We study the transition of an epidemic from growth phase to decay of the active infections in a population when lockdown health measures are introduced to reduce the probability of disease transmission . While in the case of uniform lockdown a simple compartmental model would indicate instantaneous transition to decay of the epidemic, this is not the case when partially isolated active clusters remain with the potential to create a series of small outbreaks . We model this using the Gillespie stochastic simulation algorithm based on a connected set of stochastic susceptible-infected-removed/recovered (SIR) networks representing the locked-down majority population (where the reproduction number is less than one) weakly coupled to a large set of small clusters where the infection may propagate . We find that the presence of such active clusters can lead to slower than expected decay of the epidemic and significantly delayed onset of the decay phase . We study the relative contributions of these changes, caused by the active clusters within the locked-down population, to the additional total infected population . We also demonstrate that limiting the size of the inevitable active clusters can be efficient in reducing their impact on the overall size of the epidemic outbreak . The deceleration of the decay phase becomes apparent when the active clusters form at least 5% of the population.