One Health-Based Modelling of H5N1 Clade 2.3.4.4b Spillover Risk in Urban and Peri-Urban Interfaces

Avian Influenza H5N1 clade 2.3.4.4b has caused widespread global outbreaks since 2020, with increasing transmission to mammalian hosts, including dairy cattle, domestic cats, and wildlife species. Urban and peri-urban environments may act as important interfaces for viral spread because of dense human and animal populations, backyard poultry keeping, and expanding wildlife contact. This study applied a One Health framework to assess spillover risk and develop an integrated epidemiological prediction model.
A systematic review was conducted according to PRISMA guidelines using data retrieved from PubMed, Web of Science, Scopus, GISAID, WOAH WAHIS, and WHO databases between 2020 and 2026. Integrated datasets included surveillance records, genomic sequences, climate variables (ERA5), land-use information (Copernicus), and wastewater monitoring reports. A hybrid framework integrating compartmental transmission modelling with machine learning-based hotspot prediction was developed to evaluate spillover dynamics and early warning capacity. Model performance was assessed using k-fold cross-validation and retrospective comparison with outbreaks reported during 2024–2025.
Peri-urban areas with high backyard poultry density (>5 units/ha) and proximity to water bodies (<1.5 km) were significantly associated with increased spillover risk (OR = 3.87; 95% CI: 2.64–5.68; p < 0.001). Integration of wastewater and community-based surveillance data improved predictive performance (AUC = 0.91; sensitivity = 87%) compared with conventional surveillance approaches. The framework also showed potential to identify high-risk periods approximately 5–7 weeks earlier than routine reporting systems. Climate variability and land-use change were identified as major predictors of viral persistence and spillover probability.
These findings support the importance of integrated One Health surveillance strategies for improving outbreak preparedness, early warning capacity, and risk mitigation in urban and peri-urban settings.