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Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission
The Ross-Macdonald model has dominated theory for mosquito-borne pathogen transmission dynamics and control for over a century. The model, like many other basic population models, makes the mathematically convenient assumption that populations are well mixed; i.e., that each mosquito is equally like...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861021/ https://www.ncbi.nlm.nih.gov/pubmed/24348223 http://dx.doi.org/10.1371/journal.pcbi.1003327 |
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author | Perkins, T. Alex Scott, Thomas W. Le Menach, Arnaud Smith, David L. |
author_facet | Perkins, T. Alex Scott, Thomas W. Le Menach, Arnaud Smith, David L. |
author_sort | Perkins, T. Alex |
collection | PubMed |
description | The Ross-Macdonald model has dominated theory for mosquito-borne pathogen transmission dynamics and control for over a century. The model, like many other basic population models, makes the mathematically convenient assumption that populations are well mixed; i.e., that each mosquito is equally likely to bite any vertebrate host. This assumption raises questions about the validity and utility of current theory because it is in conflict with preponderant empirical evidence that transmission is heterogeneous. Here, we propose a new dynamic framework that is realistic enough to describe biological causes of heterogeneous transmission of mosquito-borne pathogens of humans, yet tractable enough to provide a basis for developing and improving general theory. The framework is based on the ecological context of mosquito blood meals and the fine-scale movements of individual mosquitoes and human hosts that give rise to heterogeneous transmission. Using this framework, we describe pathogen dispersion in terms of individual-level analogues of two classical quantities: vectorial capacity and the basic reproductive number, [Image: see text]. Importantly, this framework explicitly accounts for three key components of overall heterogeneity in transmission: heterogeneous exposure, poor mixing, and finite host numbers. Using these tools, we propose two ways of characterizing the spatial scales of transmission—pathogen dispersion kernels and the evenness of mixing across scales of aggregation—and demonstrate the consequences of a model's choice of spatial scale for epidemic dynamics and for estimation of [Image: see text], both by a priori model formulas and by inference of the force of infection from time-series data. |
format | Online Article Text |
id | pubmed-3861021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38610212013-12-17 Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission Perkins, T. Alex Scott, Thomas W. Le Menach, Arnaud Smith, David L. PLoS Comput Biol Research Article The Ross-Macdonald model has dominated theory for mosquito-borne pathogen transmission dynamics and control for over a century. The model, like many other basic population models, makes the mathematically convenient assumption that populations are well mixed; i.e., that each mosquito is equally likely to bite any vertebrate host. This assumption raises questions about the validity and utility of current theory because it is in conflict with preponderant empirical evidence that transmission is heterogeneous. Here, we propose a new dynamic framework that is realistic enough to describe biological causes of heterogeneous transmission of mosquito-borne pathogens of humans, yet tractable enough to provide a basis for developing and improving general theory. The framework is based on the ecological context of mosquito blood meals and the fine-scale movements of individual mosquitoes and human hosts that give rise to heterogeneous transmission. Using this framework, we describe pathogen dispersion in terms of individual-level analogues of two classical quantities: vectorial capacity and the basic reproductive number, [Image: see text]. Importantly, this framework explicitly accounts for three key components of overall heterogeneity in transmission: heterogeneous exposure, poor mixing, and finite host numbers. Using these tools, we propose two ways of characterizing the spatial scales of transmission—pathogen dispersion kernels and the evenness of mixing across scales of aggregation—and demonstrate the consequences of a model's choice of spatial scale for epidemic dynamics and for estimation of [Image: see text], both by a priori model formulas and by inference of the force of infection from time-series data. Public Library of Science 2013-12-12 /pmc/articles/PMC3861021/ /pubmed/24348223 http://dx.doi.org/10.1371/journal.pcbi.1003327 Text en © 2013 Perkins 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 Perkins, T. Alex Scott, Thomas W. Le Menach, Arnaud Smith, David L. Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title | Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title_full | Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title_fullStr | Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title_full_unstemmed | Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title_short | Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission |
title_sort | heterogeneity, mixing, and the spatial scales of mosquito-borne pathogen transmission |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861021/ https://www.ncbi.nlm.nih.gov/pubmed/24348223 http://dx.doi.org/10.1371/journal.pcbi.1003327 |
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