Bayesian spatiotemporal analysis of socio-ecologic drivers of Ross River virus transmission in Queensland, Australia.
This study aims to examine the impact of socio-ecologic factors on the transmission of Ross River virus (RRV) infection and to identify areas prone to social and ecologic-driven epidemics in Queensland, Australia. We used a Bayesian spatiotemporal conditional autoregressive model to quantify the relationship between monthly variation of RRV incidence and socio-ecologic factors and to determine spatiotemporal patterns. Our results show that the average increase in monthly RRV incidence was 2.4% (95% credible interval (CrI): 0.1-4.5%) and 2.0% (95% CrI: 1.6-2.3%) for a 1 degrees C increase in monthly average maximum temperature and a 10 mm increase in monthly average rainfall, respectively. A significant spatiotemporal variation and interactive effect between temperature and rainfall on RRV incidence were found. No association between Socio-economic Index for Areas (SEIFA) and RRV was observed. The transmission of RRV in Queensland, Australia appeared to be primarily driven by ecologic variables rather than social factors.
Hu
Wenbiao
W
School of Population Health, The University of Queensland, Queensland, Australia. w.hu@sph.uq.edu.au
Clements
Archie
A
Williams
Gail
G
Tong
Shilu
S
Mengersen
Kerrie
K
D001499:Bayes Theorem; D006801:Humans; D011793:Queensland / Q000453:epidemiology; D012398:Ross River virus / Q000502:physiology*; D014777:Virus Diseases / Q000453:epidemiology / Q000635:transmission*
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