Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study.

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.