How demography-driven evolving networks impact epidemic transmission between communities

Pan, Wei; Sun, Gui‐Quan; Jin, Zhen

doi:10.1016/j.jtbi.2015.07.009

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…Now, whereas different types of networks, in particular social networks, have been studied extensively in the last two decades, the way of how the social networks influence human mobility, and vice versa, has been explored only in recent times. The interplay between social networks and mobility has been explored in the context of contact networks [24, 33, 50–54], location based social networks [31, 55], face to face networks [56] and the spreading of diseases [23, 57–60]. …”

Section: Introductionmentioning

confidence: 99%

Emergence of encounter networks due to human mobility

Riascos

Mateos

2017

PLoS ONE

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There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times. This generates a temporal network that is characterized by global quantities. We compare this dynamics with real data for two cities: New York City and Tokyo. We use data from the location-based social network Foursquare and obtain the emergent temporal encounter network, for these two cities, that we compare with our model. We found long-range (Lévy-like) distributions for traveled distances and time intervals that characterize the emergent social network due to human mobility. Studying this connection is important for several fields like epidemics, social influence, voting, contagion models, behavioral adoption and diffusion of ideas.

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Section: Introductionmentioning

confidence: 99%

Emergence of encounter networks due to human mobility

Riascos

Mateos

2017

PLoS ONE

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Section: Introductionmentioning

confidence: 99%

“…As the topology properties of networks have a profound impact on the dynamics of epidemic spreading, it is necessary to consider the effect of community structure on epidemic spreading. So far, a lot of results on epidemic dynamics in community networks have been obtained [12,14,16,29,30].To simulate the real network, many different kinds of community network models were constructed based on classical networks, and some individual behavior characteristics in real networks (such as random walk, long-range jump and awareness) were also taken into account. Liu and Hu studied the SIS (susceptible-infected-susceptible) dynamics on a random community network model with probability p (q) of intra-(inter-)…”

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confidence: 99%

Modeling and analysis of epidemic spreading on community networks with heterogeneity

Jiang

Song

et al. 2018

Journal of Parallel and Distributed Computing

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h i g h l i g h t s• A modified community network model with heterogeneity among communities is proposed.• A mathematical epidemic model for each community is presented based on this network.• Study the effect of the location of initial infection node on epidemic spreading. • Study the impact of the heterogeneity among communities on epidemic spreading. a b s t r a c t A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold. (B. Song). example, the severe acute respiratory syndrome (SARS) in 2003, the H1N1 influenza A virus in 2009, the H7N9 avian influenza virus in 2013, the Ebola virus in 2014 and the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2015. The problem of epidemic spreading has gained great attention over the years and people want to predict the epidemic spread trend and take effective public health measures with limited vaccine supply. As the topology properties of networks have a profound impact on the dynamics of epidemic spreading, it is necessary to consider the effect of community structure on epidemic spreading. So far, a lot of results on epidemic dynamics in community networks have been obtained [12,14,16,29,30].To simulate the real network, many different kinds of community network models were constructed based on classical networks, and some individual behavior characteristics in real networks (such as random walk, long-range jump and awareness) were also taken into account. Liu and Hu studied the SIS (susceptible-infected-susceptible) dynamics on a random community network model with probability p (q) of intra-(inter-)

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“…Furthermore, it is shown in [ 18 ] that the deterministic models admit a single asymptotically stable equilibrium. Similarly, considering deterministic models of epidemics for networks with demographics, [ 19 , 20 ] derive asymptotically stable equilibria. More specifically, for SIS models on networks the basic reproduction number R 0 is obtained and it is shown that for R 0 < 1 there exists a globally asymptotically stable disease-free equilibrium, while for R 0 > 1 there is a globally asymptotically stable endemic equilibrium.…”

Section: Introductionmentioning

confidence: 99%

Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects

Baumann

Sandmann

2016

PLoS ONE

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Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity.

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How demography-driven evolving networks impact epidemic transmission between communities

Cited by 13 publications

References 39 publications

Emergence of encounter networks due to human mobility

Emergence of encounter networks due to human mobility

Modeling and analysis of epidemic spreading on community networks with heterogeneity

Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects

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