In applying scan statistics for disease surveillance, it would be valuable to have an integrated model that simultaneously includes environmental covariates and spatial correlation. In this paper, a generalized scan statistics under quasi-likelihood functions is proposed to address this issue. We use a two-step estimation process to obtain estimates of coefficients and adapt a bootstrapping method for the minimal p-value to address the multiple-testing problem. Under suitable conditions, the proposed method is consistent and can control the type I error rate. Simulations and applications to real data sets are used to evaluate the method.