#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

The coevolution of contagion and behavior with increasing and decreasing awareness


Autoři: Samira Maghool aff001;  Nahid Maleki-Jirsaraei aff001;  Marco Cremonini aff002
Působiště autorů: Complex Systems Laboratory, Physics Department, Alzahra University, Tehran, Iran aff001;  Department of Social and Political Sciences, University of Milan, Milan, Italy aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225447

Souhrn

Understanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often been approximated as a generic personal trait that depends on social reinforcement, and used to introduce variability in state transition probabilities. A novelty of this work is to assume that individual awareness is a function of several contributing factors pooled together, different by nature and dynamics, and to study it for different epidemic categories. This way, our model still has awareness as the core attribute that may change state transition probabilities. Another contribution is to study positive and negative variations of awareness, in a contagion-behavior model. Imitation is the key mechanism that we model for manipulating awareness, under different network settings and assumptions, in particular regarding the degree of intentionality that individuals may exhibit in spreading an epidemic. Three epidemic categories are considered—disease, addiction, and rumor—to discuss different imitation mechanisms and degree of intentionality. We assume a population with a heterogeneous distribution of awareness and different response mechanisms to information gathered from the network. With simulations, we show the interplay between population and awareness factors producing a distribution of state transition probabilities and analyze how different network and epidemic configurations modify transmission patterns.

Klíčová slova:

Addiction – Agent-based modeling – Behavior – Behavioral addiction – Health education and awareness – Infectious disease epidemiology – Infectious disease modeling – Population dynamics


Zdroje

1. Ross R. An application of the theory of probabilities to the study of a priori pathometry.—Part I. Proceedings of the Royal Society of London Series A, Containing Papers of a Mathematical and Physical Character. 1916;92(638):204–230.

2. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london Series A, Containing papers of a mathematical and physical character. 1927;115(772):700–721.

3. Serfling RE. Historical review of epidemic theory. Human biology. 1952;24(3):145–166. 12990128

4. Daley DJ, Gani J. Epidemic modelling: an introduction. vol. 15. Cambridge University Press; 2001.

5. Dodds PS, Watts DJ. A generalized model of social and biological contagion. Journal of theoretical biology. 2005;232(4):587–604. doi: 10.1016/j.jtbi.2004.09.006 15588638

6. Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Reviews of modern physics. 2015;87(3):925. doi: 10.1103/RevModPhys.87.925

7. DuPont RL, Greene MH. The dynamics of a heroin addiction epidemic. Science. 1973;181(4101):716–722. doi: 10.1126/science.181.4101.716 4724929

8. Battista NA, Pearcy LB, Strickland WC. Modeling the prescription opioid epidemic. arXiv preprint arXiv:171103658. 2017;.

9. Goffman W, Newill V. Generalization of epidemic theory. Nature. 1964;204(4955):225–228. doi: 10.1038/204225a0 14212412

10. Daley DJ, Kendall DG. Epidemics and rumours. Nature. 1964;204(4963):1118. doi: 10.1038/2041118a0 14243408

11. Moreno Y, Nekovee M, Pacheco AF. Dynamics of rumor spreading in complex networks. Physical Review E. 2004;69(6):066130. doi: 10.1103/PhysRevE.69.066130

12. Zhao L, Cui H, Qiu X, Wang X, Wang J. SIR rumor spreading model in the new media age. Physica A: Statistical Mechanics and its Applications. 2013;392(4):995–1003. doi: 10.1016/j.physa.2012.09.030

13. Turenne N. The rumour spectrum. PloS one. 2018;13(1):e0189080. doi: 10.1371/journal.pone.0189080 29351289

14. Kostka J, Oswald YA, Wattenhofer R. Word of mouth: Rumor dissemination in social networks. In: International colloquium on structural information and communication complexity. Springer; 2008. p. 185–196.

15. Bettencourt LM, Cintrón-Arias A, Kaiser DI, Castillo-Chávez C. The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models. Physica A: Statistical Mechanics and its Applications. 2006;364:513–536. doi: 10.1016/j.physa.2005.08.083

16. Jiang Y, Jiang J. Diffusion in social networks: A multiagent perspective. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2015;45(2):198–213. doi: 10.1109/TSMC.2014.2339198

17. Gross T, Blasius B. Adaptive coevolutionary networks: a review. Journal of the Royal Society Interface. 2007;5(20):259–271. doi: 10.1098/rsif.2007.1229

18. Jackson MO, Zenou Y. Games on networks. In: Handbook of game theory with economic applications. vol. 4. Elsevier; 2015. p. 95–163.

19. Zheng M, Lü L, Zhao M, et al. Spreading in online social networks: The role of social reinforcement. Physical Review E. 2013;88(1):012818. doi: 10.1103/PhysRevE.88.012818

20. Wu ZX, Zhang HF. Peer pressure is a double-edged sword in vaccination dynamics. EPL (Europhysics Letters). 2013;104(1):10002. doi: 10.1209/0295-5075/104/10002

21. Zhang HF, Xie JR, Chen HS, Liu C, Small M. Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks. EPL (Europhysics Letters). 2016;114(3):38004.

22. Wang W, Tang M, Yang H, Do Y, Lai YC, Lee G. Asymmetrically interacting spreading dynamics on complex layered networks. Scientific reports. 2014;4:5097. doi: 10.1038/srep05097 24872257

23. Wang Z, Andrews MA, Wu ZX, Wang L, Bauch CT. Coupled disease–behavior dynamics on complex networks: A review. Physics of life reviews. 2015;15:1–29. doi: 10.1016/j.plrev.2015.07.006 26211717

24. Wang W, Liu QH, Cai SM, Tang M, Braunstein LA, Stanley HE. Suppressing disease spreading by using information diffusion on multiplex networks. Scientific reports. 2016;6:29259. doi: 10.1038/srep29259 27380881

25. Wang W, Liu QH, Liang J, Hu Y, Zhou T. Coevolution spreading in complex networks. Physics Reports. 2019;.

26. Funk S, Gilad E, Watkins C, Jansen VA. The spread of awareness and its impact on epidemic outbreaks. Proceedings of the National Academy of Sciences. 2009;106(16):6872–6877. doi: 10.1073/pnas.0810762106

27. Funk S, Gilad E, Jansen VA. Endemic disease, awareness, and local behavioural response. Journal of theoretical biology. 2010;264(2):501–509. doi: 10.1016/j.jtbi.2010.02.032 20184901

28. Wu Q, Fu X, Small M, Xu XJ. The impact of awareness on epidemic spreading in networks. Chaos: an interdisciplinary journal of nonlinear science. 2012;22(1):013101. doi: 10.1063/1.3673573

29. Agaba G, Kyrychko Y, Blyuss K. Mathematical model for the impact of awareness on the dynamics of infectious diseases. Mathematical biosciences. 2017;286:22–30. doi: 10.1016/j.mbs.2017.01.009 28161305

30. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. Journal of complex networks. 2014;2(3):203–271. doi: 10.1093/comnet/cnu016

31. Buono C, Alvarez-Zuzek LG, Macri PA, Braunstein LA. Epidemics in partially overlapped multiplex networks. PloS one. 2014;9(3):e92200. doi: 10.1371/journal.pone.0092200 24632709

32. Granell C, Gómez S, Arenas A. Dynamical interplay between awareness and epidemic spreading in multiplex networks. Physical review letters. 2013;111(12):128701. doi: 10.1103/PhysRevLett.111.128701 24093306

33. Sagar V, Zhao Y, Sen A. Effect of time varying transmission rates on the coupled dynamics of epidemic and awareness over a multiplex network. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2018;28(11):113125. doi: 10.1063/1.5042575

34. Coelho FC, Codeço CT. Dynamic modeling of vaccinating behavior as a function of individual beliefs. PLoS computational biology. 2009;5(7):e1000425. doi: 10.1371/journal.pcbi.1000425 19593365

35. Zhang HF, Xie JR, Tang M, Lai YC. Suppression of epidemic spreading in complex networks by local information based behavioral responses. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2014;24(4):043106. doi: 10.1063/1.4896333

36. Campbell E, Salathé M. Complex social contagion makes networks more vulnerable to disease outbreaks. Scientific reports. 2013;3:1905. doi: 10.1038/srep01905 23712758

37. Duval S, Wicklund RA. A theory of objective self awareness. 1972;.

38. Schraw G, Dennison RS. Assessing metacognitive awareness. Contemporary educational psychology. 1994;19(4):460–475. doi: 10.1006/ceps.1994.1033

39. Endsley MR. Toward a theory of situation awareness in dynamic systems. Human factors. 1995;37(1):32–64. doi: 10.1518/001872095779049499

40. Smith K, Hancock PA. Situation awareness is adaptive, externally directed consciousness. Human factors. 1995;37(1):137–148. doi: 10.1518/001872095779049444

41. Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science. 1974;185(4157):1124–1131. doi: 10.1126/science.185.4157.1124 17835457

42. Funk S, Salathé M, Jansen VA. Modelling the influence of human behaviour on the spread of infectious diseases: a review. Journal of the Royal Society Interface. 2010;7(50):1247–1256. doi: 10.1098/rsif.2010.0142

43. Perra N, Balcan D, Gonçalves B, Vespignani A. Towards a characterization of behavior-disease models. PloS one. 2011;6(8):e23084. doi: 10.1371/journal.pone.0023084 21826228

44. Leclerc-Madlala S. Youth, HIV/AIDS and the importance of sexual culture and context. Social Dynamics. 2002;28(1):20–41. doi: 10.1080/02533950208458721

45. Gifford E, Humphreys K. The psychological science of addiction. Addiction. 2007;102(3):352–361. doi: 10.1111/j.1360-0443.2006.01706.x 17298641

46. Bulger M, Davison P. The promises, challenges, and futures of media literacy. 2018;.

47. Knapp RH. A psychology of rumor. Public opinion quarterly. 1944;8(1):22–37. doi: 10.1086/265665

48. Callen E, Shapero D. A theory of social imitation. Physics Today. 1974;27:23. doi: 10.1063/1.3128690

49. Bauch CT. Imitation dynamics predict vaccinating behaviour. Proceedings of the Royal Society B: Biological Sciences. 2005;272(1573):1669–1675. doi: 10.1098/rspb.2005.3153 16087421

50. Sornette D, Zhou WX. Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets. Physica A: Statistical Mechanics and its Applications. 2006;370(2):704–726. doi: 10.1016/j.physa.2006.02.022

51. Zhao L, Yang G, Wang W, Chen Y, Huang J, Ohashi H, et al. Herd behavior in a complex adaptive system. Proceedings of the National Academy of Sciences. 2011;108(37):15058–15063. doi: 10.1073/pnas.1105239108

52. González-Avella JC, Eguíluz VM, Marsili M, Vega-Redondo F, San Miguel M. Threshold learning dynamics in social networks. PloS one. 2011;6(5):e20207. doi: 10.1371/journal.pone.0020207 21637714

53. de Arruda HF, Silva FN, Costa LdF, Amancio DR. Knowledge acquisition: A Complex networks approach. Information Sciences. 2017;421:154–166. doi: 10.1016/j.ins.2017.08.091

54. Bener AB, Çağlayan B, Henry AD, Prałat P. Empirical models of social learning in a large, evolving network. PloS one. 2016;11(10):e0160307. doi: 10.1371/journal.pone.0160307 27701430

55. Bruch E, Atwell J. Agent-based models in empirical social research. Sociological methods & research. 2015;44(2):186–221. doi: 10.1177/0049124113506405

56. Fagin R, Halpern JY. Belief, awareness, and limited reasoning. Artificial intelligence. 1987;34(1):39–76. doi: 10.1016/0004-3702(87)90003-8

57. Dietrich F, List C. Probabilistic opinion pooling. The Oxford Handbook of Probability and Philosophy;.

58. Newman ME. The structure and function of complex networks. SIAM review. 2003;45(2):167–256. doi: 10.1137/S003614450342480

59. Watts DJ, Dodds PS. Influentials, networks, and public opinion formation. Journal of consumer research. 2007;34(4):441–458. doi: 10.1086/518527

60. Wu XZ, Fennell PG, Percus AG, Lerman K, et al. Degree correlations amplify the growth of cascades in networks. Physical Review E. 2018;98(2):022321. doi: 10.1103/PhysRevE.98.022321 30253536

61. Raafat RM, Chater N, Frith C. Herding in humans. Trends in cognitive sciences. 2009;13(10):420–428. doi: 10.1016/j.tics.2009.08.002 19748818

62. Iacopini I, Petri G, Barrat A, Latora V. Simplicial models of social contagion. Nature communications. 2019;10(1):2485. doi: 10.1038/s41467-019-10431-6 31171784

63. Borge-Holthoefer J, Banos RA, González-Bailón S, Moreno Y. Cascading behaviour in complex socio-technical networks. Journal of Complex Networks. 2013;1(1):3–24. doi: 10.1093/comnet/cnt006


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#