اثرات آگاهی جهانی بر گسترش همه گیر در شبکه های چند منظوره
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اثرات آگاهی جهانی بر گسترش همه گیر در شبکه های چند منظوره

عنوان فارسی مقاله: اثرات آگاهی جهانی بر گسترش همه گیر در شبکه های چند منظوره
عنوان انگلیسی مقاله: The effects of global awareness on the spreading of epidemics in multiplex networks
مجله/کنفرانس: Physica A: Statistical Mechanics and its Applications
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری
کلمات کلیدی فارسی: شبکه پیچیده، گسترش همه گیر، آگاهی جهانی، روش MMCA، شبکه چند منظوره (مالتی پلکس)
کلمات کلیدی انگلیسی: Complex network, Epidemic spreading, Global awareness, MMCA method, Multiplex network
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.physa.2017.11.076
دانشگاه: College of Computer Engineering, Jiangsu University of Technology, 213001, Changzhou, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 2.244 در سال 2017
شاخص H_index: 133 در سال 2019
شاخص SJR: 0.773 در سال 2017
شناسه ISSN: 0378-4371
شاخص Quartile (چارک): Q2 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11881
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. The global awareness controlled contagion spreading model


3. Global MMCA method


4. Simulations of the epidemic threshold


5. Comparison with local awareness model (LACS)


6. Comparison with SIS–UAU model and single layer model


7. Conclusion


Acknowledgment


Appendix.


References

نمونه متن انگلیسی مقاله

Abstract


It is increasingly recognized that understanding the complex interplay patterns between epidemic spreading and human behavioral is a key component of successful infection control efforts. In particular, individuals can obtain the information about epidemics and respond by altering their behaviors, which can affect the spreading dynamics as well. Besides, because the existence of herd-like behaviors, individuals are very easy to be influenced by the global awareness information. Here, in this paper, we propose a global awareness controlled spreading model (GACS) to explore the interplay between the coupled dynamical processes. Using the global microscopic Markov chain approach, we obtain the analytical results for the epidemic thresholds, which shows a high accuracy by comparison with lots of Monte Carlo simulations. Furthermore, considering other classical models used to describe the coupled dynamical processes, including the local awareness controlled contagion spreading (LACS) model, Susceptible–Infected–Susceptible–Unaware–Aware– Unaware (SIS–UAU) model and the single layer occasion, we make a detailed comparisons between the GACS with them. Although the comparisons and results depend on the parameters each model has, the GACS model always shows a strong restrain effects on epidemic spreading process. Our results give us a better understanding of the coupled dynamical processes and highlights the importance of considering the spreading of global awareness in the control of epidemics.


Introduction


The study of networks has experienced a burst of activity in the last two decades [1–5]. In the field of physics, most approaches to these problems are related to the theory of phase transition [6], statistical physics [7] and critical phenomenon [8–10]. Especially with the rapid development of Internet and social media, the study of diffusion processes has attracted more and more interests [11–13]. The use of network theory in epidemiological models provides a way to incorporate the individual-level heterogeneity necessary for the mechanistic understanding of the spread of infectious disease [14]. And as a result, many interesting models have been proposed to help us to gain the details of these processes, such as the classical susceptible–infected–susceptible model (SIS) [15], susceptible–infected–recovery model (SIR) [16] and so on [17,18]. Different models correspond to diverse realistic scenarios and they may focus on various factors which can affect epidemic spreading, e.g., the structure of networks [19], the frequency of contacts among individuals [20], the heterogeneity of participants [21], the transmission capacity of community structure [22,23], etc.

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