Early warning of emerging infectious diseases based on multimodal data

Area of Science:

• Virology
• Bioinformatics
• Epidemiology

Background:

• The COVID-19 pandemic underscored the importance of understanding emerging infectious diseases.
• Multi-omics technologies have led to the creation of comprehensive virus information databases.
• Existing data integration aids in constructing phylogenetic trees and predicting virus behavior.

Purpose of the Study:

• To review databases of emerging infectious viruses.
• To summarize techniques for virus variant forecasting and early warning.
• To provide a reference for future virus prevention and control research, focusing on SARS-CoV-2 and influenza.

Main Methods:

• Integration of multi-dimensional information for database construction of emerging infectious viruses.
• Development of virus mutation spectrum and variant forecasting models.
• Analysis of mutation-receptor affinity and virus dynamic evolution propagation models.

Main Results:

• A comprehensive overview of current virus research, databases, and forecasting techniques.
• Focus on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus research.
• Identification of key areas: multi-dimensional data integration, variant forecasting, and early warning systems.

Conclusions:

• Effective virus variant forecasting and early warning systems are crucial for epidemic control.
• Multi-omics data integration and advanced modeling support proactive public health strategies.
• This review offers a valuable reference for advancing infectious disease surveillance and response.