tetano
Editor, Senior Moderator
J Gen Virol
. 2026 Jun;107(6):002278.
doi: 10.1099/jgv.0.002278.
Molecular surveillance and predictive risk modelling of avian influenza virus in wild birds in Egypt
Nehal M Nabil[SUP] 1 [/SUP], Maram M Tawakol[SUP] 1 [/SUP], Naglaa Hagag[SUP] 1 [/SUP], Samah Eid[SUP] 1 [/SUP], Liam Brierley[SUP] 2 3 [/SUP], Mahmoud M Naguib[SUP] 3 4 [/SUP]
Affiliations
Avian influenza viruses (AIVs) continue to circulate and pose a persistent threat to animal and public health in Egypt, a country located along major migratory birds' flyways that enable repeated viral incursions. In this study, a total of 1,087 wild birds representing 19 species were sampled to assess the role of migratory wild birds in virus introduction and spread. Six species were found positive for AIV, with Eurasian teal showing the highest prevalence (12.3%). The H5 subtype was found predominant, representing 70.3% of positive cases, whereas H9 was detected in 9.9% of positive cases. Moreover, coinfections with H5/H9 were observed in Eurasian teal, Common moorhen and Northern pintail, suggesting opportunities for cocirculation among migratory species. To predict areas at high risk of AIV detection, these findings were integrated with additional publicly available geolocated AIV sampling records and a spatial machine learning (ML) model was trained. The model demonstrated moderate predictive performance (Area Under Curve =0.608; F1=0.596) and highlighted relative humidity, temperature and proximity to wetlands as primary predictors of AIV detection. In addition, risk projections for early winter underscore several areas including the Nile Delta, Nile River corridor, southern wetlands near Lake Nasser, the south-eastern Red Sea coast and the north-western Siwa region as areas of high risk of AIV detection. The findings of this study demonstrate the role of migratory wild birds in the spread and introduction of AIV into Egypt, particularly H5, and provide an ML-based model to predict areas of high risk of virus detection in Egypt, which could in turn strengthen early detection and guide mitigation strategies.
Keywords: Avian influenza virus; Egypt; machine learning; migratory birds; spatial risk modelling.
. 2026 Jun;107(6):002278.
doi: 10.1099/jgv.0.002278.
Molecular surveillance and predictive risk modelling of avian influenza virus in wild birds in Egypt
Nehal M Nabil[SUP] 1 [/SUP], Maram M Tawakol[SUP] 1 [/SUP], Naglaa Hagag[SUP] 1 [/SUP], Samah Eid[SUP] 1 [/SUP], Liam Brierley[SUP] 2 3 [/SUP], Mahmoud M Naguib[SUP] 3 4 [/SUP]
Affiliations
- PMID: 42301748
- PMCID: PMC13271260
- DOI: 10.1099/jgv.0.002278
Avian influenza viruses (AIVs) continue to circulate and pose a persistent threat to animal and public health in Egypt, a country located along major migratory birds' flyways that enable repeated viral incursions. In this study, a total of 1,087 wild birds representing 19 species were sampled to assess the role of migratory wild birds in virus introduction and spread. Six species were found positive for AIV, with Eurasian teal showing the highest prevalence (12.3%). The H5 subtype was found predominant, representing 70.3% of positive cases, whereas H9 was detected in 9.9% of positive cases. Moreover, coinfections with H5/H9 were observed in Eurasian teal, Common moorhen and Northern pintail, suggesting opportunities for cocirculation among migratory species. To predict areas at high risk of AIV detection, these findings were integrated with additional publicly available geolocated AIV sampling records and a spatial machine learning (ML) model was trained. The model demonstrated moderate predictive performance (Area Under Curve =0.608; F1=0.596) and highlighted relative humidity, temperature and proximity to wetlands as primary predictors of AIV detection. In addition, risk projections for early winter underscore several areas including the Nile Delta, Nile River corridor, southern wetlands near Lake Nasser, the south-eastern Red Sea coast and the north-western Siwa region as areas of high risk of AIV detection. The findings of this study demonstrate the role of migratory wild birds in the spread and introduction of AIV into Egypt, particularly H5, and provide an ML-based model to predict areas of high risk of virus detection in Egypt, which could in turn strengthen early detection and guide mitigation strategies.
Keywords: Avian influenza virus; Egypt; machine learning; migratory birds; spatial risk modelling.