In the context of contact tracing for COVID-19, graph visualization offers several advantages, including facilitating awareness of potential transmission occurrences without requiring data queries across multiple sources, adding date-based information to transmission chains to facilitate clinical decision-making, and enabling resource allocation based on chain statistics. The use of Neo4j, a graph database technology, allows for effective data modeling, access control, and browser interface development, making it an ideal solution for supporting healthcare professionals in tracing contacts and reducing transmission. A case study from the Geneva canton, Switzerland, demonstrates the benefits of graph visualization in tracing contact, highlighting the importance of accurate information on proximity in time and space to inform public health strategies.