Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Naish, Suchithra; Dale, Pat; Mackenzie, John S.; McBride, William J. H.; Mengersen, Kerrie; Tong, Shilu

doi:10.1186/1471-2334-14-167

Cited by 291 publications

(270 citation statements)

References 70 publications

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“…Entre dichos factores sobresalen los cambios climáticos, los ciclos del vector, los serotipos circulantes y las dinámicas poblacionales (10,13,20). De todos ellos, el clima ha sido uno de los factores más estudiados, específicamente los cambios de la temperatura, la precipitación y la humedad, los cuales influyen en los procesos de alimentación, supervivencia y proliferación del vector, así como en la replicación del virus (11,29,30). Los resultados de los estudios hechos en Ecuador sugieren que existe coherencia entre la incidencia del dengue y la temperatura en ciclos bianuales y anuales (13), mientras que en Vietnam se han encontrado asociaciones del dengue con la humedad, la temperatura y la precipitación en modo anual (24).…”

Section: Discussionunclassified

Análisis exploratorio de ondículas de los patrones de estacionalidad del dengue en Colombia

et al. 2015

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Introducción. El dengue tiene un comportamiento estacional asociado a los cambios climáticos, los ciclos del vector, los serotipos circulantes y las dinámicas poblacionales. El análisis de ondículas permite descomponer una serie de tiempo muy larga en sus componentes de tiempo calendario y periodo. Esta es la primera vez que se utiliza esta técnica para generar un modelo exploratorio del comportamiento del dengue en Colombia. Objetivo. Examinar los patrones de estacionalidad interanual del dengue en Colombia, en particular en los cinco municipios más endémicos, para el periodo 2007 a 2012, y de los ciclos entre años entre 1978 y 2013 a nivel nacional. Materiales y métodos. Se hizo un análisis exploratorio de ondículas con base en los datos de los casos incidentes de dengue reportados por semana epidemiológica en el periodo de 2007 a 2012, y por año, en el periodo de 1978 a 2013. Se utilizó un modelo autorregresivo de primer orden como hipótesis nula. Resultados. Fue evidente el efecto de la epidemia de 2010 sobre la serie de tiempo a nivel nacional y la de los cinco municipios. Se observaron diferencias en los patrones de estacionalidad interanual por municipio. Asimismo, a nivel nacional se hallaron ciclos de dos a cinco años desde el 2004. Conclusiones. El análisis de ondícula permite estudiar una serie de tiempo larga con patrones de estacionalidad variables, como en el caso del dengue en Colombia, e identificar diferencias por regiones. Es necesario explorar estos patrones en niveles de agregación inferiores y evaluar su relación con diversas variables predictoras.

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Section: Discussionunclassified

Análisis exploratorio de ondículas de los patrones de estacionalidad del dengue en Colombia

et al. 2015

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“…The climate change can result in a disease incident which is related to environment, including dengue. The climate parameters related to dengue among others are temperature, humidity and rain fall (Naish et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Determination of Environmental Factors Affecting Dengue Incidence in Sleman District, Yogyakarta, Indonesia

Kesetyaningsih¹,

Andarini²,

Sudarto³

et al. 2018

AJID

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Background: Dengue is a disease related to the environment that spreads rapidly. Prevention movement is considered ineffective; therefore, a more efficient early warning system is required. It is required strongly correlated variables to as predictor in early warning system. This study aims to identify the environmental conditions associated with dengue. Materials and methods: This ecological study was conducted on five sub-districts selected based on the trend of the incidence. Data land cover and elevation obtained using GIS. Climate data were obtained from Meteorology and Climatology and Geophysics Agency of Yogyakarta. Results: There were 1.150 dengue cases from 2008-2013 obtained from District Health Office. The spatial pattern is clustered in all sub-districts (Z-score < -2.58). There is a positive correlation between land cover and dengue (p 0.000; r 0.284) and a negative correlation between elevation areas and dengue (p 0.000; r -0.127). Multiple Regression Test shows the effect of humidity (p 0.000) and rainfall (p 0.002) with a contribution of 13.5% -27.4% (r 2 0.135 -0.274), while temperature has no effect in all sub-districts (p > 0.05). There is no effect of climate parameters in sporadic dengue areas (p > 0.05). Conclusion: It is concluded that dengue in Sleman is clustered and associated with the environment parameter, even though it does not have close correlation. High elevated and small building area is consistent with the lower dengue cases. Humidity and rainfall affect dengue, but temperature does not affect dengue.

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“…Many studies have found associations between climatic factors and dengue transmission (see (Naish et al, 2014;Thai and Anders, 2011) for a review). However, modelling approaches and methods to account for climate factors within the model vary considerably, which could lead to differential results regarding the relationships and time lags between climatic factors and dengue relative risk.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the added value of climate information in a spatio-temporal dengue model

Lowe

Cazelles

Rácz

et al. 2015

Stoch Environ Res Risk Assess

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Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from one month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA).This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8% of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79% of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

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Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Cited by 291 publications

References 70 publications

Análisis exploratorio de ondículas de los patrones de estacionalidad del dengue en Colombia

Análisis exploratorio de ondículas de los patrones de estacionalidad del dengue en Colombia

Determination of Environmental Factors Affecting Dengue Incidence in Sleman District, Yogyakarta, Indonesia

Quantifying the added value of climate information in a spatio-temporal dengue model

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