Fig 1.
(a) An example of a human network model with 1000 nodes and 1,000,000 individuals. A stochastic metapopulation SIR model was processed on this network, and the red arrows represent the spread of the outbreak. (b) A data tree showing the order in which the outbreak spread throughout the network. Nodes are sorted by the order in which they first spread the infection to a neighboring node, and the y-axis represents the time of arrival of the epidemic.
Table 1.
A list of key variables used in this article and their definitions.
Fig 2.
Estimation of probability-dependent superspreader capacity for a node in an uncorrelated network graph, assuming all its neighbors have R ≃ 1.
In this figure Superspreader capacity is defined as the expected number of neighbors to which the node will spread the outbreak within one generation.
Fig 3.
Estimated probability-dependent superspreader capacity in a human population network with 10,000 nodes, with (a) random uncorrelated network, (b) scale free network, and (c) small world network.
Fig 4.
Estimation of time-dependent superspreader capacity for a node in an uncorrelated network graph.
Superspreader capacity is defined as the expected velocity at which a node will spread the outbreak to all its neighbors with R ≥ 1. We do not expect the structure of the network to affect this relationship significantly.
Fig 5.
One-variable response curves of superspreader risk in terms of degree, R, diffusion, centrality, and clustering.
Note that these curves have been rescaled for clarity. A scatter plot showing observed versus predicted risk indices for individual nodes is also shown, with a line indicating a 1:1 relationship.
Fig 6.
Two-dimensional response curves of superspreader risk index.
Each contour line represents one decile of risk, and each shaded area contains roughly 10% of all tested nodes.