Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

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 outbr...

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Published inPLoS medicine Vol. 15; no. 7; p. e1002613
Main Authors Lowe, Rachel, Gasparrini, Antonio, Van Meerbeeck, Cédric J, Lippi, Catherine A, Mahon, Roché, Trotman, Adrian R, Rollock, Leslie, Hinds, Avery Q J, Ryan, Sadie J, Stewart-Ibarra, Anna M
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 17.07.2018
Public Library of Science (PLoS)
Subjects
Aedes - virology
Analysis
Animals
Aquatic insects
Barbados
Barbados - epidemiology
Bayes Theorem
Bayesian analysis
Biology and life sciences
Climate
Climate change
Climate effects
Decision making
Dengue
Dengue - diagnosis
Dengue - epidemiology
Dengue - transmission
Dengue - virology
Dengue fever
Dengue Virus - pathogenicity
Disease Outbreaks
Disease transmission
Disease Vectors
Drought
Droughts
Earth Sciences
Ecology
Ecology and Environmental Sciences
Environmental risk
Epidemics
Excess rainfall
Exposure
Fever
Floods
Health risk assessment
Hot Temperature - adverse effects
Humans
Hydrologic data
Hydrologic models
Hydrology
Hygiene
Indicators
Influence
Internet
Malalties víriques
Mathematical models
Medicine
Medicine and Health Sciences
Meteorology
Mosquitoes
Mosquits
Nonlinear Dynamics
Outbreaks
Parameter estimation
People and places
Precipitation
Public health
Rain
Rainfall
Risk Assessment
Risk Factors
Studies
Temperature
Time Factors
Trends
Tropical diseases
Vector-borne diseases
Virus diseases
Water shortages
Weather
Zika virus
Barbados
New York
United Kingdom > UK
United States > US
Brazil
Mexico
Florida
Spain
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Abstract 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. 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. 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.
AbstractList 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. 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. 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.
BackgroundOver 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 findingsDistributed 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.ConclusionWe 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.
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.
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. 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. 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.
Rachel Lowe and colleagues model for the delayed effects of climate change on outbreaks of Dengue in Barbados, an approach that can be extended to model risk for other arboviruses in the Caribbean
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.
Audience Academic
Author Lippi, Catherine A
Stewart-Ibarra, Anna M
Lowe, Rachel
Hinds, Avery Q J
Rollock, Leslie
Van Meerbeeck, Cédric J
Gasparrini, Antonio
Trotman, Adrian R
Mahon, Roché
Ryan, Sadie J
AuthorAffiliation 3 Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain
9 Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago
4 Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
11 Institute for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, New York, United States of America
Africa Program, UNITED STATES
7 Quantitative Disease Ecology and Conservation Lab Group, Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
8 Ministry of Health, St. Michael, Barbados
1 Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
12 Department of Medicine and Department of Public Health and Preventative Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
2 Centre for the Mathematical Modelling of Infectious Diseases, Lon
AuthorAffiliation_xml – name: 8 Ministry of Health, St. Michael, Barbados
– name: 10 School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
– name: 11 Institute for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, New York, United States of America
– name: 5 Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, United Kingdom
– name: 4 Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
– name: 1 Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
– name: 9 Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago
– name: 2 Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
– name: Africa Program, UNITED STATES
– name: 7 Quantitative Disease Ecology and Conservation Lab Group, Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
– name: 12 Department of Medicine and Department of Public Health and Preventative Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
– name: 6 Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
– name: 3 Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain
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  givenname: Rachel
  orcidid: 0000-0003-3939-7343
  surname: Lowe
  fullname: Lowe, Rachel
  organization: Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain
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  givenname: Antonio
  orcidid: 0000-0002-2271-3568
  surname: Gasparrini
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  organization: Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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  givenname: Cédric J
  surname: Van Meerbeeck
  fullname: Van Meerbeeck, Cédric J
  organization: Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
– sequence: 4
  givenname: Catherine A
  orcidid: 0000-0002-7988-0324
  surname: Lippi
  fullname: Lippi, Catherine A
  organization: Quantitative Disease Ecology and Conservation Lab Group, Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
– sequence: 5
  givenname: Roché
  orcidid: 0000-0001-9621-7033
  surname: Mahon
  fullname: Mahon, Roché
  organization: Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
– sequence: 6
  givenname: Adrian R
  orcidid: 0000-0002-3099-8671
  surname: Trotman
  fullname: Trotman, Adrian R
  organization: Caribbean Institute for Meteorology and Hydrology, St. James, Barbados
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  surname: Rollock
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  organization: Ministry of Health, St. Michael, Barbados
– sequence: 8
  givenname: Avery Q J
  surname: Hinds
  fullname: Hinds, Avery Q J
  organization: Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago
– sequence: 9
  givenname: Sadie J
  orcidid: 0000-0002-4308-6321
  surname: Ryan
  fullname: Ryan, Sadie J
  organization: School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
– sequence: 10
  givenname: Anna M
  surname: Stewart-Ibarra
  fullname: Stewart-Ibarra, Anna M
  organization: Department of Medicine and Department of Public Health and Preventative Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30016319$$D View this record in MEDLINE/PubMed
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Snippet 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...
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...
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...
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...
Rachel Lowe and colleagues model for the delayed effects of climate change on outbreaks of Dengue in Barbados, an approach that can be extended to model risk...
BackgroundOver the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to...
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...
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SubjectTerms Aedes - virology
Analysis
Animals
Aquatic insects
Barbados
Barbados - epidemiology
Bayes Theorem
Bayesian analysis
Biology and life sciences
Climate
Climate change
Climate effects
Decision making
Dengue
Dengue - diagnosis
Dengue - epidemiology
Dengue - transmission
Dengue - virology
Dengue fever
Dengue Virus - pathogenicity
Disease Outbreaks
Disease transmission
Disease Vectors
Drought
Droughts
Earth Sciences
Ecology
Ecology and Environmental Sciences
Environmental risk
Epidemics
Excess rainfall
Exposure
Fever
Floods
Health risk assessment
Hot Temperature - adverse effects
Humans
Hydrologic data
Hydrologic models
Hydrology
Hygiene
Indicators
Influence
Internet
Malalties víriques
Mathematical models
Medicine
Medicine and Health Sciences
Meteorology
Mosquitoes
Mosquits
Nonlinear Dynamics
Outbreaks
Parameter estimation
People and places
Precipitation
Public health
Rain
Rainfall
Risk Assessment
Risk Factors
Studies
Temperature
Time Factors
Trends
Tropical diseases
Vector-borne diseases
Virus diseases
Water shortages
Weather
Zika virus
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Title Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study
URI https://www.ncbi.nlm.nih.gov/pubmed/30016319
https://www.proquest.com/docview/2089341038
https://recercat.cat/handle/2072/350370
https://pubmed.ncbi.nlm.nih.gov/PMC6049902
https://doaj.org/article/815ccb75126b4ae79f51626ba3341684
http://dx.doi.org/10.1371/journal.pmed.1002613
Volume 15
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