Modeling Parasite Infection Dynamics When There Is Heterogeneity and Imperfect Detectability

• Activity Number: 409
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Na Cui*+ and Dylan Small and Yuguo Chen
• Companies: University of Illinois at Urbana-Champaign and University of Pennsylvania and University of Illinois at Urbana-Champaign
• Keywords: Panel data ; Infection rate ; Recovery rate ; Markov chain Monte Carlo ; Bayesian hierarchical model

Abstract

Infection with the parasite Giardia lamblia is a problem among children in Kenya. Understanding the infection and recovery rate is valuable for public health planning. Two challenges in modeling these rates are that infection status is only observed at discrete times even though infection and recovery take place in continuous time and detectability of infection is imperfect. We address these issues through a Bayesian hierarchical model based on a random effects Weibull distribution. The model incorporates heterogeneity of the infection and recovery rate among individuals and allows for imperfect detectability. We estimate the model by a Markov chain Monte Carlo algorithm with data augmentation. We present simulation studies and an application to an infection study about Giardia lamblia.