By Bruno Sericola, Telek Miklós, Gábor Horváth
This booklet constitutes the refereed court cases of the twenty first overseas convention on Analytical and Stochastic Modelling concepts and functions, ASMTA 2014, held in Budapest, Hungary, in June/July 2014. The 18 papers provided have been conscientiously reviewed and chosen from 27 submissions. The papers talk about the newest advancements in analytical, numerical and simulation algorithms for stochastic structures, together with Markov techniques, queueing networks, stochastic Petri nets, procedure algebras, online game concept, etc.
Read or Download Analytical and Stochastic Modeling Techniques and Applications: 21st International Conference, ASMTA 2014, Budapest, Hungary, June 30 – July 2, 2014. Proceedings PDF
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Additional info for Analytical and Stochastic Modeling Techniques and Applications: 21st International Conference, ASMTA 2014, Budapest, Hungary, June 30 – July 2, 2014. Proceedings
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Epidemics and rumours. Nature 204, 1118 (1964) 6. : Autonomous gossiping: A self-organizing epidemic algorithm for selective information dissemination in wireless mobile adhoc networks. , Spaccapietra, S. ) ICSNW 2004. LNCS, vol. 3226, pp. 126–143. Springer, Heidelberg (2004) 7. : The Selfish Gene. Oxford University Press, Oxford (1976) 8. : Markovian sir model for opinion propagation. In: Proceedings of the 2013 25th International Teletraffic Congress (ITC), pp. 1–7. IEEE (2013) 9. : Daniel Bernoulli’s epidemiological model revisited.
K. ) Algorithms and Protocols for Wireless Sensor Networks, ch. 3. Wiley-IEEE Press (2008) 3. : I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. In: IMC 2007: Proc. 7th ACM SIGCOMM, pp. 1–14. ACM, NY (2007) 4. : Outbreaks of coinfections: The critical role of cooperativity. Europhysics Letters 104 (December 2013) 44 M. Nika et al. 5. : Epidemics and rumours. Nature 204, 1118 (1964) 6. : Autonomous gossiping: A self-organizing epidemic algorithm for selective information dissemination in wireless mobile adhoc networks.
3. Evolution of the fractions of infected, susceptible and recovered individuals for epidemics e1 and e2 and for different sizes of the intersection ν as indicated Modelling Interacting Epidemics in Overlapping Populations 41 Case Study 1: Influence of Degree of Overlap. Fig. 3 shows the influence of the degree of overlap ν between the populations on the spread of e1 and e2 . We see that the smaller the intersection, the more significant the delay of the propagation of the epidemics between the populations.