Revolutionizing infectious disease surveillance: Multi-omics technologies and AI-driven integration

The integration of advanced technologies, including next-generation sequencing, multiomics approaches, and artificial intelligence (AI), has revolutionized pathogen surveillance and preparedness. Multiomics technologies provide detailed information about how pathogens work and interact with hosts. However, the integration of diverse omics data poses bioinformatics challenges related to data heterogeneity, dimensionality, and standardization. AI is crucial for solving problems, predicting outbreaks in advance, accurately forecasting how diseases spread, and identifying new pathogen changes. Using AI in conjunction with omics and epidemiological information simplifies activities that include the identification of biomarkers, the classification of individuals, and individual treatments. The use of genomic surveillance monitoring of tuberculosis (TB) and foodborne outbreaks and AI to predict the spread of COVID-19, detect variants, and develop vaccines with the help of multiomics technologies may be considered successful. Nevertheless, the current adoption of AI-based services in public health is characterized by issues of data quality, the presence of bias in the algorithmic system, the unit of explainability, and even ethical implications. Increased preparedness for pandemics at the global level requires collaboration networks, the sharing of open data, the protection of privacy, and the adoption of One Health approaches. The increasing development of multiomics approaches and AI-based techniques and their combination has great potential to transform infectious disease surveillance and response during the precision public health era. • Next-generation sequencing and omics tools revolutionize pathogen tracking and preparedness. • Genomics–AI integration improves insights into pathogen transmission dynamics. • Whole-genome sequencing reveals pathogen networks and resistance mechanisms. • Multi-omics uncovers host–pathogen molecular interactions across biological layers. • AI-driven omics analysis enhances pathogen profiling and resistance prediction.