Advances in Norovirus Detection and Surveillance: A Brief Review of Conventional Methods and Cutting-Edge Bioinformatics Approaches

With considerable effects on global public health, human norovirus (HuNoV) is the main etiological agent of acute gastroenteritis. While advancements in molecular biology have improved detection, the rapid evolution and genetic diversity of HuNoV, particularly the GII.4 variants, present ongoing challenges for reliable diagnosis and effective outbreak control. This comprehensive review analyses the critical need for integrating conventional detection methods with cutting-edge bioinformatics approaches. We first explore established laboratory methods (such as real-time polymerase chain reaction (RT-qPCR), enzyme-linked immunosorbent assay (ELISA) and electron microscopy) with respect to specific analytical performance metrices (limit of detection, turnaround time, throughput and matrix compatibility). Although these methods are still crucial for clinical diagnosis and epidemic confirmation, primer design, antibody specificity, genomic drift in viruses, and infrastructure needs may limit their efficacy. Next, we discuss the most recent developments in digital PCR, isothermal amplification, CRISPR-based tests, biosensors, and microfluidic lab-on-chips, which offer rapid, mobile, and high-throughput techniques for point-of-care, food and environmental applications. Meanwhile, next-generation sequencing offers a powerful platform for genomic epidemiology, allowing for reliable genotype classification, mutation tracking, recombination analysis, and phylogenetic monitoring. We focus on bioinformatics, machine learning, and artificial intelligence for their real-world applications in combining genomic, epidemiological, and environmental data to enhance genotype prediction, transmission cluster identification, epidemic prediction and public health decision support. We suggest for a multifaceted approach by synergistically combining conventional diagnostics clinch with innovative molecular techniques and computational data analytics to achieve early detection, sustained monitoring and better control of this long-term global pathogen.