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Open AccessJournal ArticleDOI

Preventable H5N1 avian influenza epidemics in the British poultry industry network exhibit characteristic scales.

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TLDR
H5N1 avian influenza transmission probabilities and containment strategies, here modelled on the British poultry industry network, show that infection dynamics can additionally express characteristic scales, and hotspots can make more effective inoculation targets.
Abstract
Epidemics are frequently simulated on redundantly wired contact networks, which have many more links between sites than are minimally required to connect all. Consequently, the modelled pathogen can travel numerous alternative routes, complicating effective containment strategies. These networks have moreover been found to exhibit ‘scale-free’ properties and percolation, suggesting resilience to damage. However, realistic H5N1 avian influenza transmission probabilities and containment strategies, here modelled on the British poultry industry network, show that infection dynamics can additionally express characteristic scales. These system-preferred scales constitute small areas within an observed power law distribution that exhibit a lesser slope than the power law itself, indicating a slightly increased relative likelihood. These characteristic scales are here produced by a network-pervading intranet of so-called hotspot sites that propagate large epidemics below the percolation threshold. This intranet is, however, extremely vulnerable; targeted inoculation of a mere 3–6% (depending on incorporated biosecurity measures) of the British poultry industry network prevents large and moderate H5N1 outbreaks completely, offering an order of magnitude improvement over previously advocated strategies affecting the most highly connected ‘hub’ sites. In other words, hotspots and hubs are separate functional entities that do not necessarily coincide, and hotspots can make more effective inoculation targets. Given the ubiquity and relevance of networks (epidemics, Internet, power grids, protein interaction), recognition of this spreading regime elsewhere would suggest a similar disproportionate sensitivity to such surgical interventions.

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Citations
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Journal ArticleDOI

Epidemic control analysis: designing targeted intervention strategies against epidemics propagated on contact networks.

TL;DR: A new approach to design effective targeted intervention strategies to mitigate and control the propagation of infections across heterogeneous contact networks is introduced, using a newly developed individual-level deterministic Susceptible-Infectious-Susceptible (SIS) epidemiological model.
Journal ArticleDOI

Tracking Socioeconomic Vulnerability Using Network Analysis: Insights from an Avian Influenza Outbreak in an Ostrich Production Network

TL;DR: The hypothesis that increasing economic efficiency in the domestic ostrich industry in South Africa made the system more vulnerable to outbreak of Highly Pathogenic Avian Influenza (H5N2), and the results indicated that as time progressed, the network became increasingly vulnerable to pathogen outbreaks.
Book ChapterDOI

Networks and Models with Heterogeneous Population Structure in Epidemiology

Rowland R. Kao
- 01 Jan 2010 - 
TL;DR: In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and results from real data and their relationship to network analyses summarised are summarised.
Journal ArticleDOI

Epidemics and control strategies for diseases of farmed salmonids: A parameter study

TL;DR: The susceptibility of the English and Welsh fish farming and fisheries industry to emergent diseases is assessed using a stochastic simulation model that considers reactive, proactive, and hybrid methods of control which correspond to a mixture of policy and the ease of disease detection.
Journal ArticleDOI

An exact relationship between invasion probability and endemic prevalence for Markovian SIS dynamics on networks.

TL;DR: Markovian susceptible-infectious-susceptible (SIS) dynamics on finite strongly connected networks is considered, applicable to several sexually transmitted diseases and computer viruses, and it is shown that the probability of invasion from any given individual is equal to the (probabilistic) endemic prevalence, following successful invasion, at the individual.
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