Quantitative risk assessment of avian influenza: A scoping review.
Quantitative models are crucial for avian influenza risk assessment, but inconsistencies in methodologies limit their comparability and reproducibility.
Where it sits
this study against the rest of the snap-8 corpusSummary and findings
This scoping review assessed quantitative models used in avian influenza risk assessment, identifying five categories of models applied to 34 studies from 2020 to 2025. The review highlighted methodological inconsistencies and the need for harmonized workflows to improve reproducibility. It did not focus on Snap-8 or peptide-related outcomes.
Abstract
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.
Background
Avian influenza virus poses significant risks to both animal and public health, necessitating effective risk assessment strategies. Quantitative models have become increasingly important for understanding the spread of AIV by integrating various ecological, agricultural, and human data. This study is significant as it reviews the application of these models in recent years, highlighting advances and ongoing challenges in the field.
Methods
This scoping review followed PRISMA-ScR guidelines, searching PubMed, Web of Science, and ProQuest for studies published between January 2020 and March 2025. A total of 34 studies were identified and categorized into five model types: Logistic Regression-based Models, Generalized Linear Models, Machine Learning, Multi-Criteria Decision Analysis, and Exploratory Statistical Models.
Results
The review identified five categories of quantitative models used in AIV risk assessment. Logistic regression and GLMs were used to quantify associations between outbreaks and risk factors. Machine Learning models focused on predictive mapping, while MCDA combined expert weighting with spatial data to create composite risk maps. Exploratory statistical models examined spatial and temporal outbreak patterns. Despite these advances, the review noted inconsistencies in data preparation and validation.
Interpretation
The review highlights the diverse approaches used in AIV risk assessment, with each model type offering unique strengths. However, the inconsistencies in data handling and validation across studies present challenges for reproducibility and comparability. This suggests that while methodological advancements have been made, further standardization is needed to enhance the utility of these models in practical surveillance applications.
Key findings
- 34 eligible studies identified from January 2020 to March 2025.
- Five model categories: Logistic Regression, GLMs, ML, MCDA, Exploratory Statistical Models.
- Logistic regression and GLMs quantified associations between outbreaks and risk factors.
- Machine Learning models focused on predictive mapping.
- MCDA combined expert weighting with spatial data for composite risk maps.
Limitations
- Inconsistencies in data preparation and validation.
- Limited comparability across studies.
- Focus on methodological review rather than new empirical data.