Abstract

Long endemicity of the Highly Pathogenic Avian Influenza (HPAI) H5N1 subtype in Egypt poses a lot of threats to public health. Contrary to what is previously known, outbreaks have been circulated continuously in the poultry sectors all year round without seasonality. These changes call the need for epidemiological studies to prove or deny the influence of climate variability on outbreak occurrence, which is the aim of this study. This work proposes a modern approach to examine the degree to which the HPAI-H5N1disease event is being influenced by climate variability as a potential risk factor using generalized estimating equations (GEEs). GEE model revealed that the effect of climate variability differs according to the timing of the outbreak occurrence. Temperature and relative humidity could have both positive and negative effects on disease events. During the cold seasons especially in the first quarter, higher minimum temperatures, consistently show higher risks of disease occurrence, because this condition stimulates viral activity, while lower minimum temperatures support virus survival in the other quarters of the year with the highest negative effect in the third quarter. On the other hand, relative humidity negatively affects the outbreak in the first quarter of the year as the humid weather does not support viral circulation, while the highest positive effect was found in the second quarter during which low humidity favors the disease event.

Highlights

  • Avian influenza posed a significant pandemic threat [1, 2] through the persistence of mutant or genetically reassorted progenitor in poultry [3]

  • The outbreak is significantly associated with minimum temperature and relative humidity (p

  • The outbreak is significantly associated with the minimum, maximum temperatures and relative humidity (p

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Summary

Introduction

Avian influenza posed a significant pandemic threat [1, 2] through the persistence of mutant or genetically reassorted progenitor in poultry [3]. Based on the results from GLM, Poisson regression using generalized estimating equations (GEEs) is done to investigate the degree to which the HPAI-H5N1 event is being influenced by climate variability as a potential risk factor in the spread and maintenance of the virus in the environment. Model is based on epidemic waves outbreak data with repeated measurements from the four quarters of the year over the period 2006–2016 from Menofia, Egypt.

Results
Conclusion

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