Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to echinococcosis in Rio Negro, Argentina.

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.

A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.

Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior distribution with an efficient estimate of the marginal likelihood of the data given this parameter. An extension of the model incorporating covariates is also shown. These covariates may incorporate additional information on the problem or they may account for spatial correlation in the data. We illustrate the performance of the proposed model through both a simulation study and a case study of reported cases of varicella in the city of Valencia, Spain.

Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to visceral leishmaniasis data in Brazil.

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 (∼70%). 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.

Prospective surveillance of multivariate spatial disease data.

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented.