We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and assess the potential of SNA as a tool for outbreak monitoring and control. We analyzed contact tracing data of 1147 Covid-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymized and made public by the government. We used software tools Cytoscape and Gephi to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links, determined by contact tracing, from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) super-spreaders (outdegree[â¥]5) caused 60% of the transmissions. Real-time social network visualization can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritizing these areas and individuals for rigorous containment could help minimize resource outlay and potentially achieve a significant reduction in COVID-19 transmission.
Social Network Analysis of COVID-19 Transmission in Karnataka, India