Population migration is a critical component of large-scale spatiotemporal models of infectious disease transmission.Identifying the most influential spreaders in networks is vital to controlling and understanding the...Population migration is a critical component of large-scale spatiotemporal models of infectious disease transmission.Identifying the most influential spreaders in networks is vital to controlling and understanding the spreading process of infectious diseases.We used Baidu Migration data for the whole year of 2021 to build mobility networks.The nodes of the network represent cities,and the edges represent the population flow between cities.By applying the k-shell decomposition and the Louvain algorithm,we could get the k-shell values for each city and community partition.Then,we identified the most efficient nodes or pathways in a complex network by generating random networks.Furthermore,we analyzed the eigenvalue of the migration matrix to find the nodes that have the most impact on the network.We also found the consistency between k-shell value and eigenvalue through Kendall's t test.The main result is that in Spring Festival and National Day,the network is at higher risk of an infectious disease outbreak and the Yangtze River Delta is at the highest risk of an epidemic all year around.Shanghai is the most significant node in both k-shell value and eigenvalue analysis.The spatiotemporal property of the network should be taken into account to model the transmission of infectious diseases more accurately.展开更多
An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its commu...An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method,out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.展开更多
基金supported by the Shanghai Municipal Health Commission Clinical Research Program (20214Y0020)the General Program of Natural Science Foundation of Shanghai Municipality (22ZR1414600)the Young Health Talents Program of Shanghai Municipality (2022YQ076).
文摘Population migration is a critical component of large-scale spatiotemporal models of infectious disease transmission.Identifying the most influential spreaders in networks is vital to controlling and understanding the spreading process of infectious diseases.We used Baidu Migration data for the whole year of 2021 to build mobility networks.The nodes of the network represent cities,and the edges represent the population flow between cities.By applying the k-shell decomposition and the Louvain algorithm,we could get the k-shell values for each city and community partition.Then,we identified the most efficient nodes or pathways in a complex network by generating random networks.Furthermore,we analyzed the eigenvalue of the migration matrix to find the nodes that have the most impact on the network.We also found the consistency between k-shell value and eigenvalue through Kendall's t test.The main result is that in Spring Festival and National Day,the network is at higher risk of an infectious disease outbreak and the Yangtze River Delta is at the highest risk of an epidemic all year around.Shanghai is the most significant node in both k-shell value and eigenvalue analysis.The spatiotemporal property of the network should be taken into account to model the transmission of infectious diseases more accurately.
基金Supported by Fundamental Research Funds for the Central Universities(JBK170133)Natural Science Foundation of Sichuan Province of China(17ZB0434)Ministry of Education of Humanities and Social Science Foundation of China(11XJCZH002)
文摘An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method,out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.
