This study, conducted by Guru Rao et al., investigates how a three-layered semantic ontology framework can enhance public health intelligence by integrating diverse data sources. The researchers constructed reference, domain, and application ontologies and mapped them to structured and unstructured datasets. This includes WHO Covid-19 case data, travel records, and event reports. By using these ontologies, the researchers constructed a unified knowledge graph and applied semantic enrichment and SPARQL querying to link mass gatherings, air travel, and disease spread. Their results show that ontology-based integration not only harmonizes heterogeneous datasets but also enables inference of contextual relationships, such as identifying when infected individuals also traveled or attended large events. This overall highlights connections that are otherwise inaccessible through traditional data systems. They found that their framework demonstrated its utility through a case study tracing Covid-19 transmission linked to a major football match, where the data revealed patterns involving flight routes, event attendance, and case timelines. Overall, the researchers found that a layered ontology approach improves modularity, reuse, and analytical depth. Their methodology demonstrated the most success in noticing patterns through transmission; however, it still requires careful alignment and needs human intervention to maintain.