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• W2005694077 abstract "To identify the meteorological drivers of dengue vector density and determine high- and low-risk transmission zones for dengue prevention and control in Cairns, Australia. Weekly adult female Ae. aegypti data were obtained from 79 double sticky ovitraps (SOs) located in Cairns for the period September 2007–May 2012. Maximum temperature, total rainfall and average relative humidity data were obtained from the Australian Bureau of Meteorology for the study period. Time series–distributed lag nonlinear models were used to assess the relationship between meteorological variables and vector density. Spatial autocorrelation was assessed via semivariography, and ordinary kriging was undertaken to predict vector density in Cairns. Ae. aegypti density was associated with temperature and rainfall. However, these relationships differed between short (0–6 weeks) and long (0–30 weeks) lag periods. Semivariograms showed that vector distributions were spatially autocorrelated in September 2007–May 2008 and January 2009–May 2009, and vector density maps identified high transmission zones in the most populated parts of Cairns city, as well as Machans Beach. Spatiotemporal patterns of Ae. aegypti in Cairns are complex, showing spatial autocorrelation and associations with temperature and rainfall. Sticky ovitraps should be placed no more than 1.2 km apart to ensure entomological coverage and efficient use of resources. Vector density maps provide evidence for the targeting of prevention and control activities. Further research is needed to explore the possibility of developing an early warning system of dengue based on meteorological and environmental factors. Identifier les déterminants météorologiques de la densité du vecteur de la dengue et déterminer les zones de haut et de bas risque de transmission pour la prévention et le control de la dengue à Cairns, en Australie. Les données hebdomadaires sur les femelles adultes d’Ae. aegypti ont été obtenues à partir de 79 pondoirs pièges à doubles collants situés à Cairns durant la période de septembre 2007 à mai 2012. La température maximale, les précipitations totales et la moyenne des données d'humidité relative ont été obtenues auprès du Bureau Australien de la Météorologie pour la période d’étude. Des modèles de distribution non linéaire des séries de latence ont été utilisés pour évaluer la relation entre les variables météorologiques et la densité du vecteur. L'autocorrélation spatiale évaluée par semivariography et par krigeage ordinaire a été entreprise afin de prédire la densité du vecteur à Cairns. La densité d’Ae. aegypti a été associée avec la température et les précipitations. Cependant, ces relations différaient entre les périodes de latence courte (0–6 semaines) et longue (0–30 semaines). Les semivariogrammes ont montré que les distributions du vecteur étaient spatialement autocorrélées dans la période de septembre 2007 à mai 2008 et de janvier 2009 à mai 2009, et les cartes de densité du vecteur ont identifié des zones de haute transmission dans les zones les plus peuplées de la ville de Cairns, ainsi que de Machans Beach. Les tendances spatio-temporelles d’Ae. aegypti à Cairns sont complexes, montrant une autocorrélation spatiale et des associations avec la température et les précipitations. Les pondoirs pièges collants ne doivent pas être placés à plus d'1,2 km les uns des autres afin d'assurer une couverture entomologique et l'utilisation efficace des ressources. Les cartes de densité vectorielle fournissent des preuves pour le ciblage des activités de prévention et de contrôle. Des recherches supplémentaires sont nécessaires pour explorer la possibilité de développer un système d'alerte précoce de la dengue basée sur les facteurs météorologiques et environnementaux. Identificar los factores meteorológicos con influencia sobre la densidad del vector del dengue y determinar las zonas de alto y bajo riesgo de transmisión para la prevención del dengue y su control en Cairns, Australia. Semanalmente se obtuvieron datos sobre hembras adultas de Ae. aegypti data de 79 trampas pegajosas dobles (TP) localizadas en Cairns entre Septiembre 2007 – Mayo 2012. Datos para el periodo del estudio de temperatura máxima, precipitación total y datos promedio de humedad relativa se obtuvieron de la Oficina Australiana de Meteorología. Se utilizaron modelos de retardos distribuidos de series temporales no lineales para evaluar la relación entre las variables meteorológicas y la densidad vectorial. La autocorrelación espacial se evaluó mediante semivariografía y se realizó un kriging ordinario para predecir la densidad vectorial en Cairns. La densidad de Ae. aegypti estaba asociada con la temperatura y las precipitaciones. Sin embargo, esta relación difería entre periodos de tiempo cortos (0–6 semanas) y largos (0–30 semanas). Los semivariogramas mostraban que las distribuciones vectoriales estaban espacialmente autocorrelacionadas entre Septiembre 2007 – Mayo 2008 y Enero 2009 – Mayo 2009 y los mapas de densidad vectorial identificaron áreas de transmisión alta en las partes más pobladas de la ciudad de Cairns, al igual que en la playa de Machans. Los patrones espaciotemporales de Ae. aegypti en Cairns son complejos, mostrando una autocorrelación espacial y asociaciones con la temperatura y las precipitaciones. Las trampas pegajosas deberían de colocarse con un máximo de 1.2 km de separación entre ellas, con el fin de asegurar la cobertura entomológica y un uso eficiente de recursos. Los mapas de densidad vectorial aportan evidencia para afinar en las actividades de prevención y de control. Se requieren más estudios para explorar la posibilidad de desarrollar un sistema de advertencia temprana para el dengue, basado en factores meteorológicos y ambientales. 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. Weekly Ae. aegypti surveillance data (numbers of adult female Ae. aegypti mosquitoes captured) were obtained from 79 double SOs located in Cairns for the period September 2007–May 2012 (Figure 1). Cairns is a low-lying coastal area with seasonal high temperatures and rainfall from November to May and cooler, drier conditions from June to October. Each SO comprises two black buckets. The top bucket is lined with a polybutylene adhesive panel; the base bucket is filled with water and contains an organic attractant (e.g. lucerne) and a pellet of the insect growth regulator methoprene, to prevent mosquito production in SOs (Ritchie et al. 2003). Dry glue was used in the SOs from May 2008–December 2008 and was later found to be less efficacious than the polybutylene; therefore, this period was excluded from all analyses. Data on numbers of male Ae. aegypti were also excluded from analyses because they do not transmit DF. Meteorological data were obtained from the Australian Bureau of Meteorology (BoM) for Cairns Aero (temperature and rainfall data) and Cairns Severin Street (humidity data) weather stations for the study period. Daily observations for maximum temperature and total rainfall were retrieved from the BoM website (http://www.bom.gov.au/climate/data/), and average daily relative humidity data were accessed via a data request. Daily observations were averaged by week (Figure 2). Spatial and temporal analyses of data were undertaken separately to examine patterns and relationships in the data more comprehensively. Models were fitted for a polynomial of degrees 2–5 to determine the overall effects of temperature, rainfall and humidity; a degree two polynomial (quadratic term) produced the lowest AIC score and was used in the final model. Variables were centred on the mean value, and a natural cubic spline smoothing function was used to correct for seasonality. The time trend was controlled by smoothing calendar time (4 years) with 5 degrees of freedom, determined by model comparison for 5, 6 and 7 degrees of freedom. The terms sin(2πt/52) and cos(2πt/52) were included in the model to control for the annual periodicity of Ae. aegypti density (Stolwijk et al. 1999). Lag periods were categorised as short term (0–6 weeks) and long term (0–30 weeks). Short-term lag periods were included in the model to account for the life cycle of Ae. aegypti, which generally takes 6 weeks or less. Long-term lags were included in the model to explore the influence of meteorological variables on Ae. aegypti populations over time and validate findings from a recent study that used data from 11 BG-sentinel traps to determine the feasibility of temporal predictive modelling using meteorological indices for dengue transmission in Cairns (Azil et al. 2010). Statistical analyses were undertaken using Stata/SE11 and the dlnm package in R statistical software, version 2.15.1 (R Foundation for Statistical Computing, Vienna Austria). Spatial autocorrelation, a primary assumption of interpolation, was assessed by generating exponential binned semivariograms using the variog function in the GeoR package in R for the periods: September 2007–May 2008; January 2009–May 2009; June 2009–May 2010; June 2010–May 2011; and June 2011–May 2012. Data comprised the mean weekly number of adult female Ae. aegypti at each of the 79 georeferenced SO locations across each time period. Covariance parameters were estimated by fitting weighted least squares (WLS) models to the semivariograms using the variofit function in the GeoR package. Resulting summary statistics were used to identify the precise distance of spatial autocorrelation in the data sets (note, one decimal degree is equivalent to approximately 111 km at the equator). Kriging is a set of spatial interpolation techniques that create a prediction surface based on measured values and distance to prediction locations, facilitated by a spatial autocorrelation model using semivariogram covariance parameters (Pfeiffer et al. 2008). Ordinary kriging was selected as the preferred model because the unknown mean is estimated for each location based on nearby data, providing flexibility for prediction. Conversely, simple kriging assumes a known mean for all locations and is defined prior to modelling. A rectangular grid of 4900 prediction locations (70 × 70), each measuring 120 m by 180 m, covering the study area was created to predict Ae. aegypti numbers at locations without SOs. Ordinary kriging assuming a Poisson normal model was performed on the grid using the pois.krige and pois.glm.control functions in the GeoRglm package in R, and Aedes density estimates (counts) were obtained for each prediction location. A Monte Carlo Markov Chain (MCMC) Metropolis–Hastings algorithm based on Langevin–Hastings updates and parameterisation specified in semivariography was employed to simulate the conditional distributions for gridded locations given data from SOs (Papaspiliopoulus et al. 2003). The number of MCMC iterations performed was 1000, thinning was specified as 1, burn-in set to 0, and, to ensure approximately 60 per cent of the proposals were accepted as required by Langevin–Hastings updates, the proposal variance was 0.6 (Christensen & Waagepetersen 2002). Further details of ordinary kriging using R are available elsewhere (Christensen & Ribeiro 2012). The gridded kriging estimates were exported to ArcMap via raster image files created in the rgdal package in R. Patterns of Ae. aegypti in Cairns varied from 2007 to 2012 (Figure 2). Weekly Ae. aegypti numbers peaked in 2007–2008 and decreased over the study period. Trends in temperature reflected trends in Ae. aegypti and were confirmed by the cross-correlation moving averages graph (Figure 3). Rainfall and humidity demonstrated moderate associations with Ae. aegypti. Temperature was the only significant predictor of Ae. aegypti in the negative binomial model (RR = 1.20, P = 6.64e-14). In the final DLNM model, rainfall at two lag degrees of freedom demonstrated a significant inverse relationship with Ae. aegypti. More simply, increased rainfall resulted in significantly fewer Ae. aegypti (RR = 0.52, P = 0.002). Findings for temperature (RR = 1.31, P = 0.054) and humidity (RR = 0.92, P = 0.56) were not significant. However, visual examination of trends at differing lag periods revealed a more complex association between the meteorological factors and vector density (Figure 4). Patterns of rainfall over short-term lags (0–6 weeks) were fairly stable. However, long-term trends showed increased rainfall from 0 to 15 weeks lag resulted in a significantly higher number of Ae. aegypti, and increased rainfall from 16 to 30 weeks resulted in significantly lower number of Ae. aegypti. Temperature from 0 to 6 weeks lag showed a decreasing – but not significant – association. Over long-term lags, increased temperature was significantly associated with increased vector density and from 25 to 30 weeks. For humidity, short-term lags demonstrated an increasing association, which was statistically significant from 3 to 5 weeks. Long-term lags also showed an increasing association and were significant from 15 to 25 weeks. Spatial autocorrelation was only apparent in the vector density data sets from September 2007 to May 2008 and from January 2009 to May 2009 (Figure 5), and the spatial analysis only considered these time periods. Semivariograms demonstrated spatial autocorrelation up to a distance of 0.0118 decimal degrees for September 2007–May 2008 and 0.0173 decimal degrees for January 2009–May 2009. More simply, the number of Ae. aegypti at a given SO location was autocorrelated with the number of mosquitoes at other SOs located within 1.2 km and 1.7 km for September 2007–May 2008 and January 2009–May 2009, respectively. This spatial autocorrelation enables prediction of Ae. aegypti density to locations without SOs via interpolation (kriging). Figures 6 and 7 present interpolated densities of Ae. aegypti in Cairns and Machans Beach for September 2007–May 2008 and January 2009–May 2009. Areas of high vector density were similar between years and were mostly confined to the Cairns city area, with an additional high density zone in the less-populated Machans Beach area. 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. We sincerely thank the Queensland Tropical Public Health Team and Dengue Action Response Team for their assistance and advice during this study. AC is supported by an Australian National Health and Medical Research Council Career Development Award." @default.
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• W2005694077 title "Spatiotemporal patterns of<i>Aedes aegypti</i>populations in Cairns, Australia: assessing drivers of dengue transmission" @default.
• W2005694077 cites W1481980842 @default.
• W2005694077 cites W1501444944 @default.
• W2005694077 cites W1506558804 @default.
• W2005694077 cites W1982782291 @default.
• W2005694077 cites W1999138782 @default.
• W2005694077 cites W2000414525 @default.
• W2005694077 cites W2010207445 @default.
• W2005694077 cites W2014079126 @default.
• W2005694077 cites W2015095931 @default.
• W2005694077 cites W2015181612 @default.
• W2005694077 cites W2017460741 @default.
• W2005694077 cites W2032825603 @default.
• W2005694077 cites W2033511980 @default.
• W2005694077 cites W2049229003 @default.
• W2005694077 cites W2057486780 @default.
• W2005694077 cites W2068901195 @default.
• W2005694077 cites W2071294321 @default.
• W2005694077 cites W2071929740 @default.
• W2005694077 cites W2092133388 @default.
• W2005694077 cites W2103723267 @default.
• W2005694077 cites W2104510707 @default.
• W2005694077 cites W2105723189 @default.
• W2005694077 cites W2108022477 @default.
• W2005694077 cites W2111912125 @default.
• W2005694077 cites W2114053157 @default.
• W2005694077 cites W2119204068 @default.
• W2005694077 cites W2121362530 @default.
• W2005694077 cites W2122798427 @default.
• W2005694077 cites W2140884072 @default.
• W2005694077 cites W2144409746 @default.
• W2005694077 cites W2145875833 @default.
• W2005694077 cites W2149252100 @default.
• W2005694077 cites W2153654879 @default.
• W2005694077 cites W2160883948 @default.
• W2005694077 cites W2164643570 @default.
• W2005694077 cites W2168929296 @default.
• W2005694077 cites W2169832189 @default.
• W2005694077 cites W2172444186 @default.
• W2005694077 cites W2173951421 @default.
• W2005694077 cites W2406560719 @default.
• W2005694077 cites W4254380578 @default.
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