This study reveals the spatial and temporal characteristics of BFV disease in Queensland using GIS tools and geostatistical analysis. These methods have been applied to infectious and vector-borne diseases to study the distribution patterns of the disease, to identify the high-risk areas or hot spots, and to determine the risk factors for the transmission of the disease 
. However, this is the first attempt to implement GIS mapping techniques to examine the distribution of BFV patterns in Queensland and to provide basic information for further investigation of the social and environmental factors responsible for changing disease patterns.
Our findings indicate that the BFV disease transmission occurs in all age groups but mostly affects men aged 40–
49 years (see ). The reasons for the higher incidence rates among males are unknown, but may include different exposure rates or other behavioural risk factors such as greater mobility and work and leisure related activities 
, however, this kind of information is unavailable in this study. Clearly, the relationship between the incidence rate of BFV disease and age, and gender needs to be better understood. Further investigation is warranted to determine the underlying differences in exposure or behavioural risk factors to prevent incidence spikes in certain age groups and in men.
The results of this study indicate significant variation in the spatial distribution of BFV disease in Queensland. In this analysis, disease mapping clearly shows that there has been the spatial expansion of BFV transmission in Queensland over recent years. Our results also showed that there has been an increasing trend in incidence rates of BFV disease during the study period. This is in contrast to the patterns observed in other mosquito-borne diseases such as RRV 
This study only focused on the spatio-temporal patterns of BFV disease transmission but did not explore its reasons, however, certain speculations could be made. In our study, BFV disease has been detected in all SLAs across the state. Our findings revealed that the coastal regions had the highest incidence rates and SIRs and the inland areas had the lowest (see &). This may be due to the several conditions that favour mosquito density, survival and longevity 
. A combination of flooding or high tides and heavy rainfall has often resulted in the BFV outbreaks across Australia 
. In addition, mosquitoes in coastal areas are believed to possess more infections than those in other regions 
. Therefore, it is evident that further studies on the role of climate on BFV disease mosquitoes would assist in identifying the reasons behind this phenomenal variation. To do this, regional mosquito data would be beneficial; however, such data are scarce in Queensland.
Regarding the temporal distribution of BFV disease, significant differences were noticeable across the state. Our results showed that the annual incidence rates were fluctuated considerably, with the peak incidence rate in 2008 (see ). These variations may due partly to local changes in climate and human behaviour 
and partly to under-funded vector control programs 
Our results strongly support previous studies that have reported a strong seasonal pattern of BFV disease 
. In addition, our recent study on time series analysis clearly showed the seasonal patterns of BFV disease 
. Generally, BFV disease transmission occurs during autumn and summer periods, with peaks recorded during the month of March, immediately after the main rainy season. This is because the mosquito population usually peaks in summer, resulting in a lagged impact on the seasonal variation of BFV disease 
. Additionally, the geographic distribution of mosquito species and their seasonal activity is mostly determined by climate 
. Clearly, climate, virus, vector survival and human related factors (eg. behaviour and immunity), all contribute and interact in determining BFV disease transmission. The question about how BFV disease is driven by climatic, socio-demographic and ecological factors will be addressed in further research.
Spatial autocorrelation and semi-variogram analysis are valuable tools to study the spatial patterns over time. The semi-variogram estimators used in this paper directly account for population size, attenuating the influence of less reliable rates recorded in sparsely populated areas. In this study, we found strong evidence of spatial autocorrelation of BFV disease across the state using the global Moran's I statistic. Maps created from kriging and interpolation revealed that BFV disease was spatially and temporally distributed. Further studies of local environmental and socio-demographic factors that operate at smaller spatial scales are crucial for improving the understanding of the spatial and temporal patterns of BFV disease. Moreover, further investigation is warranted to understand the effect of climatic and topographic factors on BFV disease transmission in the study area.
There could be issues in monitoring and reporting BFV disease notification data. The clinically proven cases on BFV disease were provided by Queensland Department of Health. BFV disease is one of the notified infectious diseases in Australia, and is required to be reported to the health authority by law. This disease has been under formal surveillance by Australian government since 1993. Issues regarding data reliability were discussed by Russell 
. However, there are likely to be subclinical cases that are not reported or diagnosed. Underreporting is also likely to occur when people infected with BFV disease but did not seek medical attention. Nevertheless, these issues cannot entirely account for the geographic distribution of BFV disease across Queensland.
Our findings are consistent with the previous studies 
based on this population. In all these studies, BFV cases were distributed with large variation in each year in Queensland. Our findings are in contrast to the report from Communicable Disease Branch, Queensland Department of Health 
. The two studies differ in a number of ways. Firstly, our data analysis was based on SLAs and the report was based on Queensland area health services. Secondly, our study population size was calculated based on 2001 and 2006 census years and for the remaining years, it was calculated based on population growth whereas the report was based on estimated resident population 
. Thirdly, our study period was for the years 1993 to 2008 whereas the report was for 1997–2006 
. More importantly, our incidence rates were standardised by age/gender using Queensland total population as the reference, while crude incidence rates were used in the report 
This study has three major strengths. Firstly, this is the first study to examine the geographic variation of BFV disease across geo-political borders in Queensland using GIS techniques. This study lays a foundation for further investigation of the spatial and temporal patterns and the risk factors of this disease. Secondly, the results of this study demonstrate that GIS mapping techniques may be used as a tool to quickly display information and generate maps to highlight BFV disease risk-prone areas for developing more effective control and prevention strategies. The maps could be used to suggest high-risk areas where further investigation should be focused, to identify whether increased disease surveillance measures or possible control activities are warranted. Finally, the BFV disease data used in this study are quite comprehensive, covering the whole Queensland for 16 years.
The study has also three key limitations. First, in our analysis, the quality of disease surveillance system may vary with place and time as the awareness of BFV disease among medical professionals and public may have increased over recent years. However, heterogeneity of increased BFV activities suggests that the BFV transmission pattern is unlikely to be entirely accounted for by a detection/surveillance artifact. Second, the locations where BFV cases were notified may differ from those where they caught the disease, particularly during holiday periods, and misclassification bias is inevitable to some extent. Finally, in this study, we aimed to examine the distribution patterns of BFV disease spatially and temporally at the smallest geographical unit of the Australian census, i.e., the SLA level. This study is an ecological design and individual level data are unavailable for this study. Therefore, point-based analysis of the data is beyond the scope of this study.
In conclusion, this study has revealed that the spatio-temporal patterns of BFV disease vary significantly in Queensland as the study has highlighted that there are different transmission patterns in SLAs between coastal and inland regions. The study has also concluded that the geographic distribution of BFV disease appears to have expanded over recent decades. This is based on the results (see &) and on the observation that BFV disease has spread from north to south and west during 1993–2008. The disease maps may be useful for enhanced BFV control activities. There is a lack of knowledge on the transmission dynamics of BFV disease in Australia and this study may help to understand the distribution of BFV disease in Queensland. Future research should focus on the spectrum of risk factors for BFV disease transmission and the development of early warning systems which are necessary to improve the effectiveness and efficiency of BFV prevention and control programs.