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He Y, Martinez L, Ge Y, Feng Y, Chen Y, Tan J, Westbrook A, Li C, Cheng W, Ling F, Cheng H, Wu S, Zhong W, Handel A, Huang H, Sun J, Shen Y. Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study. Epidemiol Infect 2023; 151:1-38. [PMID: 37577939 PMCID: PMC10540215 DOI: 10.1017/s0950268823001292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/28/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.
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Affiliation(s)
- Yuncong He
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, USA
| | - Yang Ge
- School of Health Professions, University of Southern Mississippi, Hattiesburg, USA
| | - Yan Feng
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Yewen Chen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Jianbin Tan
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Adrianna Westbrook
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, USA
| | - Wei Cheng
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Feng Ling
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Huimin Cheng
- Department of Statistics, University of Georgia, Athens, USA
| | - Shushan Wu
- Department of Statistics, University of Georgia, Athens, USA
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, Athens, USA
| | - Andreas Handel
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
| | - Hui Huang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
| | - Jimin Sun
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA
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