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 d...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.展开更多
目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发...目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。展开更多
基金supported by the National Key R&D Program of China(2022YFF1203202,2018YFC2000205)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38050200,XDA26040304)the Self-supporting Program of Guangzhou Laboratory(SRPG22-007).
文摘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.
文摘目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。
