ABSTRACT
The analysis of zoonotic disease risk requires the 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.
Subject(s)
Dog Diseases/epidemiology , Echinococcosis/epidemiology , Public Health Surveillance/methods , Zoonoses/epidemiology , Adolescent , Animals , Argentina/epidemiology , Bayes Theorem , Child , Dogs , Echinococcus granulosus , Humans , Models, BiologicalABSTRACT
Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and leads to high fatality rates when left untreated. We investigate the relationship of VL cases in dogs and human cases, specifically for evidence of VL in dogs leading to excess cases in humans. We use surveillance data for dogs and humans for the years 2007-2011 to conduct both spatial and spatio-temporal analyses. Several models are evaluated incorporating varying levels of dependency between dog and human data. Models including dog data show marginal improvement over models without; however, for a subset of spatial units with ample data, models provide concordant risk classification for dogs and humans at high rates (â¼70%). Limited reported dog case surveillance data may contribute to the results suggesting little explanatory value in the dog data, as excess human risk was only explained by dog risk in 5% of regions in the spatial analysis.