This paper presents a recurrent epidemic model (REM) to explore the dynamics of Internet epidemiology through the phases of susceptibility to recovery. From both theoretical and practical standpoint, it has two main differences compared to the bare worm propagation modeling. In the first place, it defines a unique stochastic model of a general infection spread. In the second place, it models the recovery process as a stochastic queueing system, which accurately partitions diagnose, quarantine, disinfection and recovery processes and complements it as a recurrent failure-repair management model, which is entirely unique. There still exists an open question to model propagation patterns of infections and accompanying recovery models needed for effectively managing the infected individuals. The REM model is a unique concept in determining the parameters for estimating the recovery efficiency of disrupted systems and for developing long-term recovery strategies under different epidemic situations. Existing infection and worm propagation models can also be used in cooperation with REM in order to analyse necessary quarantine and recovery processes. REM can also be applied for the accurate classification of the phases in epidemic dynamics and the states of affected systems in general, and also be used as a guideline for developing stochastic simulations covering various types of systems with recurrent state dynamics in order to facilitate reliability analysis of the systems.