Covid-19 Knowledge Graph Generation and Enabling Analysis across Healthcare, Socioeconomic, and Environmental Dimensions

There are various significant factors that influence Covid-19 pandemic, such as health, socioeconomic, and environmental aspects. Researchers and administrators require better data on measures such as confirmed cases and deaths due to Covid-19 to efficiently withhold its spread. While existing Covid-19 data resources often focus on spatio-temporal aspects, most research neglects the combined impact of multiple factors. This study addresses this gap by modeling and analyzing Covid-19 epidemiological data across dimensions like location, comorbidity, and occupation. We use data warehousing (DW) technology to model and integrate data from diverse sources in the multidimensional (MD) format. Then, we enable Online Analytical Processing (OLAP)-like analysis on DWs to derive Covid-19 related insights. Besides, to make the data interoperable, and accessible, data need to be annotated, integrated, and published semantically in accordance to FAIR principles. To accomplish this, the data is modeled as a knowledge graph (KG), where the semantic relationships among data are captured in the integration process. The KG is implemented using the Resource Description Framework (RDF) model, and semantically linked with external datasets. The generated knowledge graph is analyzed through an interactive analytical interface to observe the Covid-19 confirmed cases and deaths from thirteen aspects. The performance and quality of the knowledge graph is assessed against prominent data stores modeling Covid-19 data.