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Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.

Yang H, Tang M, Gross T - Sci Rep (2015)

Bottom Line: One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts.We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity.For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

View Article: PubMed Central - PubMed

Affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.

ABSTRACT
One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

No MeSH data available.


Related in: MedlinePlus

Comparison of thresholds.The plot shows a very good agreement agreement between equation-based continuation (lines) and agent-based simulations (symbols) for the persistence thresholds βper (box, dashed). However, a notable difference exists for the invasion thresholds βinv (circle, dotted). Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
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f2: Comparison of thresholds.The plot shows a very good agreement agreement between equation-based continuation (lines) and agent-based simulations (symbols) for the persistence thresholds βper (box, dashed). However, a notable difference exists for the invasion thresholds βinv (circle, dotted). Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.

Mentions: We emphasize that the observed discrepeancy between the initial and the adapted invasion threshold could not appear in networks of identical nodes. For identical nodes the extinct state is unique on the level of the pair approximation ([I] = [II] = [SI] = 0), and thus both thresholds must coincide. The results in Fig. 2 show that is indeed the case, while different thresholds are observed in all networks with heterogeneous nodes.


Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.

Yang H, Tang M, Gross T - Sci Rep (2015)

Comparison of thresholds.The plot shows a very good agreement agreement between equation-based continuation (lines) and agent-based simulations (symbols) for the persistence thresholds βper (box, dashed). However, a notable difference exists for the invasion thresholds βinv (circle, dotted). Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4543971&req=5

f2: Comparison of thresholds.The plot shows a very good agreement agreement between equation-based continuation (lines) and agent-based simulations (symbols) for the persistence thresholds βper (box, dashed). However, a notable difference exists for the invasion thresholds βinv (circle, dotted). Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
Mentions: We emphasize that the observed discrepeancy between the initial and the adapted invasion threshold could not appear in networks of identical nodes. For identical nodes the extinct state is unique on the level of the pair approximation ([I] = [II] = [SI] = 0), and thus both thresholds must coincide. The results in Fig. 2 show that is indeed the case, while different thresholds are observed in all networks with heterogeneous nodes.

Bottom Line: One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts.We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity.For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

View Article: PubMed Central - PubMed

Affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.

ABSTRACT
One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

No MeSH data available.


Related in: MedlinePlus