A Semantic Web-Based Digital Library Infrastructure to Facilitate Computational Epidemiology

Abstract

Computational epidemiology generates and utilizes massive amounts of data. There are two primary categories of datasets: reported and synthetic. Reported data include epidemic data published by organizations (e.g., WHO, CDC, other national ministries and departments of health) during and following actual outbreaks, while synthetic datasets are comprised of spatially explicit synthetic populations, labeled social contact networks, multi-cell statistical experiments, and output data generated from the execution of computer simulation experiments. The discipline of computational epidemiology encounters numerous challenges because of the size, volume, and dynamic nature of both types of these datasets. In this dissertation, we present semantic web-based schemas to organize diverse reported and synthetic computational epidemiology datasets. There are three layers of these schemas: conceptual, logical, and physical. The conceptual layer provides data abstraction by exposing common entities and properties to the end user. The logical layer captures data fragmentation and linking aspects of the datasets. The physical layer covers storage aspects of the datasets. We can create mapping files from the schemas. The schemas are flexible and can grow. The schemas presented include data linking approaches that can connect large-scale and widely varying epidemic datasets. This linked data leads to an integrated knowledge-base, enabling an epidemiologist to ask complex queries that employ multiple datasets. We demonstrate the utility of our knowledge-base by developing a query bank, which represents typical analyses carried out by an epidemiologist during the course of planning for or responding to an epidemic. By running queries with different data mapping techniques, we demonstrate the performance of various tools. The empirical results show that leveraging semantic web technology is an effective strategy for: reasoning over multiple datasets simultaneously, developing network queries pertinent in an epidemic analysis, and conducting realistic studies undertaken in an epidemic investigation. The performance of queries varies according to the choice of hardware, underlying database, and resource description framework (RDF) engine. We provide application programming interfaces (APIs) on top of our linked datasets, which an epidemiologist can use for information retrieval, without knowing much about underlying datasets. The proposed semantic web-based digital library infrastructure can be highly beneficial for epidemiologists as they work to comprehend disease propagation for timely outbreak detection and efficient disease control activities.