Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.
Sci Rep
; 13(1): 21861, 2023 12 09.
Article
en En
| MEDLINE
| ID: mdl-38071385
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
COVID-19
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Sci Rep
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos