An epidemic of dengue 3 in far north Queensland, 1997–1999

Jn, Hanna; Sa, Ritchie; Da, Phillips; Il, Serafin; Sl, Hills; Af, van den Hurk; At, Pyke; Wj, McBride; Mg, Amadio; Rl, Spark

doi:10.5694/j.1326-5377.2001.tb143213.x

Cited by 56 publications

(36 citation statements)

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“…Such pattern of introduction and spread was consistent with descriptions of previous Cairns epidemics [13], [14], [17], and may be the product of the combination of demographic, environmental and entomologic characteristics unique to such areas. The high density of elevated old ‘Queenslander’ houses found in PP and CN, together with the presence of abundant tropical vegetation around them, provides optimal conditions for Ae.…”

Section: Discussionsupporting

confidence: 87%

“…Consistent with global trends, outbreaks of dengue have become more frequent and severe in Australia, occurring exclusively in north Queensland (NQ) [12], [13], [14], [15], [16], [17]. Originated by viral introduction via infected travelers from endemic regions, dengue outbreaks in this region are characterized by a rapid spread both in time and space, as a result of the prevailing high Ae.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Spatial Dimension of Dengue Virus Epidemic Spread within a Tropical Urban Environment

et al. 2010

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BackgroundDengue infection spread in naive populations occurs in an explosive and widespread fashion primarily due to the absence of population herd immunity, the population dynamics and dispersal of Ae. aegypti, and the movement of individuals within the urban space. Knowledge on the relative contribution of such factors to the spatial dimension of dengue virus spread has been limited. In the present study we analyzed the spatio-temporal pattern of a large dengue virus-2 (DENV-2) outbreak that affected the Australian city of Cairns (north Queensland) in 2003, quantified the relationship between dengue transmission and distance to the epidemic's index case (IC), evaluated the effects of indoor residual spraying (IRS) on the odds of dengue infection, and generated recommendations for city-wide dengue surveillance and control.Methods and FindingsWe retrospectively analyzed data from 383 DENV-2 confirmed cases and 1,163 IRS applications performed during the 25-week epidemic period. Spatial (local k-function, angular wavelets) and space-time (Knox test) analyses quantified the intensity and directionality of clustering of dengue cases, whereas a semi-parametric Bayesian space-time regression assessed the impact of IRS and spatial autocorrelation in the odds of weekly dengue infection. About 63% of the cases clustered up to 800 m around the IC's house. Most cases were distributed in the NW-SE axis as a consequence of the spatial arrangement of blocks within the city and, possibly, the prevailing winds. Space-time analysis showed that DENV-2 infection spread rapidly, generating 18 clusters (comprising 65% of all cases), and that these clusters varied in extent as a function of their distance to the IC's residence. IRS applications had a significant protective effect in the further occurrence of dengue cases, but only when they reached coverage of 60% or more of the neighboring premises of a house.ConclusionBy applying sound statistical analysis to a very detailed dataset from one of the largest outbreaks that affected the city of Cairns in recent times, we not only described the spread of dengue virus with high detail but also quantified the spatio-temporal dimension of dengue virus transmission within this complex urban environment. In areas susceptible to non-periodic dengue epidemics, effective disease prevention and control would depend on the prompt response to introduced cases. We foresee that some of the results and recommendations derived from our study may also be applicable to other areas currently affected or potentially subject to dengue epidemics.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Quantifying the Spatial Dimension of Dengue Virus Epidemic Spread within a Tropical Urban Environment

et al. 2010

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“…Here, BI values ranged from 1 to 66 during periods when dengue transmission was recorded (Figure 2). In other studies, both recent [46] and historic [47], dengue transmission was recorded when BI values were lower than the widely accepted transmission threshold of 5. Notably, in a study in Trinidad, ‘high’ transmission (25–40 cases for 75% of sample ‘cycles’) took place in areas with relatively ‘low’ abundance (∼BI<5) while, conversely, a consistently higher BI of 5.4 in neighbouring areas did not result in dengue cases [34].…”

Section: Resultsmentioning

confidence: 99%

Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence

2014

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BackgroundDespite doubts about methods used and the association between vector density and dengue transmission, routine sampling of mosquito vector populations is common in dengue-endemic countries worldwide. This study examined the evidence from published studies for the existence of any quantitative relationship between vector indices and dengue cases.Methodology/Principal FindingsFrom a total of 1205 papers identified in database searches following Cochrane and PRISMA Group guidelines, 18 were included for review. Eligibility criteria included 3-month study duration and dengue case confirmation by WHO case definition and/or serology.A range of designs were seen, particularly in spatial sampling and analyses, and all but 3 were classed as weak study designs. Eleven of eighteen studies generated Stegomyia indices from combined larval and pupal data. Adult vector data were reported in only three studies. Of thirteen studies that investigated associations between vector indices and dengue cases, 4 reported positive correlations, 4 found no correlation and 5 reported ambiguous or inconclusive associations. Six out of 7 studies that measured Breteau Indices reported dengue transmission at levels below the currently accepted threshold of 5.Conclusions/SignificanceThere was little evidence of quantifiable associations between vector indices and dengue transmission that could reliably be used for outbreak prediction. This review highlighted the need for standardized sampling protocols that adequately consider dengue spatial heterogeneity. Recommendations for more appropriately designed studies include: standardized study design to elucidate the relationship between vector abundance and dengue transmission; adult mosquito sampling should be routine; single values of Breteau or other indices are not reliable universal dengue transmission thresholds; better knowledge of vector ecology is required.

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“…Over the past 17 years (1993–2009), 6,271 laboratory-confirmed DF cases have been reported to the Australian Department of Health and Ageing (2010). Major outbreaks have occurred in northern Queensland, centered in Cairns, Townsville, and the Torres Strait islands (Hanna et al 1998, 2001; Tropical Public Health Unit Network 2004). Although DF is not naturally endemic in Australia, the dengue vector— Aedes aegypti —inhabits northern Queensland, and outbreaks can occur when the virus is introduced to the local mosquito population by infected international travelers and migrants or residents who were infected while traveling overseas (Tropical Public Health Unit Network 2004).…”

mentioning

confidence: 99%

Spatial Patterns and Socioecological Drivers of Dengue Fever Transmission in Queensland, Australia

Clements

Williams

et al. 2012

Environ Health Perspect

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Background: Understanding how socioecological factors affect the transmission of dengue fever (DF) may help to develop an early warning system of DF.Objectives: We examined the impact of socioecological factors on the transmission of DF and assessed potential predictors of locally acquired and overseas-acquired cases of DF in Queensland, Australia.Methods: We obtained data from Queensland Health on the numbers of notified DF cases by local government area (LGA) in Queensland for the period 1 January 2002 through 31 December 2005. Data on weather and the socioeconomic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive model was fitted at the LGA level to quantify the relationship between DF and socioecological factors.Results: Our estimates suggest an increase in locally acquired DF of 6% [95% credible interval (CI): 2%, 11%] and 61% (95% CI: 2%, 241%) in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature between 2002 and 2005, respectively. By contrast, overseas-acquired DF cases increased by 1% (95% CI: 0%, 3%) and by 1% (95% CI: 0%, 2%) in association with a 1-mm increase in average monthly rainfall and a 1-unit increase in average socioeconomic index, respectively.Conclusions: Socioecological factors appear to influence the transmission of DF in Queensland, but the drivers of locally acquired and overseas-acquired DF may differ. DF risk is spatially clustered with different patterns for locally acquired and overseas-acquired cases.

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An epidemic of dengue 3 in far north Queensland, 1997–1999

Cited by 56 publications

References 10 publications

Quantifying the Spatial Dimension of Dengue Virus Epidemic Spread within a Tropical Urban Environment

Quantifying the Spatial Dimension of Dengue Virus Epidemic Spread within a Tropical Urban Environment

Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence

Spatial Patterns and Socioecological Drivers of Dengue Fever Transmission in Queensland, Australia

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