In complex networks, identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks. Nowadays, it is widely used in power networks, aviation networks, computer networks, and social networks, and so on. Traditional centrality methods mainly include degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, k-shell, etc. However, single centrality method is one-sided and inaccurate, and sometimes many nodes have the same centrality value, namely the same ranking result, which makes it difficult to distinguish between nodes. According to several classical methods of identifying influential nodes, in this paper we propose a novel method that is more full-scaled and universally applicable. Taken into account in this method are several aspects of node’s properties, including local topological characteristics, central location of nodes, propagation characteristics, and properties of neighbor nodes. In view of the idea of the multi-attribute decision-making, we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes, and obtain node’s combined centrality. Then, the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks. Finally, the classical susceptible-infected-recovered (SIR) model is used to simulate the epidemic spreading in six real-society networks. Our proposed method not only considers the four topological properties of nodes, but also emphasizes the influence of neighbor nodes from the aspect of gravity. It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes, which is of great significance for monitoring and controlling the complex networks.
Background Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases. Methods We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases. Results The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals’ infection probability by 10%. The proposed model was validated using COVID-19 testing data. Conclusions Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.
A new red fluorescent probe based on rosamine–phenothiazine for highly selective and rapid detection of hypochlorite and its bioimaging in live cells.
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