Invulnerability analysis for airport weighted networks before and after COVID-19

In order to improve airport network ' s ability to cope with emergencies, topological characteristics of Chinese airport network before and after COVID-19 were analyzed based on complex network theory. And the network was weighted by using node strength, and an invulnerability assessment method was developed after identifying inflection points of loss fitting curves for weighted network characteristics metrics under different attack strategies. The results show that the topological structure of airport weighted network has no significant changes before and after the pandemic, but its connectivity is slightly sparse. And the airport network in China is much more vulnerable under different intentional attack strategies. When attack ratio reaches 8. 6%, inflection point of loss fitting curves will appear, and relative loss of global network efficiency will amount to 24. 39%, while reduction rate of the largest connected subgraph reaches 14. 67%, and relative loss of average degree and average clustering coefficient is up to 76. 87% and 68. 84%, respectively. Moreover, loss of network efficiency and the largest connected subgraph reduction rate accelerates after inflection points, in which stage the network will be paralyzed. © 2022 China Safety Science Journal. All rights reserved.

A Protein Co-Conservation Network Model Characterizes Mutation Effects on SARS-CoV-2 Spike Protein.

The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors' degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations.

A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations.

Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.

Evaluating the Impact of Vaccination on COVID-19 Pandemic Used a Hierarchical Weighted Contact Network Model

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs. © 2021 IEEE.

A Novel Pathway Network Analytics Method Based on Graph Theory.

A biological pathway is an ordered set of interactions between intracellular molecules having collective activity that impacts cellular function, for example, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lack automation. Given these circumstances, we have come up with a novel graph theoretic method to analyze disease-related genes through weighted network of biological pathways. The method automatically extracts biological structures, such as clusters of pathways and their relevance, significance of each pathway and gene, and so forth hidden in the complex network. We have demonstrated the effectiveness of the proposed method on a set of genes associated with coronavirus disease 2019.