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Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt

Author

Listed:
  • Sean G. Young

    (Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA)

  • Margaret Carrel

    (Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA
    Department of Epidemiology, University of Iowa, Iowa City, IA 52242, USA)

  • George P. Malanson

    (Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA)

  • Mohamed A. Ali

    (Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza 12311, Egypt)

  • Ghazi Kayali

    (Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Sciences Center, Houston, TX 77030, USA
    Department of Scientific Research, Human Link, Hazmieh 1107-2090, Lebanon)

Abstract

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).

Suggested Citation

  • Sean G. Young & Margaret Carrel & George P. Malanson & Mohamed A. Ali & Ghazi Kayali, 2016. "Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt," IJERPH, MDPI, vol. 13(9), pages 1-17, September.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:9:p:886-:d:77535
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    References listed on IDEAS

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    1. Rebecca S Levine & ATownsend Peterson & Krista L Yorita & Darin Carroll & Inger K Damon & Mary G Reynolds, 2007. "Ecological Niche and Geographic Distribution of Human Monkeypox in Africa," PLOS ONE, Public Library of Science, vol. 2(1), pages 1-7, January.
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    Cited by:

    1. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.

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