ABSTRACT
Objective. An outbreak of pneumonia named COVID-19 caused by a novel coronavirus in Wuhan is rapidly spreading worldwide. The objective of the present study was to clarify further the clinical characteristics and blood parameters in COVID-19 patients. Materials and Methods. Twenty-three suspected patients and 64 patients with laboratory-confirmed SARS-Cov-2 infection were admitted to a designated hospital. Epidemiological, clinical, laboratory, and treatment data were collected and analyzed. Results. Of the 64 patients studied, 47 (73.4%) had been exposed to a confirmed source of COVID-19 transmission. On admission, the most common symptoms were fever (75%) and cough (76.6%). Twenty-eight (43.8%) COVID-19 patients showed leukopenia, 10 (15.6%) showed lymphopenia, 47 (73.4%) and 41 (64.1%) had elevated high-sensitivity C-reactive protein (hsCRP) and erythrocyte sedimentation rate (ESR), respectively, and 30 (46.9%) had increased fibrinogen concentration. After the treatment, the counts of white blood cells and platelets, and the level of prealbumin increased significantly, while aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and hsCRP decreased. COVID-19 patients with the hospital stay longer than 12 days had higher body mass index (BMI) and increased levels of AST, LDH, fibrinogen, hsCRP, and ESR. Conclusions. Results of blood tests have potential clinical value in COVID-19 patients.Copyright © 2020 by the Association of Clinical Scientists, Inc.
ABSTRACT
Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node's nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.
ABSTRACT
In the field of complex networks, Identifying crucial spreaders with high propagation ability is an important aspect of research, especially in the background of the global spread of COVID-19. In view of this, a large number of ranking algorithms and their improved versions have been proposed to evaluate the importance of nodes in the network, such as degree centrality, betweenness centrality, and k-core centrality. However, most of these methods neglect to consider the average shortest path between important nodes in the process of node importance evaluation, which will be difficult to ensure that the initial crucial spreaders have a large influence on the network. Recently, the VoteRank algorithm proposed a new idea for identifying widely distributed key spreaders based on the voting mechanism, but there are some aspects of this algorithm that require improvement. In this paper, we propose a VoteRank improved by degree centrality, k-core, and h-index (DKHVoteRank) for identifying critical spreaders in the complex networks. We introduce additional metrics to optimize the voting mechanism of the VoteRank to ensure that our algorithm can identify a widely distributed spreaders with high importance in the network. We conducted simulation experiments based on the Susceptible-Infected-Recovered (SIR) model on 12 different complex network datasets, and the results show that our proposed algorithm performs significantly better than other benchmark algorithms in terms of propagation capability, propagation scale, and applicability of the algorithm.