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基于多模态数据的新发传染病早期预警

Early warning of emerging infectious diseases based on multimodal data.

作者信息

Ren Haotian, Ling Yunchao, Cao Ruifang, Wang Zhen, Li Yixue, Huang Tao

机构信息

Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024 China.

出版信息

Biosaf Health. 2023 Jun 7;5(4):193-203. doi: 10.1016/j.bsheal.2023.05.006.

DOI:10.1016/j.bsheal.2023.05.006
PMID:37362865
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10245235/
Abstract

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

摘要

2019年冠状病毒病(COVID-19)大流行极大地提高了人们对新发传染病的认识。多组学分析技术的进步催生了几个包含病毒信息的数据库。一些科学家整合了现有的病毒数据,以不同方式构建系统发育树并预测病毒突变和传播,为疫情防控提供了前瞻性技术支持。本综述总结了已知新发传染病病毒的数据库以及专注于病毒变异预测和预警的技术。它聚焦于新发传染病病毒的多维度信息整合与数据库构建、病毒突变谱构建与变异预测模型、突变抗原与受体亲和力分析、病毒动态进化传播模型以及变异监测与预警。由于人们遭受了COVID-19和反复的流感疫情,我们重点关注了严重急性呼吸综合征冠状病毒2(SARS-CoV-2)和流感病毒的研究成果。本综述全面审视了最新的病毒研究,为未来的病毒防控研究提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/448cbd3d0dcc/gr4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/c69abd4e730a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/448cbd3d0dcc/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/5a66b2f0a5cd/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/5acdecebc654/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/c69abd4e730a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4efb/11894949/448cbd3d0dcc/gr4.jpg

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