Beyond Scale-Free Networks: integrating Multilayer Social Networks With Molecular Clusters in the Local Spread of COVID-19

Authors

Kayo Fujimoto
Jacky Kuo
Guppy Stott
Ryan Lewis
Hei Kit Chan
Leke Lyu
Gabriella Veytsel
Michelle Carr
Tristan Broussard
Kirstin Short
Pamela Brown
Roger Sealy
Armand Brown
Justin Bahl

Language

English

Publication Date

12-9-2023

Journal

Scientific Reports

DOI

10.1038/s41598-023-49109-x

PMID

38071385

PMCID

PMC10710469

PubMedCentral® Posted Date

December 2023

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, COVID-19, SARS-CoV-2, Social Networking, Texas

Recommended Citation

Fujimoto, Kayo; Kuo, Jacky; Stott, Guppy; et al., "Beyond Scale-Free Networks: integrating Multilayer Social Networks With Molecular Clusters in the Local Spread of COVID-19" (2023). Faculty, Staff and Student Publications. 264.
https://digitalcommons.library.tmc.edu/uthsph_docs/264