Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study
Background: Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3...
| Autores: | , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/128563 |
| Acceso en línea: | https://hdl.handle.net/2445/128563 |
| Access Level: | acceso abierto |
| Palabra clave: | Malalties víriques Mosquits Dengue Barbados Virus diseases Mosquitoes |
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Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| title |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| spellingShingle |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study Lowe, Rachel Malalties víriques Mosquits Dengue Barbados Virus diseases Mosquitoes |
| title_short |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| title_full |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| title_fullStr |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| title_full_unstemmed |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| title_sort |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study |
| dc.creator.none.fl_str_mv |
Lowe, Rachel Gasparrini, Antonio Van Meerbeeck, Cédric J. V. Lippi, Catherine A. Mahon, Roché Trotman, Adrian R. Rollock, Leslie Hinds, Avery Q. J. Ryan, Sadie J. Stewart Ibarra, Anna M. |
| author |
Lowe, Rachel |
| author_facet |
Lowe, Rachel Gasparrini, Antonio Van Meerbeeck, Cédric J. V. Lippi, Catherine A. Mahon, Roché Trotman, Adrian R. Rollock, Leslie Hinds, Avery Q. J. Ryan, Sadie J. Stewart Ibarra, Anna M. |
| author_role |
author |
| author2 |
Gasparrini, Antonio Van Meerbeeck, Cédric J. V. Lippi, Catherine A. Mahon, Roché Trotman, Adrian R. Rollock, Leslie Hinds, Avery Q. J. Ryan, Sadie J. Stewart Ibarra, Anna M. |
| author2_role |
author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Malalties víriques Mosquits Dengue Barbados Virus diseases Mosquitoes |
| topic |
Malalties víriques Mosquits Dengue Barbados Virus diseases Mosquitoes |
| description |
Background: Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. Methods and findings: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. Conclusion: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 2019 2019 2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2445/128563 |
| url |
https://hdl.handle.net/2445/128563 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Reproducció del document publicat a: http://dx.doi.org/10.1371/journal.pmed.1002613 PLoS Medicine, 2018, vol. 15, num. 7, p. e1002613 http://dx.doi.org/ 10.1371/journal.pmed.1002613 |
| dc.rights.none.fl_str_mv |
cc by (c) Lowe et al., 2018 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
cc by (c) Lowe et al., 2018 http://creativecommons.org/licenses/by/3.0/es/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
24 p. application/pdf |
| dc.publisher.none.fl_str_mv |
Public Library of Science (PLoS) |
| publisher.none.fl_str_mv |
Public Library of Science (PLoS) |
| dc.source.none.fl_str_mv |
Articles publicats en revistes (ISGlobal) reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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1869421624132894720 |
| spelling |
Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling studyLowe, RachelGasparrini, AntonioVan Meerbeeck, Cédric J. V.Lippi, Catherine A.Mahon, RochéTrotman, Adrian R.Rollock, LeslieHinds, Avery Q. J.Ryan, Sadie J.Stewart Ibarra, Anna M.Malalties víriquesMosquitsDengueBarbadosVirus diseasesMosquitoesBackground: Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. Methods and findings: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. Conclusion: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.Public Library of Science (PLoS)2019201920182019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion24 p.application/pdfhttps://hdl.handle.net/2445/128563Articles publicats en revistes (ISGlobal)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: http://dx.doi.org/10.1371/journal.pmed.1002613PLoS Medicine, 2018, vol. 15, num. 7, p. e1002613http://dx.doi.org/ 10.1371/journal.pmed.1002613cc by (c) Lowe et al., 2018http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1285632026-05-29T05:05:01Z |
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15,81155 |