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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

Self-organized heterogeneity.Comparison of the degree distributions of the initial unadapted network used in the first set of simulations and the ‘adapted’ self-organized network. The self-organized network is significantly more heterogeneous. However, at the same time it is more resistant to disease invasion. Parameters: β = 0.032, ψa = 0.65, ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
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f5: Self-organized heterogeneity.Comparison of the degree distributions of the initial unadapted network used in the first set of simulations and the ‘adapted’ self-organized network. The self-organized network is significantly more heterogeneous. However, at the same time it is more resistant to disease invasion. Parameters: β = 0.032, ψa = 0.65, ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.

Mentions: Let us emphasize that the adapted network has a more heterogeneous degree distribution (Fig. 5) but also a higher epidemic threshold. Thus comparing the adapted and the maximally random state, the more heterogeneous network is actually more robust to the invasion of the pathogen. This appears counter-intuitive in the light of many recent work in network epidemiology, which showed that more heterogeneous contact networks are generally more susceptible to disease invasion. However, it was already shown in27 that heterogeneous networks become less susceptible if there is a correlation such that the highly connected nodes have lower susceptibility to the disease. The present results show that a simple but plausible local adaptation rule can drive this process so far that the network with heterogeneous degree distribution is more robust against disease invasion than an Erdös-Renyi random graph of the same mean degree.


Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.

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

Self-organized heterogeneity.Comparison of the degree distributions of the initial unadapted network used in the first set of simulations and the ‘adapted’ self-organized network. The self-organized network is significantly more heterogeneous. However, at the same time it is more resistant to disease invasion. Parameters: β = 0.032, ψa = 0.65, 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

f5: Self-organized heterogeneity.Comparison of the degree distributions of the initial unadapted network used in the first set of simulations and the ‘adapted’ self-organized network. The self-organized network is significantly more heterogeneous. However, at the same time it is more resistant to disease invasion. Parameters: β = 0.032, ψa = 0.65, ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
Mentions: Let us emphasize that the adapted network has a more heterogeneous degree distribution (Fig. 5) but also a higher epidemic threshold. Thus comparing the adapted and the maximally random state, the more heterogeneous network is actually more robust to the invasion of the pathogen. This appears counter-intuitive in the light of many recent work in network epidemiology, which showed that more heterogeneous contact networks are generally more susceptible to disease invasion. However, it was already shown in27 that heterogeneous networks become less susceptible if there is a correlation such that the highly connected nodes have lower susceptibility to the disease. The present results show that a simple but plausible local adaptation rule can drive this process so far that the network with heterogeneous degree distribution is more robust against disease invasion than an Erdös-Renyi random graph of the same mean degree.

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