In this project, we developed a model of Ebola spread by using innovative big data analytics techniques and tools. We used massive amounts of data from various sources including Twitter feeds, Facebook and Google. This data is then fed into a decision support system that models the spread pattern of the Ebola virus and creates dynamic graphs and predictive diffusion models on the outcome and impact on either a specific person or a specific community. As a result of this research, computational spread models for Ebola in the U.S. are created, potentially leading to more precise forward predictions of the disease propagation and tools to help identify individuals who are possibly infected, and perform trace-back analysis to locate the possible source of infection for a particular social group. Besides collaborating with FIU and other partner universities, we also closely collaborated with LexisNexis (LN), which is a leading big data company and a member of our I/UCRC for Advanced Knowledge Enablement. LexisNexis has provided the large amount of data about relationship of the people in US, and we combined it with data analytics techniques and tools to model disease spread patterns. In this part of research we used Cloud Computer system located in our College at FAU as well as LN’s High-Performance Computer Cluster (HPCC), which is intended for big data applications. We performed modeling, analytics, and development of a Decision Support System (DSS), which provides a probabilistic outcome of Ebola impact on either a specific person or a community at a specific location. Jointly with the LN research team, we created people clusters based on proximity and built a model using weighted scores, which approximate physical contacts. In creating people clusters, we used public record graph to calculate distances between an affected person and his/her relatives and friends. Base on this model, we developed disease propagation path.