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Social network analysis methods for exploring SARS-CoV-2 contact tracing data.

Karikalan Nagarajan1, Malaisamy Muniyandi2, Bharathidasan Palani2

  • 1Department of Health Economics, Indian Council of Medical Research- National Institute for Research in Tuberculosis, Chennai, 600031, India. karikalan.n@nirt.res.in.

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Summary

Social network analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contact tracing data identified key individuals and groups driving transmission. These findings can enhance public health interventions and improve disease control strategies.

Keywords:
Betweenness centralityComponentsContact tracingDegree centralityHeterogeneityIndiaInfectious diseasesPatientsSARS-CoV-2Social networks

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Area of Science:

  • Epidemiology
  • Public Health
  • Network Science

Background:

  • Contact tracing data from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is crucial for estimating epidemiological parameters.
  • This data can reveal transmission heterogeneity at the individual patient level.
  • Characterizing infectiousness variations informs targeted contact tracing interventions.

Purpose of the Study:

  • To apply social network analysis to SARS-CoV-2 contact tracing data.
  • To identify influential patients and transmission pathways.
  • To assess the utility of network metrics in understanding disease spread.

Main Methods:

  • Utilized standard social network analysis on contact tracing data from 1959 SARS-CoV-2 patients in India.
  • Created a relational network with patients as nodes and contacts as edges.
  • Calculated degree centrality and betweenness centrality to identify key transmitters and used component analysis for subgroup identification.

Main Results:

  • 11.27% of patients acted as sources for 40.19% of infections.
  • A small fraction (0.65%) of highly central patients infected a significant proportion of secondary cases.
  • Betweenness centrality identified 7.50% of patients bridging transmission, and network components accounted for substantial patient and contact numbers.

Conclusions:

  • Social network analysis effectively measures individual variations in SARS-CoV-2 transmission.
  • Identified specific patients and components disproportionately contributing to disease spread.
  • Network metrics and visualizations can augment current contact tracing, improving its efficacy.