Dimitriou Pavlos Alexandros, Silvestros Valentinos, Constantinou Elisavet, Pitris Costas, Kolios Panayiotis
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
KIOS Research and Innovation Center of Excellence, Nicosia, Cyprus.
Sci Rep. 2025 Aug 4;15(1):28416. doi: 10.1038/s41598-025-14267-7.
Complex transmission patterns are not immediately obvious to epidemiologists, hindering the development of effective intervention strategies. The aim is to develop network-based tools to identify transmission patterns across age-groups, occupations, and locations. Infection networks were constructed using COVID-19 contact tracing data, provided by the Cyprus Ministry of Health, for March 2020 to May 2021. Transmission patterns within/across age-groups, districts, and economic activities, as well as the presence of super-spreaders and the vulnerability of different groups, were assessed using the constructed networks for the first four pandemic waves. The constructed networks for all waves were sparse. Network analysis, showed that the first wave primarily involved older individuals and healthcare settings. During the second wave, a higher infection rate among young adults was observed. The dominant transmission patterns during the third and fourth wave existed between (i) individuals of similar ages, who more commonly interact, and (ii) individuals with an age difference of ~ 30 years (i.e. a generational gap). Cross-district patterns revealed transmissions were likely to occur between districts that are nearest geographically but also followed social trends. Furthermore, the study identified vulnerable occupations. Outdegree, representing the number of secondary infections caused by an individual, was also investigated. As the pandemic progressed, a decreased outdegree among older age groups (50 +) likely reflected the positive effect of vaccinations and immunity. In contrast, rising values among younger individuals probably reflected fewer vaccinations and more active social interactions. Age, district, and occupation patterns of transmission and super-spreader concentrations can guide targeted intervention strategies, prioritize vaccination efforts, and support decision-making for effective control and prevention of pandemics.
复杂的传播模式对流行病学家来说并非立竿见影，这阻碍了有效干预策略的制定。目的是开发基于网络的工具，以识别不同年龄组、职业和地点之间的传播模式。利用塞浦路斯卫生部提供的2020年3月至2021年5月的新冠病毒接触者追踪数据构建了感染网络。使用构建的网络对前四波疫情期间不同年龄组内/之间、地区和经济活动中的传播模式，以及超级传播者的存在和不同群体的脆弱性进行了评估。所有波次构建的网络都很稀疏。网络分析表明，第一波疫情主要涉及老年人和医疗机构。在第二波疫情期间，观察到年轻人中的感染率较高。第三波和第四波疫情期间的主要传播模式存在于（i）年龄相近、互动更频繁的个体之间，以及（ii）年龄相差约30岁（即代沟）的个体之间。跨地区模式显示，传播可能发生在地理上最接近但也遵循社会趋势的地区之间。此外，该研究还确定了易感染职业。还调查了代表个体引起的二次感染数量的出度。随着疫情的发展，老年组（50岁以上）的出度下降可能反映了疫苗接种和免疫力的积极作用。相比之下，年轻人中出度值的上升可能反映了疫苗接种较少和社交互动更活跃。传播的年龄、地区和职业模式以及超级传播者的聚集情况可以指导有针对性的干预策略，确定疫苗接种工作的优先次序，并支持有效控制和预防疫情的决策。
