Cargando…

Modeling the Spread of Vector-Borne Diseases on Bipartite Networks

BACKGROUND: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. METHODOLOGY/PRINCIPAL FINDINGS: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of node...

Descripción completa

Detalles Bibliográficos
Autores principales: Bisanzio, Donal, Bertolotti, Luigi, Tomassone, Laura, Amore, Giusi, Ragagli, Charlotte, Mannelli, Alessandro, Giacobini, Mario, Provero, Paolo
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2980486/
https://www.ncbi.nlm.nih.gov/pubmed/21103064
http://dx.doi.org/10.1371/journal.pone.0013796
_version_ 1782191627249385472
author Bisanzio, Donal
Bertolotti, Luigi
Tomassone, Laura
Amore, Giusi
Ragagli, Charlotte
Mannelli, Alessandro
Giacobini, Mario
Provero, Paolo
author_facet Bisanzio, Donal
Bertolotti, Luigi
Tomassone, Laura
Amore, Giusi
Ragagli, Charlotte
Mannelli, Alessandro
Giacobini, Mario
Provero, Paolo
author_sort Bisanzio, Donal
collection PubMed
description BACKGROUND: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. METHODOLOGY/PRINCIPAL FINDINGS: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of nodes. It is sufficient for one of the classes to have a scale-free degree distribution with a slow enough decay for the network to have asymptotically vanishing epidemic threshold. Data on the distribution of Ixodes ricinus ticks on mice and lizards from two independent studies are well described by a scale-free distribution compatible with an asymptotically vanishing epidemic threshold. The commonly used negative binomial, instead, cannot describe the right tail of the empirical distribution. CONCLUSIONS/SIGNIFICANCE: The extreme aggregation of vectors on hosts, described by the power-law decay of the degree distribution, makes the epidemic threshold decrease with the size of the network and vanish asymptotically.
format Text
id pubmed-2980486
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-29804862010-11-22 Modeling the Spread of Vector-Borne Diseases on Bipartite Networks Bisanzio, Donal Bertolotti, Luigi Tomassone, Laura Amore, Giusi Ragagli, Charlotte Mannelli, Alessandro Giacobini, Mario Provero, Paolo PLoS One Research Article BACKGROUND: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. METHODOLOGY/PRINCIPAL FINDINGS: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of nodes. It is sufficient for one of the classes to have a scale-free degree distribution with a slow enough decay for the network to have asymptotically vanishing epidemic threshold. Data on the distribution of Ixodes ricinus ticks on mice and lizards from two independent studies are well described by a scale-free distribution compatible with an asymptotically vanishing epidemic threshold. The commonly used negative binomial, instead, cannot describe the right tail of the empirical distribution. CONCLUSIONS/SIGNIFICANCE: The extreme aggregation of vectors on hosts, described by the power-law decay of the degree distribution, makes the epidemic threshold decrease with the size of the network and vanish asymptotically. Public Library of Science 2010-11-12 /pmc/articles/PMC2980486/ /pubmed/21103064 http://dx.doi.org/10.1371/journal.pone.0013796 Text en Bisanzio et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bisanzio, Donal
Bertolotti, Luigi
Tomassone, Laura
Amore, Giusi
Ragagli, Charlotte
Mannelli, Alessandro
Giacobini, Mario
Provero, Paolo
Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title_full Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title_fullStr Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title_full_unstemmed Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title_short Modeling the Spread of Vector-Borne Diseases on Bipartite Networks
title_sort modeling the spread of vector-borne diseases on bipartite networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2980486/
https://www.ncbi.nlm.nih.gov/pubmed/21103064
http://dx.doi.org/10.1371/journal.pone.0013796
work_keys_str_mv AT bisanziodonal modelingthespreadofvectorbornediseasesonbipartitenetworks
AT bertolottiluigi modelingthespreadofvectorbornediseasesonbipartitenetworks
AT tomassonelaura modelingthespreadofvectorbornediseasesonbipartitenetworks
AT amoregiusi modelingthespreadofvectorbornediseasesonbipartitenetworks
AT ragaglicharlotte modelingthespreadofvectorbornediseasesonbipartitenetworks
AT mannellialessandro modelingthespreadofvectorbornediseasesonbipartitenetworks
AT giacobinimario modelingthespreadofvectorbornediseasesonbipartitenetworks
AT proveropaolo modelingthespreadofvectorbornediseasesonbipartitenetworks