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Dengue fever (DF) is an increasing public health concern, with a 30-fold increase in global incidence in the past 50 years (World Health Organization 2012a). Each year there are approximately 50–100 million reported cases of DF and more than 20 000 reported deaths from dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS) (World Health Organization 2012b). In Australia, epidemics occur in northern Queensland, with most cases in Cairns (Williams et al. 2010). Epidemics result from viraemic travellers importing DF during the wet season, when environmental conditions are favourable for breeding and survival of the DF vector, Aedes aegypti. Molecular epidemiology confirms a pattern of repeated imports seeding fresh outbreaks (Ritchie et al. 2002). Small outbreaks of locally acquired DF take place almost yearly in northern Queensland, with large outbreaks occurring every 4–5 years. Locally acquired DF cases in northern Queensland increased from 27 in 1991 to 69 in 2011, with the highest number of cases recorded in 2008–2009 (n = 1037) (Scott Ritchie, unpublished observation).
In the absence of a publicly available vaccine, and despite questionable evidence for the association between vector density and virus transmission (Scott et al. 2003), entomological surveillance is considered a key strategy for the control of DF (Ooi et al. 2006; Canyon 2007; Morrison et al. 2008). In Cairns, entomological surveillance is undertaken weekly and includes monitoring double sticky ovitraps (SOs), which are designed to attract gravid adult female Ae. aegypti mosquitoes (Ritchie et al. 2004; Chadee & Ritchie 2010).
When the number of mosquitoes collected in SOs exceeds one adult female Ae. aegypti per day or health authorities are notified of a DF case, control measures are implemented: physical removal of breeding sites, installation of lethal ovitraps, interior residual spraying (IRS) and community awareness and education campaigns. Testing of Wolbachia bacteria-infected mosquitoes, which decreases vector competence, is underway in Cairns; however, it may be a number of years before Ae. aegypti populations are entirely infected with Wolbachia (Hoffmann et al. 2011). In the meantime, targeting of vector control activities may be improved by the development of an early warning system using prospective meteorological data to predict areas of high vector density, using a proactive (preventive) rather than a reactive (control) framework (Racloz et al. 2012).
Evidence for the impact of meteorological factors on vector density is inconclusive (Jansen & Beebe 2010). An early study found that rainfall and humidity, but not temperature, were associated with increases in Ae. aegypti larval populations in India (Biswas et al. 1993). In a 2006 study, humidity and temperature were positively correlated with entomological indices (Favier et al. 2006), while more recent studies showed rainfall to be the only meteorological factor investigated that was a significant driver of vector density (Dibo et al. 2008; Fávaro et al. 2008; Miyazaki et al. 2009; Wan et al. 2009). Using lag periods to determine the influence of previous meteorological conditions on vector density reveals additional complexities. For example, rainfall, temperature and humidity were all significantly associated with larval density at different time points from 0–4 months prior to entomological collection date in Taiwan (Wu et al. 2007; Chen et al. 2010). Knowledge of temporal lags is particularly important for the design of climate-based surveillance systems, where identification of suitable climatic conditions at a given time point can trigger interventions to prevent subsequent vector-borne disease outbreaks.
Recent advances in geographical information systems (GISs) and geostatistical techniques have enhanced our understanding of the spatial dynamics of Aedes populations (Eisen & Lozano-Fuentes 2009; Higa 2011). Most commonly, GISs have been used for visualising the distribution and density of vector breeding sites in a given location (Sithiprasasna et al. 2004; Moreno-Sanchez et al. 2006; Tsuda et al. 2006; Chang et al. 2009). More advanced techniques include identifying and predicting high-risk transmission zones (Carbajo et al. 2001; Ali et al. 2003; Getis et al. 2003; Chansang & Kittayapong 2007).
This study investigates temporal relationships between rainfall, temperature, humidity and Ae. aegypti to determine the meteorological drivers of DF transmission in Cairns. It also examines spatial patterns of Ae. aegypti and uses interpolation techniques to predict vector density in locations without SOs to help identify high-risk transmission zones. Study implications are discussed in the context of integrated dengue surveillance and sustainable prevention and control in Cairns.
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These results build on findings from a recent temporal study undertaken in Cairns, to present a comprehensive assessment of DF transmission risk (Azil et al. 2010). Azil et al. (2010) found that increasing temperature 6 months prior to vector collection resulted in higher adult female Ae. aegypti density, suggesting that egg diapause and overwinter survival occur in Ae. aegypti populations in Cairns. However, the study was limited to 11 BG-sentinel traps and did not include spatial analysis. Our results confirmed that an increase in temperature 25–30 weeks prior to vector collection date resulted in a statistically significant increase in the number of Ae. aegypti found in SOs (Figure 4).
We also identified a significant inverse relationship between rainfall and Ae. aegypti density, which may be due to heavy rainfall increasing the number of water-filled containers suitable for oviposition and thus reducing the number of gravid female mosquitoes selecting a SO as a suitable breeding container. Heavy rainfall causing flushing of breeding sites may also contribute to reduced vector density; however, evidence suggests that larval and pupal Ae. aegypti populations are only slightly affected by heavy rainfall, perhaps due to adaptation to tropical habitats over time (Koenraadt & Harrington 2008). Previous studies found rainfall was a contributing factor in the transmission of DF; however, these studies focused on DF cases and did not include data on entomological indices (Hurtado-Diaz et al. 2007; Banu et al. 2011; Hu et al. 2011, 2012). Meteorological findings may inform DF management in Cairns, where control activities (e.g. physical removal of breeding sites) are scaled up following periods low rainfall and high temperature.
We used robust spatial interpolation methods to predict the density of Ae. aegypti in Cairns for improved targeting of prevention and control activities. To our knowledge, this is the first study to produce vector density (risk) maps in Cairns. As expected, risk maps showed high vector density zones in Cairns city area, where human population density is greatest: consistent with findings from a recent study in Colombia demonstrating the importance of human population density in DF transmission (Padmanabha et al. 2012). A high-risk zone seen in the less-populated Machans Beach area may be due to a profusion of vector breeding sites. The risk zone was slightly smaller and less dense in January-May 2009 compared to September 2007-May 2008, possibly demonstrating the impact of control efforts in this area following the 2008–2009 epidemic.
The consistency of risk zones between maps indicates that these areas are probably persistent breeding sites and should be targeted by the Queensland Dengue Action Response Team (DART) for regular control activities including the identification and elimination of breeding sites. Moreover, DART can direct resources away from low-risk transmission zones to these areas when required. Other studies have attempted to predict risk zones for Ae. aegypti in Australia; however, they focused on future risk for the whole country and did not include small-scale predictions for DF management in Cairns (Kearney et al. 2009; Williams et al. 2010).
There was no observed spatial autocorrelation in data sets for periods from late 2009 to 2012, which may have been due to decreases in Ae. aegypti numbers and a disruption of the spatial dynamics of vector populations following the expansion of control activities in 2009. In September 2007–May 2008, spatial autocorrelation was found up to a distance of 1.2 km, and in January 2009–May 2009, spatial autocorrelation was identified up to a distance of 1.7 km. These results indicate that SOs should be placed no further than 1.2 km apart to ensure that all of the spatial variations in vector density are captured by the sampling method. Denser sampling could be undertaken in areas of specific interest to capture finer-scale heterogeneity and facilitate targeted control. This information is useful for DART, who previously distributed SOs throughout Cairns according to the location of prior DF epidemic zones and human population density (Williams et al. 2006). Additionally, this information may be used to facilitate the distribution of SOs for DF vector surveillance in other parts of Queensland (e.g. Townsville), if required.
Our results showed the number of Ae. aegypti decreasing over the study period (2007–2012). Yet, unless prevention and control programmes are improved, these populations will resurge. Evidence supporting the correlation between vector density and DF infection is weak, with experts suggesting that even when Ae. aegypti population density is low, DF transmission can occur due to Ae. aegypti's regular blood meal feeding, which increases the likelihood of transmitting the disease (Kuno 1997). However, without other public health prevention tools such as vaccination, improving DF surveillance, prevention and control programmes with the aim of decreasing vector density should be prioritised (Scott et al. 2003). Indeed, the geographic expansion of Ae. albopictus from the Torres Strait to other parts of Australia, particularly Queensland, in the near future may result in higher vector density and increased DF transmission risk (Russell et al. 2005).
Spatial decision support systems (SDSSs) are increasingly recognised as essential for managing vector-borne diseases (Duncombe et al. 2012; Kelly et al. 2012). SDSSs utilise a range of routinely collected data and expert knowledge to explore spatiotemporal patterns of disease, including the prediction of potential epidemic locations. In 2008, a SDSS was developed to inform DF control in Mexico (Lozano-Fuentes et al. 2008). A more recent study in Cairns demonstrated space–time clustering of DF transmission during an epidemic to identify key tools for use in an integrated DF management system, such as a SDSS (Vazquez-Prokopec et al. 2010). DART currently uses GIS to identify locations of SOs, map vector control responses to DF cases and plan the distribution of IRS in Cairns; however, GIS capability is not fully utilised. The development of a SDSS for Cairns – building on a number of recent studies exploring the spatial and temporal patterns of DF, including this one – would enable data collection and reporting standardisation, targeting and coordination of control strategies and facilitation of resource allocation decisions via automated user-defined reports (Eamchan et al. 1989; Eisen & Eisen 2011).
This study has some limitations. Information on wind direction and strength, shading and the availability of and competition for food resources for Ae. aegypti – important drivers of vector survival – was not included in our analyses, possibly confounding associations between meteorological factors and vector density (Tun-Lin et al. 2000). Additionally, collected Ae. aegypti mosquitoes were not serologically tested for the presence of DF virus, and DF case data and information on serotype-specific herd immunity were not included in our analyses. Without a proven association between vector density and virus transmission, it was not possible to ascertain a direct link between our vector density predictions and the risk of DF in the human population.
Despite these limitations, our findings, along with previous studies, suggest that temperature and rainfall are associated with Ae. aegypti density. Vector density maps identify potential high transmission zones for targeting of prevention and control activities, and semivariograms provide evidence for the optimal spacing of SOs in Cairns. Further research into the spatiotemporal patterns of Ae. aegypti in Cairns should involve confirmation of the association between vector density and DF risk; additional exploration of the role of meteorological factors on Ae. aegypti to progress predictive climate-based models; and the development and consequent evaluation of the feasibility of an operational SDSS to improve the efficiency of surveillance and sustainability of DF prevention and control in Cairns.