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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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Average single-link infection rate  for (a), (b) Conference and (c), (d) Email data sets.(a), (c) Original temporal networks. (b), (d) Randomized temporal networks.
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pone-0068629-g004: Average single-link infection rate for (a), (b) Conference and (c), (d) Email data sets.(a), (c) Original temporal networks. (b), (d) Randomized temporal networks.

Mentions: for the original and randomized temporal networks are shown for various and values for Conference (Figures 4(a) and 4(b)) and Email (Figures 4(c) and 4(d)) data sets. Because infection can be induced only through a single link in the present simulations, we examined values that are much smaller than those used in Figures 2 and 3. For both data sets, for the original temporal networks (Figures 4(a) and 4(c)) is larger than that for the randomized networks (Figures 4(b) and 4(d)) for intermediate values of ( and for Conference and Email data sets, respectively). The behavior of is consistent with the results of the network-based simulations (Figures 2 and 3).


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

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

Average single-link infection rate  for (a), (b) Conference and (c), (d) Email data sets.(a), (c) Original temporal networks. (b), (d) Randomized temporal networks.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0068629-g004: Average single-link infection rate for (a), (b) Conference and (c), (d) Email data sets.(a), (c) Original temporal networks. (b), (d) Randomized temporal networks.
Mentions: for the original and randomized temporal networks are shown for various and values for Conference (Figures 4(a) and 4(b)) and Email (Figures 4(c) and 4(d)) data sets. Because infection can be induced only through a single link in the present simulations, we examined values that are much smaller than those used in Figures 2 and 3. For both data sets, for the original temporal networks (Figures 4(a) and 4(c)) is larger than that for the randomized networks (Figures 4(b) and 4(d)) for intermediate values of ( and for Conference and Email data sets, respectively). The behavior of is consistent with the results of the network-based simulations (Figures 2 and 3).

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