Using Social Network Analysis during COVID-19 contact tracing in Libya, 2020

Abstract

Abstract Background Social network analysis (SNA) may provide valuable insights into individual-level transmission dynamics, allowing for targeted contact tracing interventions. We used SNA to understand the variability in SARS-CoV-2 transmission at the case level during the first wave in Libya. Methods We analyzed the spread of COVID-19 in Libya from March to August 2020 using contact tracing data from 287 confirmed cases. We created a network to visualize the transmission patterns, representing each case as a node and connections as links. We focused on direct contacts identified through contact tracing and calculated network measures such as out-degree centrality, in-degree centrality, and betweenness centrality. Gephi software was used for data visualization and understanding the network structure. Results Of the 287 COVID-19 cases, 264 (92%) had nodes with zero outdegrees, indicating that they did not contribute to infection transmission. The remaining 23 (8%) had non-zero out-degree measures and transmitter infection to other cases, 10 (4%) of whom were super-spreaders (outdegree≥5) causing 70% of the transmission. Six (2%) had no epidemiological contact, indicated by zero in-degree centrality measures; 63% had zero betweenness centrality measures, suggesting they did not act as intermediaries connecting separate transmission chains within the network. The network contained nine clusters of connected nodes, with the largest having 91 (24%) cases; the mean path length between cases was 1.846, and the network diameter was 4. Conclusions The study revealed distinct clusters of connected cases, along with the presence of super-spreaders. This network structure underscores the critical role of early identification and isolation of individuals with high transmission potential to effectively contain outbreaks. This revised conclusion directly references the findings of nine clusters and super-spreaders, demonstrating how these results support the importance of early intervention. Key messages • Social network analysis identifies clusters and super-spreaders in COVID-19 transmission, emphasizing the need for targeted interventions. • Connected clusters and super-spreaders highlight the importance of contact tracing and early isolation for effective control of COVID-19 outbreaks.