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The humanity has been facing a plethora of challenges associated withinfectious diseases, which kill more than 6 million people a year. Althoughcontinuous efforts have been applied to relieve the potential damages from suchmisfortunate events, it is unquestionable that there are many persistingchallenges yet to overcome. One related issue we particularly address here isthe assessment and prediction of such epidemics. In this field of study,traditional and ad-hoc models frequently fail to provide proper predictivesituation awareness (PSAW), characterized by understanding the currentsituations and predicting the future situations. Comprehensive PSAW forinfectious disease can support decision making and help to hinder diseasespread. In this paper, we develop a computing system platform focusing oncollective intelligence causal modeling, in order to support PSAW in the domainof infectious disease. Analyses of global epidemics require integration ofmultiple different data and models, which can be originated from multipleindependent researchers. These models should be integrated to accurately assessand predict the infectious disease in terms of holistic view. The system shallprovide three main functions: (1) collaborative causal modeling, (2) causalmodel integration, and (3) causal model reasoning. These functions aresupported by subject-matter expert and artificial intelligence (AI), withuncertainty treatment. Subject-matter experts, as collective intelligence,develop causal models and integrate them as one joint causal model. Theintegrated causal model shall be used to reason about: (1) the past, regardinghow the causal factors have occurred; (2) the present, regarding how the spreadis going now; and (3) the future, regarding how it will proceed. Finally, weintroduce one use case of predictive situation awareness for the Ebola virusdisease.

Predictive Situation Awareness for Ebola Virus Disease using a Collective Intelligence Multi-Model Integration Platform: Bayes Cloud