Empirical Bayesian Estimation of the Disease Transmission Probability in Multiple-vector-transfer Designs

• Activity Number: 200
• Type: Contributed
• Date/Time: Tuesday, August 10, 2004 : 9:00 AM to 10:50 AM
• Sponsor: Biometrics Section
• Author(s): Christopher R. Bilder*+ and Joshua M. Tebbs
• Companies: University of Nebraska, Lincoln and Kansas State University
• Keywords: group testing ; insect vectors ; plant pathology ; plant disease ; proportion ; virus transmission

Abstract

Plant disease is responsible for major losses in agriculture throughout the world. Diseases are often spread by insect organisms that transmit a bacterium, virus, or other pathogen. To assess disease epidemics, plant pathologists often use multiple-vector-transfers. In such contexts, s>1 insect vectors are moved from an infected source to each of n test plants. The purpose here is to present new estimators for p, the probability of pathogen transmission for an individual vector, motivated from an empirical Bayesian approach. In studying point estimate properties, one of our proposed estimators consistently results in a smaller bias and mean squared error than the maximum likelihood estimator (MLE) as proposed by Thompson and Swallow. This bias reduction is frequently fivefold or more in optimal settings for the MLE. Furthermore, these estimators are easier to compute than the classical Bayes estimators proposed by Chaubey and Li, and Chick. Finally our newly proposed empirical credible intervals possess the desirable property that lower bound will never be negative.