Estimation of infection and recovery rates for highly polymorphic parasites when detectability is imperfect, using hidden Markov models.
A Bayesian hierarchical model is proposed for estimating parasitic infection dynamics for highly polymorphic parasites when detectability of the parasite using standard tests is imperfect. The parasite dynamics are modelled as a non-homogeneous hidden two-state Markov process, where the observed process is the detection or failure to detect a parasitic genotype. This is assumed to be conditionally independent given the hidden process, that is, the underlying true presence of the parasite, which evolves according to a first-order Markov chain. The model allows the transition probabilities of the hidden states as well as the detectability parameter of the test to depend on a number of covariates. Full Bayesian inference is implemented using Markov chain Monte Carlo simulation. The model is applied to a panel data set of malaria genotype data from a randomized controlled trial of bed nets in Tanzanian children aged 6-30 months, with the age of the host and bed net use as covariates. This analysis confirmed that the duration of infections with parasites belonging to the MSP-2 FC27 allelic family increased with age.
Swiss Tropical Institute, Socinstrasse 57, Postfach CH-4002, Basel, Switzerland. Thomas-A.Smith@unibas.ch
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D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001512:Bedding and Linens; D002675:Child, Preschool; D005838:Genotype; D006801:Humans; D007223:Infant; D016778:Malaria, Falciparum / Q000235:genetics* / Q000517:prevention & control / Q000635:transmission; D008390:Markov Chains; D009010:Monte Carlo Method; D010963:Plasmodium falciparum / Q000235:genetics*; D016133:Polymerase Chain Reaction; D012150:Polymorphism, Restriction Fragment Length; D016032:Randomized Controlled Trials as Topic; D013636:Tanzania / Q000453:epidemiology