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Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data.

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  • المؤلفون: Wang Y;Wang Y; Zhao Y; Zhao Y; Pan Q; Pan Q
  • المصدر:
    Briefings in bioinformatics [Brief Bioinform] 2022 Jan 17; Vol. 23 (1).
  • نوع النشر :
    Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Systematic Review
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
    • بيانات النشر:
      Publication: Oxford : Oxford University Press
      Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
    • الموضوع:
      COVID-19*/epidemiology ; COVID-19*/immunology ; COVID-19*/transmission ; Models, Biological* ; Pandemics* ; Phylogeny* ; SARS-CoV-2*/genetics ; SARS-CoV-2*/immunology ; SARS-CoV-2*/pathogenicity; Humans
    • نبذة مختصرة :
      Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.
      (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
    • Grant Information:
      1636933 National Science Foundation
    • Contributed Indexing:
      Keywords: batch effects; control policy; epidemiology model with network topology; network characteristics; phylogenetic tree; sampling bias
    • الموضوع:
      Date Created: 20211003 Date Completed: 20220201 Latest Revision: 20220201
    • الموضوع:
      20250114
    • الرقم المعرف:
      10.1093/bib/bbab406
    • الرقم المعرف:
      34601563