ArticleBMC medical research methodology2020
Social network analysis methods for exploring SARS-CoV-2 contact tracing data.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 55 citations in OpenAlex.
- Non-Markovian dynamics and effective reproduction number in COVID-19: Evidence from Cyprus contact tracing data.PLoS computational biology · 2026Article
- A network analysis of carbapenem-resistant Klebsiella pneumoniae among healthcare facilities.Scientific reports · 2025Observational
- A novel voting measure for identifying influential nodes in complex networks based on local structure.Scientific reports · 2025Article
- Bridging the Gap: Integrating Knowledge from the Study of Social Network Analysis and Infectious Disease Dynamics in Human and Nonhuman Primates.Annual review of anthropology · 2024Article
- Nasopharyngeal angiotensin converting enzyme 2 (ACE2) expression as a risk-factor for SARS-CoV-2 transmission in concurrent hospital associated outbreaks.BMC infectious diseases · 2024Article
- The Role of Location in the Spread of SARS-CoV-2: Examination of Cases and Exposed Contacts in South Texas, Using Social Network Analysis.Disaster medicine and public health preparedness · 2023Article
- Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.International journal of environmental research and public health · 2023Article
- A Strategy Formulation Framework for Efficient Screening during the Early Stage of a Pandemic.Tropical medicine and infectious disease · 2023Article
- A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.Health services insights · 2023Article
- Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study.JMIR infodemiology · 2023Article
- Digital Health Care Industry Ecosystem: Network Analysis.Journal of medical Internet research · 2022Article
- Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong.International journal of environmental research and public health · 2022Article
- Empirical Analysis of the Dynamics of the COVID-19 Epidemic in Urban Embedded Social Networks.Frontiers in public health · 2022Article
- Why Re-Invent the Wheel? Social Network Approaches Can Be Used to Mitigate SARS-CoV-2 Related Disparities in Latinx Seasonal Farmworkers.International journal of environmental research and public health · 2021Article
- 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
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundContact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels.
methodsStandard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as "nodes" and their epidemiological contact as "edges" was created. Directed network perspective was utilized in which directionality of infection emanated from a "source patient" towards a "target patient". Network measures of " degree centrality" and "betweenness centrality" were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers.
resultsOut-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient's which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network.
conclusionsSocial network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.
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