Avian Influenza Virus (AIV) is a serious public health problem. The main causes have not yet been precisely detected. This work aims to map the AIV vulnerability based on the geographical relationships between environmental key factors and the vulnerability axis. Modeling the different predictors of AIV vulnerability is very important because the rate of contamination increases with the availability of suitable conditions. We deduced a statistical model from the geographically weighted regression results to predict the vulnerability of AIV through the district of Batna as a function of Six environmental variables (slaughterhouses, normalized difference of vegetation index, slope, temperatures, markets, and road density) and indicates that the vulnerability is highest in the Central and Eastern municipalities with more than 61% of the total area at medium or high AIV vulnerability. These results represent a modest contribution that may open a new path for environment-epidemiology relationships and associated geo-analytical databases.
Assessing Avian Influenza Vulnerability Using Geographically Weighted Regression, Batna Algeria
Fouad Feradi, Rabah Bouhata, Mohamed Issam Kalla, Mahdi Kalla; Assessing Avian Influenza Vulnerability Using Geographically Weighted Regression, Batna Algeria. The Arab World Geographer 1 September 2023; 26 (1): 76–87. doi: https://doi.org/10.5555/1480-6800-26.1.76
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