Regional variability in relationships between climate and dengue/DHF in Indonesia

Abstract

Since 1970, the worldwide distribution, frequency and intensity of epidemics of dengue and dengue haemorrhagic fever (DHF) have increased dramatically. In Indonesia, as elsewhere, the geographic distribution and behaviour of the two main vectors –Aedes aegypti and Aedes albopictus– and the consequent transmission dynamics of the disease are strongly influenced by climate. Monthly incidence data were examined in relation to monthly data for temperature, rainfall, rainfall anomalies, humidity and the Southern Oscillation Index for 1992–2001. Focusing on eight provinces, significant Pearson correlations were observed between dengue/DHF incidence and at least one climate variable (r = ±0.2 to ±0.43; P < 0.05). Multiple regression analyses showed that 12.9–24.5 per cent of variance in incidence was explained by two or three climate variables in each province (P < 0.1–0.01). Rainfall appears to be the principal climatic agent affecting the geographic distribution and temporal pattern of incidence while temperature appears to play a critical role in outbreak intensity. Wide regional and temporal variations in the strength and nature of the observed associations led to the identification of three groups of provinces where increases in dengue/DHF incidence were variously associated with increased rainfall, decreased rainfall and/or high susceptibility to climate variability. Although climatic factors play an important role in explaining the timing and intensity of dengue/DHF outbreaks, a wide range of other factors specific to local environments also appear to be involved – information that may assist in the prediction and mitigation of regional dengue/DHF outbreaks.

A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan

“…The same predictors were also used in the Veracruz DF study with the lags of 20, 20, and 3 weeks, respectively (Hurtado-Diaz et al 2007). In Indonesia, a study showed that the significant temporal lags for rainfall and temperature were 1 and 2 months, respectively; moreover, the humidity and SOI became significant and were included in the analysis (Arcari et al 2007). In addition, an EWS model should account for the spatial dependence of dengue incidences, which describes the spatial spreading and propagation pattern of a DF epidemic (Mondini et al 2005;Tran and Raffy 2006;Wen et al 2006).…”

“…In addition, an EWS model should account for the spatial dependence of dengue incidences, which describes the spatial spreading and propagation pattern of a DF epidemic (Mondini et al 2005;Tran and Raffy 2006;Wen et al 2006). It is worth noticing that, in most studies, the prediction of DF incidences during an epidemic underestimated the magnitude of the infections (Arcari et al 2007). This may be due to the significant uncertainty characterizing many synergetic factors that contribute to the process of dengue virus transmission, including virus serotype, mosquitoes' sizes, and spatial distribution and dynamic interaction of human hosts.…”

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

“…Typically, the most significant time lags between temperature/precipitation and dengue are found to be around 1-2 months (Arcari et al, 2007;Cheong et al, 2013;Descloux et al, 2012;García et al, 2011;Gharbi et al, 2011;Gomes et al, 2012;Jeefoo et al, 2010;Lowe et al, 2011;Wu et al, 2007) , although some studies report lags of around 3-4 months (Bi et al, 2001;Chen et al, 2012;Depradine and Lovell, 2004;Yu et al, 2011) .…”

ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data

“…In a major ENSO event, such as in the 1997/1998 ENSO, it decreased the rainfall intensity and delayed the wet season [5]. Such impacts may cause socioeconomic effects, such as the decline of health quality [6] and ecological impacts, such as forest fires [7] and tree mortality [8,9] during the warmer phase of ENSO (El Nino). Adequate identification, assessment, and monitoring efforts are required to formulate proper mitigation actions in order to avoid the adverse effects of an intensified ENSO and to prevent any further negative ecological and social economics impacts.…”