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

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

Jackknife test results using area under the receiver operating characteristic (ROC) curve (AUC) for H5N1.
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ijerph-13-00886-f007: Jackknife test results using area under the receiver operating characteristic (ROC) curve (AUC) for H5N1.

Mentions: Jackknife tests are summarized in Figure 7, Figure 8 and Figure 9. The strongest single contributor to the H5N1 niche was human population, which was also the variable that caused the largest loss in AUC when it was removed from the model. Mean diurnal surface temperature (summer and winter), Elevation, and mean NDVI (summer and winter) were also strong contributors. For the H9N2 model, the strongest single contributor was mean summer NDVI, followed by Poultry density and water scarcity. The largest drop in AUC accompanied the removal of elevation from the model. The highest overall AUC for the H9N2 model was achieved by the full model after removing Distance to major highways. In both models, air temperatures, precipitation measurements, and distance to fresh water were generally poor contributors. The strongest contributor to the co-infection niche was distance to LBM, followed by human population and elevation. The largest drop in AUC coincided with the removal of the wealth index.


Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt
Jackknife test results using area under the receiver operating characteristic (ROC) curve (AUC) for H5N1.
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00886-f007: Jackknife test results using area under the receiver operating characteristic (ROC) curve (AUC) for H5N1.
Mentions: Jackknife tests are summarized in Figure 7, Figure 8 and Figure 9. The strongest single contributor to the H5N1 niche was human population, which was also the variable that caused the largest loss in AUC when it was removed from the model. Mean diurnal surface temperature (summer and winter), Elevation, and mean NDVI (summer and winter) were also strong contributors. For the H9N2 model, the strongest single contributor was mean summer NDVI, followed by Poultry density and water scarcity. The largest drop in AUC accompanied the removal of elevation from the model. The highest overall AUC for the H9N2 model was achieved by the full model after removing Distance to major highways. In both models, air temperatures, precipitation measurements, and distance to fresh water were generally poor contributors. The strongest contributor to the co-infection niche was distance to LBM, followed by human population and elevation. The largest drop in AUC coincided with the removal of the wealth index.

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