Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Abstract

BackgroundBarmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis.Methods/Principal FindingsWe calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state.Conclusions/SignificanceThis is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Spatial analysis of risk factors for transmission of the Barmah Forest virus in Queensland, Australia

“…While this is in agreement with previous analyses indicating higher BFV incidences along coastal areas (Naish et al, 2006(Naish et al, , 2009(Naish et al, , 2011a(Naish et al, , 2011b, distance from the coast has not been previously identified as a significant risk factor. Perhaps, those areas which are closer to coast had more BFV cases, more BFV vectors, higher population density and more outdoor activities, on average (Australian Bureau of Statistics, 2011).…”

“…Additionally, this study did not consider analysing age and gender differences as it was out of the study scope. Second, there could be issues in monitoring and reporting BFV disease notification data and these were discussed in our previous study (Naish et al, 2011a). Moreover, the location of infection site where BFV cases were notified may differ from those where they contracted the disease, particularly during holidays, and misclassification bias is inevitable to some extent.…”

“…However, most of the previous studies have only employed GIS for descriptive analyses (Quinn et al, 2005;Pelecanos et al, 2011). Few empirical studies have explored the disease risk using spatial pattern analysis, which generates more precise information about high-risk areas of disease transmission (Naish et al, 2011a(Naish et al, , 2011b. As extensively discussed in the geography, spatial statistics and spatial epidemiology, ignoring possible spatial effects (such as spatial dependence and heterogeneity) in data analysis Fig.…”

“…The change in approach has been warranted by the fact that traditional application of ordinary least square (OLS) regression for the analysis of outbreaks assumes that there is no spatial dependence/clustering. As evidenced by a number of studies (Quinn et al, 2005;Naish et al, 2011a;Pelecanos et al, 2011), this assumption is not in accord with the epidemiologic characteristics of BFV disease, because BFV incidence in a specified spatial unit (such as local government area (LGA) or statistical local area (SLA)) is likely to be dependent on location. In other words, units that are close to each other are more likely to have similar BFV incidence rates, depending on the degree of interaction between the populations in neighbouring units and on variations in climatic, socioeconomic and ecological factors that influence BFV disease (Quinn et al, 2005;Naish et al, 2011a;Pelecanos et al, 2011).…”

Infections and arthritis

“…Tests should be repeated twice, at least 10e14 days apart. A fourfold increase in IgG titer fulfills the serological criterion [83]. Other serological tests that can be used include immunofluorescence for IgG or IgM, hemagglutination inhibition, complement fixation titers, and neutralization titers.…”