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Complexity and anisotropy in host morphology make populations less susceptible to epidemic outbreaks
Authors:Francisco J. Pérez-Reche  Sergei N. Taraskin  Luciano da F. Costa  Franco M. Neri  Christopher A. Gilligan
Affiliation:1.Department of Chemistry, University of Cambridge, Cambridge, UK;2.St Catharine''s College and Department of Chemistry, University of Cambridge, Cambridge, UK;3.Department of Plant Sciences, University of Cambridge, Cambridge, UK;4.Institute of Physics at São Carlos, University of São Paulo, PO Box 369, São Carlos, São Paulo 13560-970, Brazil;5.National Institute of Science and Technology of Complex Systems, Niterói-RJ, Brazil
Abstract:One of the challenges in epidemiology is to account for the complex morphological structure of hosts such as plant roots, crop fields, farms, cells, animal habitats and social networks, when the transmission of infection occurs between contiguous hosts. Morphological complexity brings an inherent heterogeneity in populations and affects the dynamics of pathogen spread in such systems. We have analysed the influence of realistically complex host morphology on the threshold for invasion and epidemic outbreak in an SIR (susceptible–infected–recovered) epidemiological model. We show that disorder expressed in the host morphology and anisotropy reduces the probability of epidemic outbreak and thus makes the system more resistant to epidemic outbreaks. We obtain general analytical estimates for minimally safe bounds for an invasion threshold and then illustrate their validity by considering an example of host data for branching hosts (salamander retinal ganglion cells). Several spatial arrangements of hosts with different degrees of heterogeneity have been considered in order to separately analyse the role of shape complexity and anisotropy in the host population. The estimates for invasion threshold are linked to morphological characteristics of the hosts that can be used for determining the threshold for invasion in practical applications.
Keywords:epidemics   heterogeneity   percolation
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