Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996-2005

Tipayamongkholgul, Mathuros; Fang, Chi-Tai; Klinchan, Suratsawadee; Liu, Chung-Ming; King, Chwan-Chuen

doi:10.1186/1471-2458-9-422

Cited by 102 publications

(97 citation statements)

References 31 publications

(61 reference statements)

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“…These results suggest that the HFRS incidence dynamics are interrelated with climate change and the MEI can serve as a potential predictor of HFRS occurrence. We notice that similar results have been found for diseases like dengue fever, dengue hemorrhagic fever, hantavirus cardiopulmonary syndrome, and malaria [68][69][70][71]. Moreover, regional precipitation is known to be influenced by ENSO, showing the strongest interrelation with climate variability around the Globe [72].…”

Section: Discussionsupporting

confidence: 84%

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

Christakos

Zhang

et al. 2017

Front. Appl. Math. Stat.

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Background: Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, especially in Heilongjiang province (90% of all reported HFRS cases worldwide occur in China). The dynamic identification of high HFRS incidence spatiotemporal regions and the quantitative assessment of HFRS associations with climate change in Heilongjiang province can provide valuable guidance for HFRS monitoring, preventing and control. Yet, so far there exist very few and of limited scope quantitative studies of the spatiotemporal HFRS spread and its climatic associations in Heilongjiang province. Making up for this lack of quantitative studies is the reason for the development of the present work.Method: To address this need, the well-known Bayesian maximum entropy (BME) method of space-time modeling and mapping together with its recently proposed variant, the projected BME (P-BME) method, were employed in this work to perform a composite space-time analysis and mapping of HFRS incidence in Heilongjiang province during the years 2005-2013. Also, using multivariate El Niño-Southern Oscillation index as a proxy, we proposed a combination of Hilbert-Huang transform and wavelet analysis to study the "HFRS incidence-climate change" associations. Results:The main results of this work were two-fold: (1) three core areas were identified with high HFRS incidences that were spatially distributed and exhibited distinct biomodal temporal patterns in the eastern, western, and southern parts of Heilongjiang province; and (2) there exists a considerable association between HFRS incidence and climate change, particularly, an ∼6 months period coherency was clearly detected. Conclusions:The combination of modern space-time modeling and mapping techniques (P-BME theory, Hilbert-Huang spectrum analysis, and wavelet analysis) used in this work led to valuable quantitative findings concerning the spatiotemporal spread of HFRS incidence in Heilongjiang province and its association with climate change. Our essential findings include the identification of three core areas with high HFRS incidences in Heilongjiang province, and considerable evidence that HFRS incidence is closely related to climate change.

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Section: Discussionsupporting

confidence: 84%

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

Christakos

Zhang

et al. 2017

Front. Appl. Math. Stat.

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“…Several previous studies that use climate to model dengue fever include an autoregressive time series component based on the idea that the current value of the time series can be explained as a function of past values (Hii et al, 2012;Tipayamongkholgul et al, 2009). However, these studies do not always quantify the contribution of an autoregressive lagged disease term compared to climate covariates to the variance explained by such a model.…”

Section: Discussionmentioning

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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“…That said, the tendency to store water during dry conditions may well provide suitable breeding sites for the dengue vector. In contrast to Johansson et al [61], Tipayamongkholguln et al [67], analysing dengue data for Thailand using Poisson regression, found that up to 22% (in eight northern inland mountainous provinces) and 15% (in five southern tropical coastal provinces) of the variation in the monthly incidence of dengue cases were attributable to global ENSO cycles as described by the ENSO multivariate and sea level pressure indices, with the tendency for dengue incidence to increase during El Niño phases. However, the authors noted some geographical heterogeneity in ENSO dengue associations, with not all individual provinces revealing statistically significant associations.…”

Section: Denguementioning

confidence: 98%

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

McGregor

Ebi

2018

Atmosphere

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Abstract:The El Niño Southern Oscillation (ENSO) is an important mode of climatic variability that exerts a discernible impact on ecosystems and society through alterations in climate patterns. For this reason, ENSO has attracted much interest in the climate and health science community, with many analysts investigating ENSO health links through considering the degree of dependency of the incidence of a range of climate diseases on the occurrence of El Niño events. Because of the mounting interest in the relationship between ENSO as a major mode of climatic variability and health, this paper presents an overview of the basic characteristics of the ENSO phenomenon and its climate impacts, discusses the use of ENSO indices in climate and health research, and outlines the present understanding of ENSO health associations. Also touched upon are ENSO-based seasonal health forecasting and the possible impacts of climate change on ENSO and the implications this holds for future assessments of ENSO health associations. The review concludes that there is still some way to go before a thorough understanding of the association between ENSO and health is achieved, with a need to move beyond analyses undertaken through a purely statistical lens, with due acknowledgement that ENSO is a complex non-canonical phenomenon, and that simple ENSO health associations should not be expected.

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Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996-2005

Cited by 102 publications

References 31 publications

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

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

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

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