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

Abstract

Abstract. Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia but few data are available on the risk factors. We assessed the impact of spatial climatic, socioeconomic and ecological factors on the transmission of BFV disease in Queensland, Australia, using spatial regression. All our analyses indicate that spatial lag models provide a superior fit to the data compared to spatial error and ordinary least square models. The residuals of the spatial lag models were found to be uncorrelated, indicating that the models adequately account for spatial and temporal autocorrelation. Our results revealed that minimum temperature, distance from coast and low tide were negatively and rainfall was positively associated with BFV disease in coastal areas, whereas minimum temperature and high tide were negatively and rainfall was positively associated with BFV disease (all P-value <0.05). The study demonstrates that BFV disease is more densely distributed in coastal areas and is influenced by climatic and ecological factors. The spatial analytical approach used in this study may have ramifications in the planning and implementation of BFV disease prevention and control programmes.

The Role of Temperature in Transmission of Zoonotic Arboviruses

“…West Nile virus is by far the most studied enzootic arbovirus (Table 3). Models have been reviewed for WNV [15,149,163,168], Japanese encephalitis virus (JEV) [15,16,20], RVFV [14,15], RRV [7], and Barmah forest virus (BFV) [7,169,170]. Modeling studies on temperature for remaining viruses include: EEEV [3,171], LACV [13], MVEV [3], Sindbis virus (SINV) [3,172,173], St. Louis encephalitis virus (SLEV) [3,174], and Western equine encephalitis virus (WEEV) ([174,175,176] and citations therein, [3]).…”

“…BFV risk increased with increasing minimum temperatures in coastal regions [169]. BFV risk was decreased with increasing minimum temperature at the scale of the entire state of Queensland [170], but this effect could be due to a contrast between interior and coastal areas rather than an inconsistent temperature effect (i.e., Simpson’s paradox [249,250]).…”

Mapping the Morbidity Risk Associated with Coal Mining in Queensland, Australia

“…The analysis considered the higher concentration of the Queensland population around urban areas and the large expansion of coal mines in rural and less populated areas by implementing a Bayesian spatial regression. Spatial regression models incorporate the spatial correlation between geographical areas to provide robust risk estimates and better quantification of their uncertainty; an approach increasingly used in epidemiological studies of mortality and morbidity in Queensland [33][34][35][36]. This study estimated the association of coal production with hospitalisations due to six disease groups in Queensland, using a Bayesian spatial hierarchical analysis.…”