A brief introduction is presented to modeling in stochastic epidemiology. Several useful epidemiological concepts such as the basic reproduction number and the nal size of an epidemic are dened. Three well-known stochastic modeling formulations are in- troduced: discrete-time Markov chains, continuous-time Markov chains, and stochastic dierential equations. Methods for derivation, analysis and numerical simulation of the three types of stochastic epidemic models are presented. Emphasis is placed on some of the dierences between the three stochastic modeling formulations as illustrated in the classic SIS (susceptible-infected-susceptible) and SIR (susceptible-infected-recovered) epidemic models. In addition, some of the unique properties of stochastic epidemic models, such as the probability of an outbreak, nal size distribution, critical commu- nity size, and expected duration of an epidemic are demonstrated in various models of diseases impacting humans and wildlife.