In the U.S., 80% of Medicare spending is for managing patients with multiple coexisting conditions. Predicting potentially correlated diseases for an individual patient and correlated disease progression paths are both important research tasks. For example, obese patients are at an increased risk for developing type-2 diabetes and hypertension. This correlation is called comorbidity relationship. Discovering the comorbidity relationships is complex and difficult due to the limited access to Electronic Health Records by privacy laws. In this paper, we present a framework called Social Data-based Prediction of Incidence and Trajectory to predict potential risks for medical conditions as well as its progression trajectory to identify the comorbidity path. The framework utilizes patients' publicly available social media data and presents a collaborative prediction model to predict the ranked list of potential comorbidity incidences, and a trajectory prediction model to reveal different paths of condition progression. The experimental results show that our framework is able to predict future conditions for online patients with a coverage value of 48% and 75% for a top-20 and a top-100 ranked list, respectively. For risk trajectory prediction, our framework is able to reveal each potential progression trajectory between any two conditions and infer the confidence of the future trajectory, given any observed condition. The predicted trajectories are validated with existing comorbidity relations from the medical literature.