  In this paper, the recurrent events that can occur more than one over the
follow-up time have been modeled by phase-type distributions. We use the
finite-state continuous-time Markov process with multi states for patients with
recurrent events. The number of recurrences until time $t$, the time stay for
every state and the time till death are of importances. The time till death is
assumed to have a phase-type distribution (which is defined in a Markov chain
environment) with interpretable parameters. The underlying continuous-time
Markov chain has one absorbing state (death) and transient states to reflect
recovery and disease stages. A system of differential equations is obtained to
calculate the probability of various number of transitions, the conditional
expected time to stay in a disease stage and the probability of transition from
a stage to another. The model has been calibrated via a real and simulated
datasets. The bootstrap techniques have been used to construct the confidence
intervals for the parameters.
