title: Modeling parasite infection dynamics when there is heterogeneity and imperfect detectability.
abstract: Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection status is only observed at discrete times even though infection and recovery take place in continuous time and (2) 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 the parasite Giardia lamblia among children in Kenya.
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, U.S.A.
authors.forename: Na
authors.initials: N
authors.lastname: Cui
authors.forename: Yuguo
authors.initials: Y
authors.lastname: Chen
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D002648:Child; D016829:Giardia lamblia; D005873:Giardiasis / Q000453:epidemiology / Q000469:parasitology; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D010272:Parasitic Diseases / Q000453:epidemiology / Q000469:parasitology; D012008:Recurrence
keywords: Bayesian Hierarchical Model; Infection Rate; Markov Chain Monte Carlo; Panel Data; Recovery Rate; Two-state Process