AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home International Journal of Crowd Science Article
PDF (877 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

COVID-19 Spread Simulation in a Crowd Intelligence Network

Linzhi Shan1, Hongbo Sun1( )
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
Show Author Information

Abstract

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19.

Keywords

COVID-19epidemic spread simulationmultiple scenesmulti-layer coupled network

References

1
World Health Organization, Coronavirus disease (COVID-19) weekly epidemiological update and weekly operational update, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports, 2021.
2
S. C. Glotzer, S. Kim, P. T. Cummings, A. Deshmukh, M. HeadGordon, G. Karniadakis, L. Petzold, C. Sagui, and M. Shinozuka, International assessment of research and development in simulation-based engineering and science, Tech. Rep. ADA506976, World Technology Evaluation Center, Baltimore, MD, USA, 2009.
3

W. O. Kermack and A. G. McKendrick, Contributions to the mathematical theory of epidemics. II, The problem of endemicity, Proceedings of Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 138, no. 834, pp. 55–83, 1932.
Crossref Google Scholar
4
J. M. De Almeida Simones, An agent-based approach to spatial epidemics through GIS, PhD dissertation, Centre for Advanced Spatial Analysis & Department of Geography, Univ. Coll. London, London, UK, 2007.
5

A. James, J. W. Pitchford, and M. J. Plank, An event-based model of superspreading in epidemics, Proc. R. Soc. B Biol. Sci., vol. 274, no. 1610, pp. 741–747, 2007.
Crossref Google Scholar
6
M. C. González and H. J. Herrmann, Scaling of the propagation of epidemics in a system of mobile agents, Phys. A: Stat. Mech. Appl., vol. 340, no. 4, pp. 741−748, 2004.https://doi.org/10.1016/j.physa.2004.05.017
Crossref
7

M. Frasca, A. Buscarino, A. Rizzo, L. Fortuna, and S. Boccaletti, Dynamical network model of infective mobile agents, Phys. Rev. E, vol. 74, no. 3, p. 036110, 2006.
Crossref Google Scholar
8
O. Miramontes and B. Luque, Dynamical small-world behavior in an epidemical model of mobile individuals, Phys. D: Nonlinear Phenom., vols. 168&169, pp. 379–385, 2002.https://doi.org/10.1016/S0167-2789(02)00525-0
Crossref
9
M. L. Averill, Simulation Modeling and Analysis 5th ed. (in Chinese). Beijing, China: China Machine Press, 2017.
10
M. Hajduk, M. Sukop, and M. Haun, Cognitive multi-agent systems: Structures, strategies and applications to mobile robotics and robosoccer. Berlin, Germany: Springer, 2019.https://doi.org/10.1007/978-3-319-93687-1
Crossref
11
X. L. Tang, Combination of Swarm Intelligence and Multi-Agent System: Theory, Method and Application (in Chinese). Beijing, China: Science Press, 2014.
12

C. Moore and M. E. Newman, Epidemics and percolation in small-world networks, Phys. Rev. E, vol. 61, no. 5, pp. 5678–5682, 2000.
Crossref Google Scholar
13

R. Pastor-Satorras and A. Vespignani, Epidemic spreading in scale-free networks, Phys. Rev. Lett., vol. 86, no. 14, pp. 3200–3203, 2001.
Crossref Google Scholar
14

X. W. Chu, Z. Z. Zhang, J. H. Guan, and S. G. Zhou, Epidemic spreading with nonlinear infectivity in weighted scale-free networks, Phys. A:Stat. Mech. Appl., vol. 390, no. 3, pp. 471–481, 2011.
Crossref Google Scholar
15
X. Li and G. R. Chen, A local-world evolving network model, Phys. A: Stat. Mech. Appl., vol. 328, nos. 1&2, pp. 274–286, 2003.https://doi.org/10.1016/S0378-4371(03)00604-6
Crossref
16

H. J. Li and Y. H. Ma, Study of epidemic spreading in weighted local-world complex networks, Comput. Eng. Appl., vol. 45, no. 35, pp. 80–83, 2009.
Google Scholar
17

J. X. Li, W. Q. Li, and Z. Jin, The epidemic model based on the approximation for third-order motifs on networks, Math. Biosci., vol. 297, pp. 12–26, 2018.
Crossref Google Scholar
18

R. C. Barnard, L. Berthouze, P. L. Simon, and I. Z. Kiss, Epidemic threshold in pairwise models for clustered networks: Closures and fast correlations, J. Math. Biol., vol. 79, no. 3, pp. 823–860, 2019.
Crossref Google Scholar
19

Y. Wang, J. L. Ma, J. D. Cao, and L. Li, Edge-based epidemic spreading in degree-correlated complex networks, J. Theor. Biol., vol. 454, pp. 164–181, 2018.
Crossref Google Scholar
20

J. P. Zhang, C. Yang, Z. Jin, and J. Li, Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks, J. Theor. Biol., vol. 449, pp. 1–13, 2018.
Crossref Google Scholar
21
J. Q. Fang, X. F. Wang, and Z. G. Zheng, Research of dynamical complexity of nonlinear networks, Complex Syst. Complexity Sci., vol. 7, nos. 2&3, pp. 5–9, 2010.
22

V. Colizza and A. Vespignani, Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations, J. Theor. Biol., vol. 251, no. 3, pp. 450–467, 2008.
Crossref Google Scholar
23

S. Funk and V. A. A. Jansen, Interacting epidemics on overlay networks, Phys. Rev. E, vol. 81, no. 3, p. 036118, 2010.
Crossref Google Scholar
24

F. Chen and C. G. Li, Transmission of sexually transmitted disease in complex network of the Penna model, J. Stat. Mech. Theory Exp., vol. 2007, p. P04006, 2007.
Crossref Google Scholar
25

J. Gómez-Gardeñes, V. Latora, Y. Moreno, and E. Profumo, Spreading of sexually transmitted diseases in heterosexual populations, Proc. Natl. Acad. Sci. USA, vol. 105, no. 5, pp. 1399–1404, 2008.
Crossref Google Scholar
26
Z. Jin, J. P. Zhang, L. P. Song, G. Q. Sun, J. L. Kan, and H. P. Zhu, Modelling and analysis of influenza A (H1N1) on networks, BMC Public Health, vol. 11, no. 1, p. S9, 2011.https://doi.org/10.1186/1471-2458-11-S1-S9
Crossref
27

A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, and R. M. Eggo, Early dynamics of transmission and control of COVID-19: A mathematical modelling study, Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020.
Crossref Google Scholar
28

M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Carraro, R. Casagrandi, and A. Rinaldo, Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures, Proc. Natl. Acad. Sci. USA, vol. 117, no. 19, pp. 10484–10491, 2020.
Crossref Google Scholar
29

F. Ndaïrou, I. Area, J. J. Nieto, and D. F. M. Torres, Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan, Chaos Solitons Fractals, vol. 135, p. 109846, 2020.
Crossref Google Scholar
30

K. Prem, Y. Liu, T. W. Russell, A. J. Kucharski, R. M. Eggo, N. Davies, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, P. M. Jit, and P. Klepac, The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study, Lancet Public Health, vol. 5, no. 5, pp. E261–E270, 2020.
Crossref Google Scholar
31

J. Hellewell, S. Abbott, A. Gimma, N. I. Bosse, C. I. Jarvis, T. W. Russell, J. D. Munday, A. J. Kucharski, P. W. J. Edmunds, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, et al., Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts, Lancet Global Health, vol. 8, no. 4, pp. E488–E496, 2020.
Crossref Google Scholar
32

M. Mandal, S. Jana, S. K. Nandi, A. Khatua, S. Adak, and T. K. Kar, A model based study on the dynamics of COVID-19: Prediction and control, Chaos, Solitons & Fractals, vol. 136, p. 109889, 2020.
Crossref Google Scholar
33
Central Compilation and Translation Bureau, Marx/Engels Collected Works. Beijing, China: People’s Publishing House, 2009.
34
Y. T. Chai, C. Y. Miao, B. W. Sun, Y. Q. Zheng, and Q. Z. Li, Crowd science and engineering: Concept and research framework, International Journal of Crowd Science, vol. 1, no. 1, pp. 2−8, 2017.https://doi.org/10.1108/IJCS-01-2017-0004
Crossref
35
C. Yu, Y. T. Chai, and Y. Liu, Literature review on collective intelligence: A crowd science perspective, International Journal of Crowd Science, vol. 2, no. 1, pp. 64−73, 2018.https://doi.org/10.1108/IJCS-08-2017-0013
Crossref
36
S. P. Wang, L. Z. Cui, L. Liu, X. D. Lu, and Q. Z. Li, Projecting real world into CrowdIntell network: A methodology, International Journal of Crowd Science, vol. 3, no. 2, pp. 138−154, 2019.https://doi.org/10.1108/IJCS-01-2019-0006
Crossref
37

Z. Wang, H. B. Sun, and B. D. Fan, A novel steady-state maintenance simulation framework for multi-information disseminations in crowd network, International Journal of Crowd Science, vol. 4, no. 3, pp. 273–282, 2020.
Crossref Google Scholar
38
L. Z. Shan and H. B. Sun, Distributed collaborative simulation middleware based on reflective memory network, in Proc. IEEE 24th Int. Conf. on Computer Supported Cooperative Work in Design (CSCWD), Dalian, China, 2021, pp. 274−279.https://doi.org/10.1109/CSCWD49262.2021.9437793
Crossref
39

C. Liu, G. H. Ding, J. Q. Gong, L. C. Wang, and Z. D. Ke, Study on mathematical model of SARS outbreak prediction and early warning, (in Chinese), Chin. Sci. Bull., vol. 49, no. 21, pp. 2245–2251, 2004.
Crossref Google Scholar
40

X. Xiong, Y. Y. Li, S. J. Qiao, N. Han, Y. Wu, J. Peng, and B. Y. Li, An emotional contagion model for heterogeneous social media with multiple behaviors, Phys. A:Stat. Mech. Appl., vol. 490, pp. 185–202, 2018.
Crossref Google Scholar
41
A. King, AKShare, GitHub, GitHub repository, https://github.com/akfamily/akshare, 2019.
42
National Health Commission of the People’s Republic of China, Epidemic notification (in Chinese), http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml, 2021.
43
J. Yang and J. Leskovec, Defining and evaluating network communities based on ground-truth, Knowl. Inf. Syst., vol. 42, no. 1, pp. 181−213, 2015.https://doi.org/10.1007/s10115-013-0693-z
Crossref
44

D. J. Watts and S. H. Strogatz, Collective dynamics of ‘small-world’ networks, Nature, vol. 393, no. 6684, pp. 440–442, 1998.
Crossref Google Scholar
45

R. Y. Li, S. Pei, B. Chen, Y. M. Song, T. Zhang, W. Yang, and J. Shaman, Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2), Science, vol. 368, no. 6490, pp. 489–493, 2020.
Crossref Google Scholar
46

S. Y. Cao, Y. Gan, C. Wang, M. Bachmann, S. B. Wei, J. Gong, Y. C. Huang, T. T. Wang, L. Q. Li, K. Lu, et al., Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China, Nat. Commun., vol. 11, no. 1, p. 5917, 2020.
Crossref Google Scholar
47

H. W. Wang, Z. Z. Wang, Y. Q. Dong, R. J. Chang, C. Xu, X. Y. Yu, S. X. Zhang, L. Tsamlag, M. Shang, J. Y. Huang, et al., Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China, Cell Discov., vol. 6, no. 1, p. 10, 2020.
Crossref Google Scholar
48
People’s Government of Hubei Province, Notification of Wuhan novel coronavirus pneumonia prevention and control headquarters No. 1, (in Chinese), http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm, 2020.
International Journal of Crowd Science
Volume 6 Issue 3,
September 2022
Pages 117-127
DOI: 10.26599/IJCS.2022.9100002
Cite this article:
Shan L, Sun H. COVID-19 Spread Simulation in a Crowd Intelligence Network. International Journal of Crowd Science, 2022, 6(3): 117-127. https://doi.org/10.26599/IJCS.2022.9100002

981

Views

67

Downloads

1

Crossref

1

Scopus

Altmetrics
Received: 29 December 2021
Revised: 24 January 2022
Accepted: 25 January 2022
Published: 09 August 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return