 368 101136
[2] Chen A, Liu H and Su G 2024 International Journal of Disaster Risk Reduction 101 104226
[3] Yang P, Fan R, Wang Y and Zhang Y 2024 Chin. Phys. B 33 070206
[4] Li Y and Sheng D 2024 Computers in Human Behavior 152 108055
[5] Wang X and Zhang S 2024 Mathematics and Computers in Simulation 217 374
[6] Yang B, Lin Y, Xiong W, et al. 2024 The Lancet Regional HealthWestern Pacific 43 100969
[7] Kermack W O and McKendrick A G 1932 Proceedings of the Royal Society of London Series A 138 55
[8] Kermack W O and McKendrick A G 1991 Bulletin of Mathematical Biology 53 33
[9] Burki T K 2022 The Lancet Respiratory Medicine 10 e17
[10] Velavan T P and Meyer C G 2021 International Journal of Infectious Diseases 103 278
[11] Aleta A, Martin-Corral D, Pastore Y, Piontti A, et al. 2020 Nature Human Behavior 4 964
[12] Song H, So J, Shim M, et al. 2023 Computers in Human Behavior 138 107439
[13] Wang Z, Andrews M A, Wu Z X, et al. 2015 Physics of Life Reviews 15 57
[14] Kan J Q and Zhang H F 2017 Communications in Nonlinear Science and Numerical Simulation 44 193
[15] Weitz J S, Park S W, Eksin C, et al. 2020 Proc. Natl. Acad. Sci. USA 117 32764
[16] Chai Y, Wang Y G, Yan J and Sun X L 2023 Chin. Phys. B 32 090202
[17] Wang R, Zhang X and Wang M 2024 Applied Mathematics and Computation 468 128507
[18] Huo L and Wu B 2024 Chin. Phys. B 33 038702
[19] Huo L and Yu Y 2023 Chin. Phys. B 32 108703
[20] You X M, Zhang M and Ma Y H 2023 Nonlinear Dyn. 111 21351
[21] Granell C, Gómez S and Arenas A 2013 Phys. Rev. Lett. 111 128701
[22] Granell C, Gómez S and Arenas A 2014 Phys. Rev. E 90 012808
[23] Du K and Fan R 2024 Expert Systems with Applications 235 121225
[24] Zhang Y, Su Y, Weigang L, et al. 2019 Chaos, Solitons & Fractals 121 168
[25] Wang J, Cai S, Wang W, et al. 2023 Applied Mathematics and Computation 437 127537
[26] Chen P, Guo X, Jiao Z, et al. 2022 Frontiers in Physics 10 964883
[27] Zhang Q 2022 Human Security in China: A Post-Pandemic State 155-174
[28] Yin F, Pan Y, Tang X, et al. 2022 Applied Mathematics Letters 133 108231
[29] Huo L and Gu J 2023 Physica A 609 128323
[30] Jain K, Bhatnagar V, Prasad S, et al. 2022 IEEE Transactions on Network Science and Engineering 10 20
[31] Xu H, Zhao Y and Han D 2022 Nonlinear Dyn. 110 901
[32] Wang Z, Andrews M A, Wu Z X, et al. 2015 Physics of Life Reviews 15 1
[33] Zhu B, Zheng X, Liu H, et al. 2020 Chaos, Solitons & Fractals 140 110123
[34] Zhao Q, He Y, Wang Y, et al. 2024 Sustainable Cities and Society 107 105403
[35] Yin F, Xia X, Pan Y, et al. 2022 Information Sciences 594 118
[36] Dharmawan C and Argaheni N B 2021 PLACENTUM: Jurnal Ilmiah Kesehatan dan Aplikasinya 9 16
[37] Zhang L, Pan J, Chen W, et al. 2020 American journal of cancer research 10 1294
[38] Li D, Qian W, Sun X, et al. 2023 Applied Mathematics and Computation 458 128233
[39] Hou Y, Lu Y, Dong Y, et al. 2023 Applied Mathematics and Computation 446 127850
[40] Zhang J, Wang X and Chen S 2023 Chaos, Solitons & Fractals 173 113601
[41] Kumar V and Bhattacharyya S 2023 Chaos, Solitons & Fractals 175 114014
[42] Chen W, Wang J, Yu F, et al. 2021 Applied Mathematics and Computation 411 126497
[43] Hatfield E, Cacioppo J T and Rapson R L 1993 Current Directions in Psychological Science 2 96
[44] Wang X, Li W, Du J 2010 Advances in Psychological Science 18 1236
[45] Li R, Wang W and Di Z 2017 Physica A 467 30
[46] Li R, Richmond P and Roehner B M 2018 Physica A 510 713
[47] Li R, Tang M and Hui P M 2013 Acta Phys. Sin. 62 168903 (in Chinese)
[48] Doan S, MacDonald S and Swaminathan K 2023 Mental Health & Prevention 30 200272
[49] Pandey R, Mandal S P, Shukla M, et al. 2023 Heliyon 9 e21934
[50] Mueller I, Pruessner L, Holt D V, et al. 2023 Journal of Affective Disorders 348 398 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
