| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects |
| Liang'an Huo(霍良安)1,2,† and Bingjie Wu(武兵杰)1 |
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 |
|
|
|
|
Abstract Today, with the rapid development of the internet, a large amount of information often accompanies the rapid transmission of disease outbreaks, and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks. In fact, the disease transmission process is very complex. Besides this information, there will often be individual behavioral measures and other factors to consider. Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease, rarely divided into information and behavior, respectively. To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism, this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion, vaccination behavior, and disease transmission. This is achieved by considering four influential relationships between adjacent layers in multilayer networks. In the information layer, the diffusion process of negative information is described, and the feedback effects of local and global vaccination are considered. In the behavioral layer, an individual's vaccination behavior is described, and the probability of an individual receiving a vaccination is influenced by two factors: the influence of negative information, and the influence of local and global disease severity. In the disease layer, individual susceptibility is considered to be influenced by vaccination behavior. The state transition equations are derived using the micro Markov chain approach (MMCA), and disease prevalence thresholds are obtained. It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior, and is mainly influenced by global vaccination behavior; vaccination behavior is mainly influenced by local disease conditions, and is less influenced by global disease conditions; the disease transmission threshold increases with the increasing vaccination rate; and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate. Finally, it is found that when individual vaccination behavior considers both the influence of negative information and disease, it can increase the disease transmission threshold and reduce the scale of disease transmission. Therefore, we should resist the diffusion of negative information, increase vaccination proportions, and take appropriate protective measures in time.
|
Received: 07 July 2023
Revised: 12 September 2023
Accepted manuscript online: 07 October 2023
|
|
PACS:
|
87.23.Kg
|
(Dynamics of evolution)
|
| |
87.23.Ge
|
(Dynamics of social systems)
|
| |
64.60.aq
|
(Networks)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 72174121 and 71774111), 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:
Liang'an Huo
E-mail: huohuolin@yeah.net
|
Cite this article:
Liang'an Huo(霍良安) and Bingjie Wu(武兵杰) Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects 2024 Chin. Phys. B 33 038702
|
[1] Mahase E 2020 Brit. Med. J. 368 m1003
[2] Russell B S, Hutchison M and Tambling R 2020 Child Psychiatry Hum. Dev. 51 671
[3] Lin S H, Lan Y T, Hisa P H, Kao C M, Tsou H H and Lin Y H 2023 J. Affect. Disord. 325 119
[4] Egbert A R, Karpiak S, Havlik R and Cankurtaran S 2023 J. Psychosom. Res. 166 111145
[5] Galali Y 2021 Food Sci. Nutr. 9 2105
[6] Xie Y, Xu E and Al-Aly Z 2022 Brit. Med. J. 376 e068993
[7] Kermack W O and McKendrick A G 1991 Bull. Math. Biol. 53 33
[8] Gray A, Greenhalgh D, Hu L, Mao X and Pan J 2011 Siam J. Appl. Math. 71 876
[9] Kermack W O and McKendrick A G 1991 Bull. Math. Biol. 53 57
[10] McCluskey C C 2010 Nonlinear Anal-Real. 11 55
[11] Liu L L, Wang J L and Liu X N 2015 Nonlinear Anal-Real. 24 18
[12] Pastor-Satorras R and Vespignani A 2001 Phys. Rev. Lett. 86 3200
[13] Newman M E J 2003 Siam Rev. 45 167
[14] Buono C, Alvarez-Zuzek L G, Macri P A and Braunstein L A 2014 Plos One 9 e92200
[15] Funk S, Gilada E, Watkinsb C and Jansen V A 2009 Proc. Natl. Acad. Sci. USA 106 6872
[16] Shi H J, Duan Z S, Chen G R and Li R 2009 Chin. Phys. B 18 3309
[17] Granell C, Gómez S and Arenas A 2013 Phys. Rev. Lett. 111 128701
[18] Gómez S, Díaz-Guilera A, Gómez-Gardeñes J, Pérez-Vicente C J, Moreno Y and Arenas A 2013 Phys. Rev. Lett. 110 028701
[19] Boccaletti S, Bianconi G, Criado R, Del Genio C I, Gómez-Gardeñes J, Romance M, Sendiña-Nadal I, Wang Z and Zanin M 2014 Phys. Rep. 544 1
[20] De Arruda G F, Rodrigues F A and Moreno Y 2018 Phys. Rep. 756 1
[21] Wang W, Liu Q H, Liang J H and Zhou T 2019 Phys. Rep. 820 1
[22] Sun M F, Tao Y Z and Fu X C 2021 Chaos 31 093134
[23] Peng K Y, Lu Z, Lin V, Lindstrom M R, Parkinson C, Wang C T, Bertozzi A L and Porter M A 2021 Math. Mod. Meth. Appl. Sci. 31 2455
[24] Huo L A and Gu J F 2023 Phys. A 609 128323
[25] Chang X, Cai C R, Zhang J Q and Wang C Y 2021 Phys. Rev. E 104 044303
[26] Yang R, Wang B H, Ren J Bai W J, Shi Z W, Wang W X and Zhou T 2007 Phys. Lett. A 364 189
[27] Granell C, Gómez S and Arenas A 2014 Phys. Rev. E 90 012808
[28] Xia C Y, Wang Z S, Zheng C Y, Guo Q T, Shi Y T, Dehmer M and Chen Z Q 2019 Inform. Sci. 471 185
[29] Wang Z S, Xia C Y, Chen Z Q and Chen G R 2021 IEEE Trans. Cybern. 51 1454
[30] Yin Q, Wang Z S, Xia C Y and Bauch C T 2022 Commun. Nonlinear Sci. Numer. Simul. 109 106312
[31] Xu H D, Zhao Y and Han D 2022 Nonlinear Dyn. 110 901
[32] Du E H, Chen E, Liu J and Zheng C M 2020 Sci. Total. Environ. 761 144114
[33] Wu Q C and Chen S F 2022 Chaos Solitons Fract. 159 112119
[34] Nie Y Y, Zhong X N, Wu T, Liu Y B, Lin T and Wang W 2022 J. King Saud Univ.-Comput. Inform. Sci. 34 2871
[35] Zuo C, Zhu F P, Meng Z Y, Ling Y T, Zheng Y Z and Zhao X K 2022 Infect. Genet. Evol. 98 105218
[36] Zhang H G, Chen X L, Peng Y, Kou G and Wang R J 2022 Inform. Sci. 605 366
[37] Jain K, Bhatnager V, Prasad S and Kaur S 2023 IEEE Trans. Netw. Sci. Eng. 10 20
[38] Zuo C, Wang A J, Zhu F P, Meng Z Y and Zhao X K 2021 Complexity 2021 6680135
[39] Nie Y Y, Zhong X N, Lin T and Wang W 2022 Appl. Math. Comput. 432 127380
[40] Gao B, Deng Z H and Zhao D W 2016 Chaos Solitons Fract. 93 175
[41] Utsumi S, Are M R, Tatsukawa Y and Tanimoto J 2022 Chaos Solitons Fract. 159 112178
[42] Kabir K M A and Tanimoto J 2019 Commun. Nonlinear Sci. Numer. Simul. 76 92
[43] Li X J, Li C and Li X 2022 Sci. China Inform. Sci. 65 172202
[44] Wang B, Xie Z Y and Han Y X 2021 Phys. Rev. E 104 044307
[45] Backstrom L, Boldi P, Rosa M and Vigna S 2012 Proceeding of the 4th Annual ACM Web Science Conference, June 22-24, 2012, Evanston, America, p. 33
[46] Ugander J, Karrer B, Backstrom L and Marlow C 2011 arXiv: 1111.4503v1
[47] Nie Y Y, Li W Y, Pan L M, Lin T and Wang W 2022 Appl. Math. Comput. 417 126773
[48] Nie Y Y, Zhong X N, Lin T and Wang W 2023 Chaos Solitons Fract. 166 112909 |
| 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
|
|
|
