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Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability
Author(s) -
Cui Na,
Chen Yuguo,
Small Dylan S.
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12050
Subject(s) - imperfect , markov chain monte carlo , weibull distribution , bayesian probability , markov chain , computer science , statistics , infection rate , econometrics , biology , mathematics , medicine , artificial intelligence , surgery , philosophy , linguistics
Summary 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.