Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

Chuang, Ting-Wu; Chaves, Luis Fernando; Chen, Po Jiang

doi:10.1371/journal.pone.0178698

Cited by 71 publications

(68 citation statements)

References 66 publications

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“…The ENSO event also influenced inter-annual rainfall variability [44], with Sumatra, Kalimantan and Malaysia being most affected [45]. This phenomenon is similar to ENSO 1992-2001 [46] and was also recorded in Taiwan in 2016 [47][48][49][50]. Notably, other causes of inter-annual variation in DF incidence have been reported in the literature, including varying levels of population immunity [51][52][53][54][55][56].…”

Section: Variablesmentioning

confidence: 69%

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Husnina

Clements

Wangdi

2019

Tropical Med Int Health

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Objectives To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors. Methods A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero‐inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. Results There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5–9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9–12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1–18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9–8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island. Conclusions Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk‐based surveillance might be warranted for dengue fever in Indonesia.

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Section: Variablesmentioning

confidence: 69%

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Husnina

Clements

Wangdi

2019

Tropical Med Int Health

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“…Based on these statistical relationships an experimental early warning system has been proposed for the Guadalcanal region of the Solomon Islands [109]. Chuang et al [110] used cross-wavelet coherence to evaluate the regional El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables for Taiwan. Their work revealed the importance of non-linear and lag effects of minimum temperature and precipitation on dengue.…”

Section: Enso and Health Forecastingmentioning

confidence: 99%

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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“…Four representative infectious diseases were included in the study: two mosquito-borne infections (Dengue and Malaria) and two infections that spread from person to person (Hand Foot and Mouth Disease (HFMD) and Chickenpox). For all four pathogens, a relationship has previously been found between incidence and climatic variables [40][41][42] or for there to be a seasonality to incidence [43]. Not all four pathogens were considered for each country: some are not present in each country while others are not captured in routine infectious disease surveillance systems.…”

Section: Sources Of Datamentioning

confidence: 99%

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

Chen

Chu

Chen

et al. 2018

Journal of Biomedical Informatics

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Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

Abstract: Background

Cited by 71 publications

References 66 publications

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

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

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

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