Abstract

Epidemics and evolution of many pathogens occur on similar timescales so that their dynamics are often entangled. Here, in a first step to study this problem theoretically, we analyze mutating pathogens spreading on simple SIR networks with grid-like connectivity. We have in mind the spatial aspect of epidemics, which often advance on transport links between hosts or groups of hosts such as cities or countries. We focus on the case of mutations that enhance an agent’s infection rate. We uncover that the small-world property, i.e., the presence of long-range connections, makes the network very vulnerable, supporting frequent supercritical mutations and bringing the network from disease extinction to full blown epidemic. For very large numbers of long-range links, however, the effect reverses and we find a reduced chance for large outbreaks. We study two cases, one with discrete number of mutational steps and one with a continuous genetic variable, and we analyze various scaling regimes. For the continuous case we derive a Fokker-Planck-like equation for the probability density and solve it for small numbers of shortcuts using the WKB approximation. Our analysis supports the claims that a potentiating mutation in the transmissibility might occur during an epidemic wave and not necessarily before its initiation.

Highlights

  • Epidemics and evolution of many pathogens occur on similar timescales so that their dynamics are often entangled

  • The mathematical modeling of dynamical processes on networks has, in the recent years, produced an abundance of results that help to unravel the complexities of epidemic waves and predict the outcomes and risks of transmitting diseases[4,5,6,7]

  • We will study two scenarios, one for pathogens requiring a small number of mutational steps for adaptation and one for pathogens requiring a large number of steps, where a continuous approximation may be valid: i) In a first case, we introduce a relation λd(γ) with the mutating genetic variable γ, which can have three values: -1 in the initial state, 0 in a neutral state, 1 in the supercritical state

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Summary

Introduction

Epidemics and evolution of many pathogens occur on similar timescales so that their dynamics are often entangled. Given the phylogenetic data for instance for the case of Ebola epidemic in 2013-2016, it appears that the genetic variability of the entire virus population of the outbreak is much larger than the variability at each infection site or country[30]. The difference of our scenario with mutations to this former mutationless case of SIR in small world networks is the generation of an explosive giant component in a fraction of the runs.

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