scholarly journals Antivax movement and epidemic spreading in the era of social networks: Nonmonotonic effects, bistability, and network segregation

2021 ◽  
Vol 104 (3) ◽  
Author(s):  
Marcelo A. Pires ◽  
Andre L. Oestereich ◽  
Nuno Crokidakis ◽  
Sílvio M. Duarte Queirós
PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0152021 ◽  
Author(s):  
Ting Liu ◽  
Ping Li ◽  
Yan Chen ◽  
Jie Zhang

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xu Zhang ◽  
Yurong Song ◽  
Haiyan Wang ◽  
Guo-Ping Jiang

In social networks, the age and the region of individuals are the two most important factors in modeling infectious diseases. In this paper, a spatial susceptible-infected-susceptible (SIS) model is proposed to describe epidemic spreading over a network with region and age by establishing several partial differential equations. Numerical simulations are performed, and the simulation of the proposed model agrees well with real influenza-like illness (ILI) in the USA reported by the Centers for Disease Control (CDC). Moreover, the proposed model can be used to predict the infected density of individuals. The results show that our model can be used as a tool to analyze influenza cases in the real world.


2021 ◽  
Vol 9 ◽  
Author(s):  
M. Bellingeri ◽  
M. Turchetto ◽  
D. Bevacqua ◽  
F. Scotognella ◽  
R. Alfieri ◽  
...  

In this perspective, we describe how the link removal (LR) analysis in social complex networks may be a promising tool to model non-pharmaceutical interventions (NPIs) and social distancing to prevent epidemics spreading. First, we show how the extent of the epidemic spreading and NPIs effectiveness over complex social networks may be evaluated with a static indicator, that is, the classic largest connected component (LCC). Then we explain how coupling the LR analysis and type SIR epidemiological models (EM) provide further information by including the temporal dynamics of the epidemic spreading. This is a promising approach to investigate important aspects of the recent NPIs applied by government to contain SARS-CoV-2, such as modeling the effect of the social distancing severity and timing over different network topologies. Further, implementing different link removal strategies to halt epidemics spreading provides information to individuate more effective NPIs, representing an important tool to offer a rationale sustaining policies to prevent SARS-CoV-2 and similar epidemics.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ping Huang ◽  
Xiao-Long Chen ◽  
Ming Tang ◽  
Shi-Min Cai

In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic (RNR-SIS) dynamic model (coupled model for short) on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading.


Author(s):  
Marko Gosak ◽  
Maja Duh ◽  
Rene Markovič ◽  
Matjaz Perc

Author(s):  
Alain Barrat ◽  
Ciro Cattuto

The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.


Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 318
Author(s):  
Xiaokang Cheng ◽  
Narisa Zhao ◽  
Chenqi Li

In this paper, we aim to study the impact of the shift in herding tendency on the diffusion of internet investment products in modular social networks. The epidemic spreading mechanism is applied and numerical analyses are conducted. The results suggest that the increase in herding tendency slows down the diffusion process and postpones the outbreak time of the diffusion, but such negative effects can be compromised when the independent acceptance willingness is high. When independent acceptance willingness is low, the limited extent of the herding tendency increases the diffusion scope. In addition, the expansion of the propagation lifetime or the increase of the clustering coefficient increases the threshold so that the herding tendency has an effect on outbreak size. Further, the growth of the herding propensity tends to magnify the positive influence of the clustering coefficient and the negative effect of the modularity.


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