e-Bulletin July 2026

A Pioneering AI Early Detection System to Reduce Human Risk from Avian Influenza A Viruses
Identification of Key Genomic Clue for Early Pandemic Detection

A research team led by Professor Tommy Lam, Associate Professor at our School, has developed a machine-learning classifier capable of analysing the genomes of influenza A viruses (IAVs) to accurately predict their potential risk of transmission among mammals. The team has successfully identified the key clues that may explain cross-species transmission of influenza A viruses from birds to mammals, and even to humans. The study found that when guanine (G) or cytosine (C) associated in the IVA genome decreased, the virus demonstrated a higher risk for sustained transmission in mammals, including humans. The research team recommends incorporating this genome signature into future influenza pandemic risk assessment frameworks to facilitate the early identification of high-risk viral strains. This groundbreaking study has been published in Nature Microbiology [link to the publication].

Influenza viruses come in various types, such as IAVs, which are commonly found in birds (also known as avian influenza) and can infect other animals, including mammals and humans. Once an avian influenza virus successfully adapts to the mammalian host environment and, more critically, gains the ability for human-to-human transmission, it could trigger an influenza pandemic, posing a severe threat to public health.

Professor Lam emphasised that introducing more refined risk assessment methods, including the incorporation of GC-related genomic content, would help strengthen surveillance efforts and facilitate earlier detection of high-risk viruses, enabling timely action at the early stage of human infection.