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- Title
Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.
- Authors
Fujimoto, Kayo; Kuo, Jacky; Stott, Guppy; Lewis, Ryan; Chan, Hei Kit; Lyu, Leke; Veytsel, Gabriella; Carr, Michelle; Broussard, Tristan; Short, Kirstin; Brown, Pamela; Sealy, Roger; Brown, Armand; Bahl, Justin
- Abstract
This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.
- Subjects
HOUSTON (Tex.); COVID-19 pandemic; SARS-CoV-2 Delta variant; SOCIAL networks; SOCIAL interaction; GOODNESS-of-fit tests
- Publication
Scientific Reports, 2023, Vol 13, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-023-49109-x