Limits...
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 in self-organized networks.The plot shows a very good agreement between equation-based continuation (lines) and agent-based simulations started in an artificially created adapted state (symbols) for both the invasion thresholds βinv (circle, dotted) and the persistence thresholds βper (box, dashed). See Fig. 2 for comparison. Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4543971&req=5

f4: Comparison of thresholds in self-organized networks.The plot shows a very good agreement between equation-based continuation (lines) and agent-based simulations started in an artificially created adapted state (symbols) for both the invasion thresholds βinv (circle, dotted) and the persistence thresholds βper (box, dashed). See Fig. 2 for comparison. Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.

Mentions: To verify that the self-organization of the link distribution explains the observed discrepancy between the initial and the adapted invasion threshold, we turn to the agent-based simulation again. However, in this case we start the simulation from an artificially created adapted state. To initialize this state we simulate the system with the same set of initial parameters until the system reaches the stationary state. Then we retain the self-organized link pattern, but reassign all epidemic states, such that all agents are susceptible except for 20 initially infected. Then we simulate the system again until it either reaches the endemic state or the epidemic goes extinct. We locate the epidemic threshold by running a series of such simulations and find the point where the probability to reach the disease-free state becomes zero. The epidemic threshold that is thus found coincides with the result from continuation of the equation-based model (Fig. 4).


Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.

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

Comparison of thresholds in self-organized networks.The plot shows a very good agreement between equation-based continuation (lines) and agent-based simulations started in an artificially created adapted state (symbols) for both the invasion thresholds βinv (circle, dotted) and the persistence thresholds βper (box, dashed). See Fig. 2 for comparison. 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

f4: Comparison of thresholds in self-organized networks.The plot shows a very good agreement between equation-based continuation (lines) and agent-based simulations started in an artificially created adapted state (symbols) for both the invasion thresholds βinv (circle, dotted) and the persistence thresholds βper (box, dashed). See Fig. 2 for comparison. Parameters: ψb = 0.05, ω = 0.2, μ = 0.002, N = 105, K = 106.
Mentions: To verify that the self-organization of the link distribution explains the observed discrepancy between the initial and the adapted invasion threshold, we turn to the agent-based simulation again. However, in this case we start the simulation from an artificially created adapted state. To initialize this state we simulate the system with the same set of initial parameters until the system reaches the stationary state. Then we retain the self-organized link pattern, but reassign all epidemic states, such that all agents are susceptible except for 20 initially infected. Then we simulate the system again until it either reaches the endemic state or the epidemic goes extinct. We locate the epidemic threshold by running a series of such simulations and find the point where the probability to reach the disease-free state becomes zero. The epidemic threshold that is thus found coincides with the result from continuation of the equation-based model (Fig. 4).

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