Non-universal power-law dynamics of SIR models on hierarchical modular networks
G\'eza \'Odor

TL;DR
This paper studies how hierarchical modular networks influence the spread dynamics of SIR models, revealing non-universal power-law behaviors and the effects of network topology, heterogeneity, and mobility on epidemic growth and decay.
Contribution
It demonstrates that finite topological dimensions in hierarchical networks lead to power-law prevalence growth, with heterogeneity and mobility affecting epidemic dynamics but not the fundamental scaling exponents.
Findings
Power-law growth depends on network topological dimension.
Heterogeneity alters critical behavior and epidemic size distribution.
Mobility increases epidemic magnitude without changing scaling exponents.
Abstract
Power-law (PL) time dependent infection growth has been reported in many COVID-19 statistics. In simple SIR models the number of infections grows at the outbreak as on -dimensional Euclidean lattices in the endemic phase or follow a slower universal PL at the critical point, until finite sizes cause immunity and a crossover to an exponential decay. Heterogeneity may alter the dynamics of spreading models, spatially inhomogeneous infection rates can cause slower decays, posing a threat of a long recovery from a pandemic. COVID-19 statistics have also provided epidemic size distributions with PL tails in several countries. Here I investigate SIR like models on hierarchical modular networks, embedded in 2d lattices with the addition of long-range links. I show that if the topological dimension of the network is finite, average degree dependent PL growth of…
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