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Fujimoto K, Kuo J, Stott G, Lewis R, Chan HK, Lyu L, Veytsel G, Carr M, Broussard T, Short K, Brown P, Sealy R, Brown A, Bahl J. Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19. Sci Rep 2023; 13:21861. [PMID: 38071385 PMCID: PMC10710469 DOI: 10.1038/s41598-023-49109-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
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.
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Affiliation(s)
- Kayo Fujimoto
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA.
| | - Jacky Kuo
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Guppy Stott
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - Ryan Lewis
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Hei Kit Chan
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Leke Lyu
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - Gabriella Veytsel
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | | | | | | | - Pamela Brown
- City of Houston Health Department, Houston, TX, USA
| | - Roger Sealy
- City of Houston Health Department, Houston, TX, USA
| | - Armand Brown
- City of Houston Health Department, Houston, TX, USA
| | - Justin Bahl
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA.
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Zhu Y, Shen R, Dong H, Wang W. Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model. PLoS One 2023; 18:e0286558. [PMID: 37310972 DOI: 10.1371/journal.pone.0286558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/18/2023] [Indexed: 06/15/2023] Open
Abstract
Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. By considering individual random movement and disease spread, our simulations using the CDD-SEIR model reveal that the distribution of agent types in the community exhibits spatial heterogeneity. The estimated basic reproduction number R0 depends on group mobility, increasing logarithmically in strongly heterogeneous cases and saturating in weakly heterogeneous conditions. Notably, R0 is approximately independent of virus virulence when group mobility is low. We also show that transmission through small amounts of long-term contact is possible due to short-term contact patterns. The dependence of R0 on environment and individual movement patterns implies that reduced contact time and vaccination policies can significantly reduce the virus transmission capacity in situations where the virus is highly transmissible (i.e., R0 is relatively large). This work provides new insights into how individual movement patterns affect virus spreading and how to protect people more efficiently.
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Affiliation(s)
- Youyuan Zhu
- Kuang Yaming Honors School, Nanjing University, Nanjing, China
- Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructure, Nanjing, China
- Department of Physics, Nanjing University, Nanjing, China
| | - Ruizhe Shen
- Kuang Yaming Honors School, Nanjing University, Nanjing, China
- Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructure, Nanjing, China
- Department of Physics, Nanjing University, Nanjing, China
| | - Hao Dong
- Kuang Yaming Honors School, Nanjing University, Nanjing, China
- Department of Physics, Nanjing University, Nanjing, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Wei Wang
- Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructure, Nanjing, China
- Department of Physics, Nanjing University, Nanjing, China
- Institute for Brain Sciences, Nanjing University, Nanjing, China
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