Modeling A Personal Exposure History through Event-Event Relationships

As disease surveillance systems improve in both standardization of reporting methods and data collection, better modeling and analysis methods are needed for use in spatial epidemiological studies. Recently new systems have been developed for the analysis of disease; however, many have difficulty clearly representing the complex concept of personal exposure histories. Exposure histories must not only capture the spatial and temporal dimensions of possible disease exposure events but also must convey the dynamic factors within the individual\u27s environment. This paper presents an ontology driven approach to represent data from heterogeneous sources to provide the foundation for improving environmental health monitoring systems to assess risk of longer latency disease based on the concept of a personal exposure history. The ontology presented is transformed into Resource Description Framework (RDF) to enhance the ability to explicitly query on event-event relationships. Special consideration is given to the efficient integration of large volumes of data available from the expanding deployment of environmental monitoring sensor networks