AI Model Cracks Bird Flu Human Spillover Code

Indian researchers have developed an Artificial Intelligence-based model named BharatSim to understand the spillover dynamics of the H5N1 bird flu virus from birds to humans. This ultra-large-scale agent-based simulation framework, originally built for COVID-19, now describes the sequential stages of zoonotic spillover, including potential human-to-human transmission. The model emphasizes that early intervention is crucial, with culling birds identified as the most effective measure to curb H5N1 outbreaks in farm or market settings, provided no primary infection has occurred.

Since its emergence in the late 1990s, avian flu has occasionally infected humans. The World Health Organization reports that between 2003 and August 2025, there have been 990 human H5N1 cases across 25 countries, resulting in 475 deaths, a concerning 48 percent fatality rate. The study indicates that controlling secondary infections relies on isolating cases and quarantining household contacts, but beyond tertiary contacts, stringent measures like lockdowns become necessary.

The computational model demonstrates that policy measures explored in real-time can constrain disease spread, offering insights into the epidemiology of emerging infectious diseases. Researchers stress that once community transmission begins, public health measures such as lockdowns, compulsory masking, and large-scale vaccination drives become the primary recourse. The findings underscore the importance of rapid and decisive action in the initial stages of an outbreak.