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

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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).

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(A) Locations of H5N1 Outbreaks in the study area; and (B) the accompanying niche model for H5N1.
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ijerph-13-00886-f003: (A) Locations of H5N1 Outbreaks in the study area; and (B) the accompanying niche model for H5N1.

Mentions: The estimated distribution of areas suitable for H5N1 and H9N2, as predicted by the niche models, are shown in Figure 3 and Figure 4. The niche estimate for co-infection cases is depicted in Figure 5. Figure 6 shows the maps of co-infection potential derived from the niche estimates for H5N1 and H9N2 by multiplication and averaging. Niche equivalency and background similarity test results are shown in Table 4. All model comparisons using the I metric (H5N1 vs. H9N2, H5N1 vs. co-infection, and H9N2 vs. co-infection) were less than 1 and statistically significant and, therefore, non-equivalent.


Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt
(A) Locations of H5N1 Outbreaks in the study area; and (B) the accompanying niche model for H5N1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036719&req=5

ijerph-13-00886-f003: (A) Locations of H5N1 Outbreaks in the study area; and (B) the accompanying niche model for H5N1.
Mentions: The estimated distribution of areas suitable for H5N1 and H9N2, as predicted by the niche models, are shown in Figure 3 and Figure 4. The niche estimate for co-infection cases is depicted in Figure 5. Figure 6 shows the maps of co-infection potential derived from the niche estimates for H5N1 and H9N2 by multiplication and averaging. Niche equivalency and background similarity test results are shown in Table 4. All model comparisons using the I metric (H5N1 vs. H9N2, H5N1 vs. co-infection, and H9N2 vs. co-infection) were less than 1 and statistically significant and, therefore, non-equivalent.

View Article: PubMed Central - PubMed

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).

No MeSH data available.


Related in: MedlinePlus