In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.
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http://dx.doi.org/10.1007/s11277-021-08998-9 | DOI Listing |
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFBiochemistry
January 2025
National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States.
SARS-CoV-2 infections led to a worldwide pandemic in 2020. As of 2024, therapeutics against SARS-CoV-2 have continued to be desirable. NSP14 is a dual-function methyltransferase (MTase) and exonuclease (ExoN) with key roles in SARS-CoV-2 genome propagation and host immune system evasion.
View Article and Find Full Text PDFViruses
December 2024
World Health Organization, 1202 Geneva, Switzerland.
Setting up a global SARS-CoV-2 surveillance system requires an understanding of how virus isolation and propagation practices, use of animal or human sera, and different neutralisation assay platforms influence assessment of SARS-CoV-2 antigenicity. In this study, with the contribution of 15 independent laboratories across all WHO regions, we carried out a controlled analysis of neutralisation assay platforms using the first WHO International Standard for antibodies to SARS-CoV-2 variants of concern (source: NIBSC). Live virus isolates (source: WHO BioHub or individual labs) or spike plasmids (individual labs) for pseudovirus production were used to perform neutralisation assays using the same serum panels.
View Article and Find Full Text PDFVirology
December 2024
Departments of Surgery & Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA. Electronic address:
The sphingolipid network is sustained principally by the balance of bioactive sphingolipid molecules and their regulation by sphingolipid-metabolizing enzymes. The components in the lipid system display key functions in numerous cellular and disease conditions including virus infections. During the COVID-19 pandemic, there was a fruitful effort to use an inhibitor that blocks the activity of sphingosine kinase (SphK) 2 to cure the devastating disease.
View Article and Find Full Text PDFMol Biomed
January 2025
Department of Artificial Intelligence and Machine Learning, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, Maharashtra, 442001, India.
Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses.
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