摘要
2019年新型冠状病毒肺炎(COVID-19)大流行是一场严重的全球公共卫生突发事件。隔离病例、切断传播途径已经成为目前控制疫情的主要有效措施。由于溯源工作的复杂性,病例溯源信息统计数据不完整。在本研究中,我们提取了卫生健康委员会报告的确诊病例的行程轨迹相关数据,并构建了初始的疾病传播网络,基于链路预测算法构建最可能的疾病传播网络。在重构前后网络的基础上,比较重构前后的不同拓扑特征,获得重要病例和感染途径。结果表明,重构后的网络具有较高的聚类系数,较短的平均路径长度,即大多数病例无法直接连通,而是通过少量病例进行接触,这是疾病在较短时间内迅速传播的原因。此外,通过对疾病传播网络中心性指标的计算和分析,我们发现青亭路和天地名城小区的2例病例是网络传播的重要病例。我们的研究结果表明,尽快隔离重要病例,切断重要传播路径,将能够为控制新冠肺炎疫情作出重大贡献。
Coronavirus Disease 2019 (COVID-19) pandemic is a grave global public health emergency. Isolating cases and cutting off the route of transmission have become the main and effective measures to control the epidemic thus far. Owing to the complexity of traceability work, the statistical data of case tracing information are incomplete. In this study, we extract data associated with the itinerary trajectories of confirmed cases reported by Health Commission and then construct the initial dis-ease transmission network. We establish the most likely disease transmission network based upon link prediction algorithms. On the basis of the network before and after reconstruction, we compare the different topological characteristics of the network to obtain important cases and infection routes. The results reveal that, the reconstructed network has higher clustering coefficient as well as shorter average path length, i.e. most cases cannot be connected but are contacted through a small amount of cases, signifying that the reason for the rapid spread of the disease in a short peri-od of time. In particular, by calculating and analyzing the centrality index of the disease transmis-sion network, we find that two cases live in Qingting road and Tiandimingcheng community respec-tively were important cases of network transmission. Our results suggest that isolating important cases and cutting off important connections as soon as possible, which will be able to significantly contribute to the control of COVID-19.
出处
《应用数学进展》
2022年第5期2559-2571,共13页
Advances in Applied Mathematics