The extent to which Hawkes point process models can more accurately characterize the evolution of a disease epidemic than a standard compartmental model such as SEIR is investigated. Maximum likelihood estimation was used to fit SEIR model parameters to Ebola outbreak data in West Africa in 2014 from the World Health Organization (WHO). Projections using simulation were then conducted using the Poisson-leaping Tau Method (Cao et al. 2007) to evaluate the fit. The projections and rate function were compared to Hawkes point process estimation and simulation over the same data and projection scale. Results indicate that Hawkes models outperformed SEIR in predicting the spread of Ebola in West Africa with a 38% reduction in RMSE for weekly case estimation across all countries (total RMSE of 59.6 cases/week using SEIR compared to 37.2 for Hawkes). An analysis using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation shows that the improved fit from Hawkes modeling cannot be attributed to overfitting.