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" Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt. "
Young, Sean G; Young, Sean G; Carrel, Margaret; Malanson, George P; Ali, Mohamed A; Kayali, Ghazi
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AL
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| Record Number
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912503
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LA8p93p3p1
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| Title & Author
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Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt. [Article]\ Young, Sean G; Young, Sean G; Carrel, Margaret; Malanson, George P; Ali, Mohamed A; Kayali, Ghazi
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2016
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| Title of Periodical
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UCLA
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| Abstract
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Human outbreaks with avian influenza have been, so far, constrained by poor viral adaptation to non-avian hosts. This could be overcome via co-infection, whereby two strains share genetic material, allowing new hybrid strains to emerge. Identifying areas where co-infection is most likely can help target spaces for increased surveillance. Ecological niche modeling using remotely-sensed data can be used for this purpose. H5N1 and H9N2 influenza subtypes are endemic in Egyptian poultry. From 2006 to 2015, over 20,000 poultry and wild birds were tested at farms and live bird markets. Using ecological niche modeling we identified environmental, behavioral, and population characteristics of H5N1 and H9N2 niches within Egypt. Niches differed markedly by subtype. The subtype niches were combined to model co-infection potential with known occurrences used for validation. The distance to live bird markets was a strong predictor of co-infection. Using only single-subtype influenza outbreaks and publicly available ecological data, we identified areas of co-infection potential with high accuracy (area under the receiver operating characteristic (ROC) curve (AUC) 0.991).
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