A Bayesian spatio-temporal framework to identify outbreaks and examine environmental and social risk factors for infectious diseases monitored by routine surveillance
Spatio-temporal disease patterns can provide clues to etiological pathways, but can be complex to model. Using a flexible Bayesian hierarchical framework, we identify previously undetected space-time clusters and environmental and socio-demographic risk factors for reported giardiasis and cryptosporidiosis at the New Zealand small area level. For giardiasis, there was no seasonal pattern in outbreak probability and an inverse association with density of dairy cattle (β^₁= -0.09, Incidence Risk...[Show more]
|Collections||ANU Research Publications|
|Source:||Spatial and Spatio-temporal Epidemiology|
|Access Rights:||Open Access|
|1-s2.0-S1877584516300715-main.pdf||2.59 MB||Adobe PDF|
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