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.