From mapping to prediction: A systematic review and critical appraisal of methodological approaches to assessing avian influenza incursion risk in domestic poultry.

Highly pathogenic avian influenza (HPAI) viruses continue to threaten global poultry production and public health. To examine how methodological approaches have been used to assess this risk, we conducted a systematic review and critical appraisal of peer-reviewed studies assessing avian influenza virus (AIV) incursion risk in domestic poultry, using PubMed and Web of Science databases. Quantitative modelling approaches ranged from statistical, data-driven methods to mechanistic frameworks and operated at different spatial and temporal scales, using farms or geographical areas as the epidemiological unit of interest. Across studies, analytical choices were largely shaped by data availability, which constrained achievable resolution and influenced the questions models could address. Most applications focused on area-level analyses, often describing historical spatial risk patterns rather than supporting prospective inference. Statistical models frequently relied on proxy variables linked to past incursions, limiting biological interpretability and robustness. Recent improvements in wild bird surveillance, including citizen-science initiatives, have enabled more explicit representation of wild bird dynamics, although persistent gaps in poultry population data continue to restrict model development in many settings. In settings where farm-level data are limited, mechanistic approaches are commonly applied, relying on assumptions regarding underlying disease transmission dynamics. A consistent challenge across studies was limited model validation, reflecting sparse outbreak data and spatial and temporal clustering of outbreaks. Interpretation was further complicated by scale mismatches and risks of ecological and atomistic fallacies when extrapolating between farm-level and area-level outputs. Overall, the review highlights the importance of ensuring that risk questions and predictive expectations reflect the available data and the feasibility of validation.