Social distancing strategies against disease spreading

Valdez, L. D.; Buono, C.; Macri, P. A.; Braunstein, L. A.

doi:10.1142/9789814590143_0015

Cited by 8 publications

(10 citation statements)

References 30 publications

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“…We first examine some of the concepts of the generating function formalism for an isolated network, and we then extend this formalism to the partially overlapped multiplex network. In the generating function framework, the relevant magnitude that provides information about the process is the probability f that a branch of infection can extend throughout the network [34,35]. When a branch of infection reaches a node of connectivity k across one of its links, the branch can only expand through its remaining k − 1 connections.…”

Section: Introductionmentioning

confidence: 99%

Epidemics in Partially Overlapped Multiplex Networks

et al. 2014

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Many real networks exhibit a layered structure in which links in each layer reflect the function of nodes on different environments. These multiple types of links are usually represented by a multiplex network in which each layer has a different topology. In real-world networks, however, not all nodes are present on every layer. To generate a more realistic scenario, we use a generalized multiplex network and assume that only a fraction of the nodes are shared by the layers. We develop a theoretical framework for a branching process to describe the spread of an epidemic on these partially overlapped multiplex networks. This allows us to obtain the fraction of infected individuals as a function of the effective probability that the disease will be transmitted . We also theoretically determine the dependence of the epidemic threshold on the fraction of shared nodes in a system composed of two layers. We find that in the limit of the threshold is dominated by the layer with the smaller isolated threshold. Although a system of two completely isolated networks is nearly indistinguishable from a system of two networks that share just a few nodes, we find that the presence of these few shared nodes causes the epidemic threshold of the isolated network with the lower propagating capacity to change discontinuously and to acquire the threshold of the other network.

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Section: Introductionmentioning

confidence: 99%

Epidemics in Partially Overlapped Multiplex Networks

et al. 2014

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“…In contrast, an individual with many friends may not share a held opinion if only a few of the friends agree with the opinion. Indeed, in many opinion models [16][17][18][19][20][21][22][23][24][25][26][27][28][29] individuals strongly tend to conform to the majority opinion of their friends. The spreading of information on OSNs is similar to that in epidemic and binary-choice opinion models, but the detailed mechanisms can differ.…”

Section: Introductionmentioning

confidence: 99%

Competing for Attention in Social Media under Information Overload Conditions

Feng

et al. 2015

PLoS ONE

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Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

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“…They evaluated a simple case in which connections among individuals are randomly removed and a more complex case whereby each individual retains at least a few connections after the contact reduction. Valdez et al 40 – 42 proposed an adaptive SIR model in which a link-activation–deactivation strategy different from the link-rewiring approach is introduced into the discrete SIR model to demonstrate the social distancing effect. These published researches focused on the effect of network topology changed by cutting links and link-rewiring on the epidemic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Effects of void nodes on epidemic spreads in networks

Kuga

Tanimoto

2022

Sci Rep

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We present the pair approximation models for susceptible–infected–recovered (SIR) epidemic dynamics in a sparse network based on a regular network. Two processes are considered, namely, a Markovian process with a constant recovery rate and a non-Markovian process with a fixed recovery time. We derive the implicit analytical expression for the final epidemic size and explicitly show the epidemic threshold in both Markovian and non-Markovian processes. As the connection rate decreases from the original network connection, the epidemic threshold in which epidemic phase transits from disease-free to endemic increases, and the final epidemic size decreases. Additionally, for comparison with sparse and heterogeneous networks, the pair approximation models were applied to a heterogeneous network with a degree distribution. The obtained phase diagram reveals that, upon increasing the degree of the original random regular networks and decreasing the effective connections by introducing void nodes accordingly, the final epidemic size of the sparse network is close to that of the random network with average degree of 4. Thus, introducing the void nodes in the network leads to more heterogeneous network and reduces the final epidemic size.

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Social distancing strategies against disease spreading

Cited by 8 publications

References 30 publications

Epidemics in Partially Overlapped Multiplex Networks

Epidemics in Partially Overlapped Multiplex Networks

Competing for Attention in Social Media under Information Overload Conditions

Effects of void nodes on epidemic spreads in networks

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