Geospatial Health 8(1), 2013, pp. 289-299
Spatial analysis of risk factors for transmission of the Barmah Forest virus in Queensland, Australia Suchithra Naish1, Kerrie Mengersen2, Shilu Tong1 1
School of Public Health and Social Work and Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia; 2Mathematical Sciences, Queensland University of Technology, Queensland, Australia
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
Spatial analysis of risk factors for transmission of the Barmah Forest virus in Queensland, Australia Suchithra Naish1, Kerrie Mengersen2, Shilu Tong1 1
School of Public Health and Social Work and Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia; 2Mathematical Sciences, Queensland University of Technology, Queensland, Australia
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
