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Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China
Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical av...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6408462/ https://www.ncbi.nlm.nih.gov/pubmed/30850598 http://dx.doi.org/10.1038/s41467-019-09035-x |
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author | Oidtman, Rachel J. Lai, Shengjie Huang, Zhoujie Yang, Juan Siraj, Amir S. Reiner, Robert C. Tatem, Andrew J. Perkins, T. Alex Yu, Hongjie |
author_facet | Oidtman, Rachel J. Lai, Shengjie Huang, Zhoujie Yang, Juan Siraj, Amir S. Reiner, Robert C. Tatem, Andrew J. Perkins, T. Alex Yu, Hongjie |
author_sort | Oidtman, Rachel J. |
collection | PubMed |
description | Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005–2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis. |
format | Online Article Text |
id | pubmed-6408462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64084622019-03-11 Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China Oidtman, Rachel J. Lai, Shengjie Huang, Zhoujie Yang, Juan Siraj, Amir S. Reiner, Robert C. Tatem, Andrew J. Perkins, T. Alex Yu, Hongjie Nat Commun Article Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005–2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis. Nature Publishing Group UK 2019-03-08 /pmc/articles/PMC6408462/ /pubmed/30850598 http://dx.doi.org/10.1038/s41467-019-09035-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Oidtman, Rachel J. Lai, Shengjie Huang, Zhoujie Yang, Juan Siraj, Amir S. Reiner, Robert C. Tatem, Andrew J. Perkins, T. Alex Yu, Hongjie Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title | Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title_full | Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title_fullStr | Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title_full_unstemmed | Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title_short | Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China |
title_sort | inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in guangzhou, china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6408462/ https://www.ncbi.nlm.nih.gov/pubmed/30850598 http://dx.doi.org/10.1038/s41467-019-09035-x |
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