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The analysis of zoonotic disease risk requires tshe consideration of both human and animal geo-referenced disease incidence data. Here we show an application of joint Bayesian analyses to the study of echinococcosis granulosus (EG) in the province of Rio Negro, Argentina. We focus on merging passive and active surveillance data sources of animal and human EG cases using joint Bayesian spatial and spatio-temporal models. While similar spatial clustering and temporal trending was apparent, there appears to be limited lagged dependence between animal and human outcomes. Beyond the data quality issues relating to missingness at different times, we were able to identify relations between dog and human data and the highest 'at risk' areas for echinococcosis within the province. Author summary: This work focuses on utilizing animal disease data to try and inform our understanding of the spread of diseases in humans. We implement predictive models to estimate the relationship between the distribution of disease in animal populations and the distribution of disease in human populations. Development of a better understanding of this relationship could inform animal and public health interventions aiming to mitigate against human disease before it spreads. Missing data and limited data resources made discovery of these relationships difficult, but we fit multiple model types to try and identify any connection between these two populations. We found specific areas with elevated risk of human disease and changes in disease risk over time. Finally, there was some indication of an association between previous years' levels of animal disease and human disease when using animals as covariables. [ABSTRACT FROM AUTHOR]