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A global model for predicting the arrival of imported dengue infections

With approximately half of the world’s population at risk of contracting dengue, this mosquito-borne disease is of global concern. International travellers significantly contribute to dengue’s rapid and large-scale spread by importing the disease from endemic into non-endemic countries. To prevent f...

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Detalles Bibliográficos
Autores principales: Liebig, Jessica, Jansen, Cassie, Paini, Dean, Gardner, Lauren, Jurdak, Raja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892502/
https://www.ncbi.nlm.nih.gov/pubmed/31800583
http://dx.doi.org/10.1371/journal.pone.0225193
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author Liebig, Jessica
Jansen, Cassie
Paini, Dean
Gardner, Lauren
Jurdak, Raja
author_facet Liebig, Jessica
Jansen, Cassie
Paini, Dean
Gardner, Lauren
Jurdak, Raja
author_sort Liebig, Jessica
collection PubMed
description With approximately half of the world’s population at risk of contracting dengue, this mosquito-borne disease is of global concern. International travellers significantly contribute to dengue’s rapid and large-scale spread by importing the disease from endemic into non-endemic countries. To prevent future outbreaks and dengue from establishing in non-endemic countries, knowledge about the arrival time and location of infected travellers is crucial. We propose a network model that predicts the monthly number of dengue-infected air passengers arriving at any given airport. We consider international air travel volumes to construct weighted networks, representing passenger flows between airports. We further calculate the probability of passengers, who travel through the international air transport network, being infected with dengue. The probability of being infected depends on the destination, duration and timing of travel. Our findings shed light onto dengue importation routes and reveal country-specific reporting rates that have been until now largely unknown. This paper provides important new knowledge about the spreading dynamics of dengue that is highly beneficial for public health authorities to strategically allocate the often limited resources to more efficiently prevent the spread of dengue.
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spelling pubmed-68925022019-12-14 A global model for predicting the arrival of imported dengue infections Liebig, Jessica Jansen, Cassie Paini, Dean Gardner, Lauren Jurdak, Raja PLoS One Research Article With approximately half of the world’s population at risk of contracting dengue, this mosquito-borne disease is of global concern. International travellers significantly contribute to dengue’s rapid and large-scale spread by importing the disease from endemic into non-endemic countries. To prevent future outbreaks and dengue from establishing in non-endemic countries, knowledge about the arrival time and location of infected travellers is crucial. We propose a network model that predicts the monthly number of dengue-infected air passengers arriving at any given airport. We consider international air travel volumes to construct weighted networks, representing passenger flows between airports. We further calculate the probability of passengers, who travel through the international air transport network, being infected with dengue. The probability of being infected depends on the destination, duration and timing of travel. Our findings shed light onto dengue importation routes and reveal country-specific reporting rates that have been until now largely unknown. This paper provides important new knowledge about the spreading dynamics of dengue that is highly beneficial for public health authorities to strategically allocate the often limited resources to more efficiently prevent the spread of dengue. Public Library of Science 2019-12-04 /pmc/articles/PMC6892502/ /pubmed/31800583 http://dx.doi.org/10.1371/journal.pone.0225193 Text en © 2019 Liebig 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liebig, Jessica
Jansen, Cassie
Paini, Dean
Gardner, Lauren
Jurdak, Raja
A global model for predicting the arrival of imported dengue infections
title A global model for predicting the arrival of imported dengue infections
title_full A global model for predicting the arrival of imported dengue infections
title_fullStr A global model for predicting the arrival of imported dengue infections
title_full_unstemmed A global model for predicting the arrival of imported dengue infections
title_short A global model for predicting the arrival of imported dengue infections
title_sort global model for predicting the arrival of imported dengue infections
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892502/
https://www.ncbi.nlm.nih.gov/pubmed/31800583
http://dx.doi.org/10.1371/journal.pone.0225193
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