ArticleEpidemiology and infection2020
Social network analysis of COVID-19 transmission in Karnataka, India.
Article in Epidemiology and infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- COVID-19 transmission dynamics: age-specific patterns and super spreaders in South Korea.BMC public health · 2026Article
- Network epidemiological analysis of COVID-19 transmission patterns by age, occupation and residence across four waves in Cyprus.Scientific reports · 2025Article
- The impact of the COVID-19 pandemic on governmental hospitals performance indicators in city of Yazd, Iran: an interrupted time-series analysis.BMC health services research · 2025Article
- Understanding the progress of COVID-19 transmission in a rural district: a social network approach.PeerJ · 2024Article
- COVID-19 Mortality in the Delta Wave in India: A Hospital-Based Study From Ramanagara District, Karnataka.Cureus · 2023Article
- A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media.Sensors (Basel, Switzerland) · 2023Review
- Assessing efficacy of instructor based orientation to donning doffing protocols and modifications to doffing area infrastructure in reducing SARS-CoV-2 infection among Doctors assigned to COVID-19 patient care.Infection prevention in practice · 2023Article
- An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects.Applied soft computing · 2023Article
- A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.Health services insights · 2023Article
- Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making.Informatics in medicine unlocked · 2022Article
- Empirical Analysis of the Dynamics of the COVID-19 Epidemic in Urban Embedded Social Networks.Frontiers in public health · 2022Article
- Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction.Informatics in medicine unlocked · 2022Article
- Mining relationships between transmission clusters from contact tracing data: An application for investigating COVID-19 outbreak.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The role of age in the spreading of COVID-19 across a social network in Bucharest.Journal of complex networks · 2021Article
- COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021Article
- Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China.International journal of environmental research and public health · 2021Article
- Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?International journal of environmental research and public health · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links 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 mean 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 ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.
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