4.5 Article

Social mixing and network characteristics of COVID-19 patients before and after widespread interventions: A population-based study

Journal

EPIDEMIOLOGY AND INFECTION
Volume 151, Issue -, Pages -

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0950268823001292

Keywords

COVID-19; network; temporal; widespread intervention; agent-based simulation

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SARS-CoV-2 spreads quickly among humans through social networks, and social mixing and network characteristics may facilitate transmission. Limited data on network structural features has hindered in-depth studies, but comparing network characteristics over time can provide additional insights into transmission dynamics.
SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topo-logical structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network charac-teristics over time offers additional insights into transmission dynamics. We examined con-firmed 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 % (p < 0.001); the average shortest path length decreased from 1.53 to 1.14 (p < 0.001); the average betweenness reduced from 0.65 to 0.11 (p < 0.001); the average cluster size dropped from 4.05 to 2.72 (p = 0.004); and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 (p = 0.099). 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 differ-ent pandemic stages, revealing transmission network heterogeneities.

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