Abstract The emergence of instabilities and steady-state pattern formation in epidemic spread are studied using a stochastic Partial Differential Equation compartmental epidemic model. Strongly nonlinear infection transmission forces and random environmental effects are taken into account in the study. Environmental uncertainties are modeled using the Ornstein-Uhlenbeck process and instabilities are studied by computing the maximal Lyapunov exponent obtained using higher-order perturbation analysis. Steady-state pattern formation is studied using stationary solutions obtained by numerically solving the PDE model equations. A range of values of the diffusion coefficient and correlation time in parameter space that support the onset of instabilities are obtained. It is shown that the stability and pattern formation results depend critically on the correlation time of the Ornstein-Uhlenbeck stochastic process; specifically, lower values of steady-state infection density are obtained for higher correlation times. For an Ornstein-Uhlenbeck process with a lower correlation time, the results are shown to approach those obtained for the case of white noise. The correlation time of the Ornstein-Uhlenbeck plays a significant role in the onset of Turing-type and noise-induced instabilities, as well as self-organized pattern formation in a stochastic epidemic model with strongly nonlinear infection forces.