Quantitative risk assessment of avian influenza: A scoping review

Avian influenza virus (AIV) continues to pose serious risks to animal and public health. Understanding its spread requires integrating ecological, agricultural, and human information. Quantitative models provide a practical way to represent these interactions, evaluate multiple risk factors, and generate spatial insights that support early detection and control. In recent years, advances in data availability and computational methods have increased the use of these models for AIV risk assessment. This review mapped how quantitative modelling has been applied to AIV risk assessment in recent years. Following PRISMA-ScR guidelines, we searched PubMed, Web of Science, and ProQuest for studies published between January 2020 and March 2025, identifying 34 eligible studies. Five model categories were identified: Logistic Regression-based Models, Generalized Linear Models (GLMs), Machine Learning (ML), Multi-Criteria Decision Analysis (MCDA), and Exploratory Statistical Models. Logistic regression and GLMs quantified associations between outbreaks and risk factors, while ML models focused on predictive mapping. MCDA combined expert weighting with spatial data to generate composite risk maps, and exploratory analyses examined spatial and temporal outbreak patterns. Despite methodological progress, inconsistencies remain in data preparation and validation, limiting comparability across studies. Clearer, harmonized workflows are needed to improve reproducibility and support translation into surveillance tools globally.