A Systematic Risk Assessment and Prediction Model for Avian Influenza

Avian influenza virus (AIV) is a significant pathogen affecting humans and animals, greatly burdening public health and economies worldwide. Timely and effective risk assessment and early warning systems are critical for preventing and controlling avian influenza outbreaks. However, existing risk assessment models are often limited, typically incorporating only a narrow range of factors. In this study, we constructed an integrated risk prediction model that addresses the multifactorial nature of avian influenza by incorporating pathogen, environmental, and host-related determinants. At the pathogen level, we developed models to predict viral virulence, transmissibility, host adaptation, and drug resistance, providing insights into the risks of specific viral mutations. For the environmental dimension, we constructed models that estimate viral persistence and transmissibility within various ecological and geographic contexts, utilizing meteorological and ecological data. Additionally, we assessed the likelihood of zoonotic transmission based on migratory bird patterns and factors associated with human social behaviors. From the host perspective, we analyzed epidemiological data to project population susceptibility to various avian influenza strains. Finally, we integrated these three factors into a systematic risk prediction framework, enabling a more comprehensive approach to avian influenza virus prediction and risk assessment. In retrospective analyses, our model successfully predicted several avian influenza outbreaks, demonstrating its potential for early warning. This model offers a robust tool for mitigating the impact of potential avian influenza pandemics and enhancing preventive and control strategies.

Jingru Feng, School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

Avian influenza in mammals, pandemic preparedness, and one health