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

PLoS ONE, published 13 Oct 2011

Suchithra Naish, Wenbiao Hu, Kerrie Mengersen, Shilu Tong

“Background: Barmah 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.

Maps showing the inverse distance weighting interpolated incidence rates of BFV disease by SLA over different periods (A:1993–1996, B:1997–2000, C:2001–2004 and D:2005–2008).

Maps showing the inverse distance weighting interpolated incidence rates of BFV disease by SLA over different periods (A:1993–1996, B:1997–2000, C:2001–2004 and D:2005–2008).

“Methods/Principal Findings: We 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

“Conclusions/Significance: This 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.”

A Spatial Analysis of R&D: The Role of Industry Proximity

CRENoSCRENoS Working Paper Number 2012_04 (2012)

O.A. Carboni

“This paper employs individual firm data in order to check the existence of industry-spatial effects alongside other microeconomic determinants of R&D investment. Spatial proximity is defined by a measure of firms’ industry distance based on trade intensity between sectors. The spatial model specified here refers to the combined spatial autoregressive model with autoregressive disturbances (SARAR). In modelling the outcome for each location as dependent on a weighted average of the outcomes of other locations, outcomes are determined simultaneously. The results of the spatial two stage least square estimation suggest that in their R&D decision firms benefit from spillovers originating from neighbouring industries.”