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Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics.

Takaguchi T, Masuda N, Holme P - PLoS ONE (2013)

Bottom Line: This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases.In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection.We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Informatics, The University of Tokyo, Tokyo, Japan.

ABSTRACT
Records of social interactions provide us with new sources of data for understanding how interaction patterns affect collective dynamics. Such human activity patterns are often bursty, i.e., they consist of short periods of intense activity followed by long periods of silence. This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases. We investigate a model of history-dependent contagion. In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection. We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.

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Related in: MedlinePlus

for  in Email data set.We set . The vertical ticks in the box plot in the bottom indicate the times of the events that involve node .
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getmorefigures.php?uid=PMC3716695&req=5

pone-0068629-g001: for in Email data set.We set . The vertical ticks in the box plot in the bottom indicate the times of the events that involve node .

Mentions: When node in state interacts with an node through an event, is increased by unity. In the absence of interaction with nodes, is assumed to decay exponentially in time. In other words, is given by(1)where(2)and is the time of an event between node and an node, and is the decay time constant. An example time course of is shown in Figure 1.


Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics.

Takaguchi T, Masuda N, Holme P - PLoS ONE (2013)

for  in Email data set.We set . The vertical ticks in the box plot in the bottom indicate the times of the events that involve node .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3716695&req=5

pone-0068629-g001: for in Email data set.We set . The vertical ticks in the box plot in the bottom indicate the times of the events that involve node .
Mentions: When node in state interacts with an node through an event, is increased by unity. In the absence of interaction with nodes, is assumed to decay exponentially in time. In other words, is given by(1)where(2)and is the time of an event between node and an node, and is the decay time constant. An example time course of is shown in Figure 1.

Bottom Line: This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases.In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection.We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Informatics, The University of Tokyo, Tokyo, Japan.

ABSTRACT
Records of social interactions provide us with new sources of data for understanding how interaction patterns affect collective dynamics. Such human activity patterns are often bursty, i.e., they consist of short periods of intense activity followed by long periods of silence. This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases. We investigate a model of history-dependent contagion. In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection. We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.

Show MeSH
Related in: MedlinePlus