He Yuncong, Martinez Leonardo, Ge Yang, Feng Yan, Chen Yewen, Tan Jianbin, Westbrook Adrianna, Li Changwei, Cheng Wei, Ling Feng, Cheng Huimin, Wu Shushan, Zhong Wenxuan, Handel Andreas, Huang Hui, Sun Jimin, Shen Ye
Epidemiol Infect. 2023 Aug 14;151:1-38. doi: 10.1017/S0950268823001292.
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 [Image: see text] to 62.8 [Image: see text] [Image: see text] ; the average shortest path length decreased from 1.53 to 1.14 [Image: see text] ; the average betweenness reduced from 0.65 to 0.11 [Image: see text] ; the average cluster size dropped from 4.05 to 2.72 [Image: see text] ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 [Image: see text] 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.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）通过社交网络在人类中迅速传播，社交混合和网络特征可能会促进传播。然而，关于拓扑结构特征的数据有限，阻碍了深入研究。现有研究基于快照分析，无法对网络变化进行时间维度的调查。比较不同时间的网络特征有助于深入了解传播动态。我们研究了中国东部某省的新冠肺炎确诊患者，利用广泛干预前后的传播网络拓扑结构分析社交混合和网络特征。在两个时间段之间，单例网络的百分比从38.9[图：见正文]增加到62.8[图：见正文][图：见正文]；平均最短路径长度从1.53降至1.14[图：见正文]；平均介数从0.65降至0.11[图：见正文]；平均聚类系数从4.05降至2.72[图：见正文]；出度从0.75略有下降但不显著至0.63[图：见正文]。结果表明，非药物干预有效地破坏了传播网络，防止了疾病的进一步传播。此外，我们发现，在应用描述性和基于主体的建模方法后，网络的动态结构比单纯检查感染曲线提供了更多信息。总之，我们研究了不同疫情阶段新冠肺炎患者的社交混合和网络特征，揭示了传播网络的异质性。
