tetano
Editor, Senior Moderator
Environ Sci Pollut Res Int
. 2022 Feb 6.
doi: 10.1007/s11356-022-19016-1. Online ahead of print.
Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1
Wei Chen[SUP] 1 [/SUP], Xuepeng Zhang[SUP] 2 [/SUP], Wenwu Zhao[SUP] 3 4 [/SUP], Lan Yang[SUP] 2 [/SUP], Zhe Wang[SUP] 2 [/SUP], Hongru Bi[SUP] 2 [/SUP]
Affiliations
Abstract
The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.
Keywords: Binary logistic regression; COVID-19; H5N1; Remote sensing; Responsible Editor: Lotfi Aleya; Spatiotemporal analysis.
. 2022 Feb 6.
doi: 10.1007/s11356-022-19016-1. Online ahead of print.
Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1
Wei Chen[SUP] 1 [/SUP], Xuepeng Zhang[SUP] 2 [/SUP], Wenwu Zhao[SUP] 3 4 [/SUP], Lan Yang[SUP] 2 [/SUP], Zhe Wang[SUP] 2 [/SUP], Hongru Bi[SUP] 2 [/SUP]
Affiliations
- PMID: 35128608
- DOI: 10.1007/s11356-022-19016-1
Abstract
The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.
Keywords: Binary logistic regression; COVID-19; H5N1; Remote sensing; Responsible Editor: Lotfi Aleya; Spatiotemporal analysis.