Impact of heterogeneous human activities on epidemic spreading

Yang, Zimo; Cui, Aixiang; Zhou, Tao

doi:10.1016/j.physa.2011.06.068

Cited by 36 publications

(27 citation statements)

References 46 publications

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“…This effect, which we call population effect, is also observed in Fig. 4 of [8] where the authors study the impact of heterogeneous human activities on epidemic spreading through simulations. Theorem 3 also says that the degree of acceleration is asymptotically proportional to the network size.…”

Section: Theoremsupporting

confidence: 60%

“…The authors in [7] derived a closed-form equation for the critical level of virus infection rate that lets a virus persist in a network when the virus is recoverable with a certain rate. More realistic average spread behaviors of a virus with the heterogeneity inherent in human mobility patterns have been studied through simulations in [8]. In computer networks, [9] analyzed the average propagation behavior of code red worm in the Internet using measurement data from ISPs.…”

Section: Introductionmentioning

confidence: 99%

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On Stochastic Confidence of Information Spread in Opportunistic Networks

Kim

Lee

Shroff

2016

IEEE Trans. on Mobile Comput.

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Predicting spreading patterns of information or virus has been a popular research topic for which various mathematical tools have been developed. These tools have mainly focused on estimating the average time of spread to a fraction (e.g., α) of the agents, i.e., so-called average α-completion time E(Tα). We claim that understanding stochastic confidence on the time Tα rather than only its average gives more comprehensive knowledge on the spread behavior and wider engineering choices. Obviously, the knowledge also enables us to effectively accelerate or decelerate a spread. To demonstrate the benefits of understanding the distribution of spread time, we introduce a new metric G α,β that denotes the time required to guarantee α completion (i.e., penetration) with probability β. Also, we develop a new framework characterizing G α,β for various spread parameters such as number of seeders, contact rates between agents, and heterogeneity in contact rates. We apply our technique to a large-scale experimental vehicular trace and show that it is possible to allocate resources for acceleration of spread in a far more elaborated way compared to conventional average-based mathematical tools.

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Section: Theoremsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

On Stochastic Confidence of Information Spread in Opportunistic Networks

Kim

Lee

Shroff

2016

IEEE Trans. on Mobile Comput.

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“…In addition to imperfect mixing in space, it has recently been shown that imperfect mixing in time also causes slowerthan-expected transmission over time-ordered networks [44,51]. [52,53].…”

Section: Temporal Activity Patterns Enable Transmission Dualitymentioning

confidence: 99%

Beyond contact-based transmission networks: the role of spatial coincidence

Richardson

Gorochowski

2015

J. R. Soc. Interface.

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Animal societies rely on interactions between group members to effectively communicate and coordinate their actions. To date, the transmission properties of interaction networks formed by direct physical contacts have been extensively studied for many animal societies and in all cases found to inhibit spreading. Such direct interactions do not, however, represent the only viable pathways. When spreading agents can persist in the environment, indirect transmission via 'same-place, different-time' spatial coincidences becomes possible. Previous studies have neglected these indirect pathways and their role in transmission. Here, we use rock ant colonies, a model social species whose flat nest geometry, coupled with individually tagged workers, allowed us to build temporally and spatially explicit interaction networks in which edges represent either direct physical contacts or indirect spatial coincidences. We show how the addition of indirect pathways allows the network to enhance or inhibit the spreading of different types of agent. This dual-functionality arises from an interplay between the interaction-strength distribution generated by the ants' movement and environmental decay characteristics of the spreading agent. These findings offer a general mechanism for understanding how interaction patterns might be tuned in animal societies to control the simultaneous transmission of harmful and beneficial agents.

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“…[39,40] with weights between w min = 1 and w max = 10 4 . The former has a clear mean, while the latter lacks a characteristic mean.…”

Section: Modelmentioning

confidence: 99%

Epidemic spreading in weighted networks: An edge-based mean-field solution

Yang

Zhou

2012

Phys. Rev. E

Self Cite

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Weight distribution greatly impacts the epidemic spreading taking place on top of networks. This paper presents a study of a susceptible-infected-susceptible model on regular random networks with different kinds of weight distributions. Simulation results show that the more homogeneous weight distribution leads to higher epidemic prevalence, which, unfortunately, could not be captured by the traditional mean-field approximation. This paper gives an edge-based mean-field solution for general weight distribution, which can quantitatively reproduce the simulation results. This method could be applied to characterize the nonequilibrium steady states of dynamical processes on weighted networks.

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Impact of heterogeneous human activities on epidemic spreading

Cited by 36 publications

References 46 publications

On Stochastic Confidence of Information Spread in Opportunistic Networks

On Stochastic Confidence of Information Spread in Opportunistic Networks

Beyond contact-based transmission networks: the role of spatial coincidence

Epidemic spreading in weighted networks: An edge-based mean-field solution

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