BAYESIAN BETA REGRESSION: APPLICATIONS TO HOUSEHOLD EXPENDITURE DATA AND GENETIC DISTANCE BETWEEN FOOT-AND-MOUTH DISEASE VIRUSES

There is considerable interest in understanding how factors such as time and geographic distance between isolates might influence the evolutionary direction of foot‐and‐mouth disease. Genetic differences between viruses can be measured as the proportion of nucleotides that differ for a given sequence or gene. We present a Bayesian hierarchical regression model for the statistical analysis of continuous data with sample space restricted to the interval (0, 1). The data are modelled using beta distributions with means that depend on covariates through a link function. We discuss methodology for: (i) the incorporation of informative prior information into an analysis; (ii) fitting the model using Markov chain Monte Carlo sampling; (iii) model selection using Bayes factors; and (iv) semiparametric beta regression using penalized splines. The model was applied to two different datasets.     

Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging

Summary.  Using Bayesian model averaging, we quantify associations of governance and economic health with country level presence of foot-and-mouth disease (FMD) and estimate the probability of the presence of FMD in each country from 1997 to 2005. The Bayesian model averaging accounted for countries' previous FMD status and other possible confounders, as well as uncertainty about the 'true' model, and provided accurate predictions (90% specificity and 80% sensitivity). This model represents a novel approach to predicting FMD, and other conditions, on a global scale and in identifying important risk factors that can be applied to global policy and allocation of resources for disease control.