Volume 19, Issue 7 (10-2020)
JRUMS 2020, 19(7): 677-692 |
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Hashemi Foumani S, Motieyan H. Modeling the Prevalence of Avian Influenza in Guilan Province Using Data Mining Models and Spatial Information System in 2016: An Ecological Study. JRUMS 2020; 19 (7) :677-692
URL: http://journal.rums.ac.ir/article-1-5386-en.html
URL: http://journal.rums.ac.ir/article-1-5386-en.html
Babol Noshirvani University of Technology
Abstract: (1443 Views)
Background and Objectives: Infection of birds to Highly Pathogenic Avian Influenza (HPAI) and their extinction impose heavily losses on the livestock and poultry industry along with public health. Nowadays, due to the volume and variety of data, the need of using location-based technologies and data mining sciences has become inevitable. This study aims to model the prevalence of avian influenza, using the capabilities of spatial analyses.
Materials and Methods: In this analytical-ecological study, the year 2016 is selected as the target year to prepare 17 variables (climate, environment, and man-made) and their spatial layers in Guilan province because of the high prevalence of the disease in this year. The weights of the variables were computed through combination of Boosted Regression Trees (BRT) analysis and Geographically Weighted Regression (GWR), and then prevalence of the disease was prepared and evaluated by the Receiver Operating Characteristic (ROC) curve.
Results: The variables of wetlands, live poultry markets, and pools have the highest weights according to BRT analysis, with 18.91, 15.59, and 12.8 percent, respectively. Also, in terms of time, the month of February has the highest prevalence among the three cold months of the year.
Conclusion: The disease has been observed in the areas around wetlands, pools, and live poultry markets. Therefore, the General Veterinary Administration, as a regulatory and policy-making body, and poultry producers and sellers as executive agents can play a significant role in monitoring, controlling, and preventing the spread of the disease.
Key words: Avian influenza, Spatial analysis, Boosted regression, Geographically weighted regression
Funding: This study did not have any funds.
Conflict of interest: None declared.
Ethical approval: The Ethics Committee of Babol Noshirvani University of Technology approved the study.
How to cite this article: Hashemi Foumani S M, Motieyan H. Modeling the Prevalence of Avian Influenza in Guilan Province Using Data Mining Models and Spatial Information System in 2016: An Ecological Study. J Rafsanjan Univ Med Sci 2020; 19 (7): 677-92. [Farsi]
Materials and Methods: In this analytical-ecological study, the year 2016 is selected as the target year to prepare 17 variables (climate, environment, and man-made) and their spatial layers in Guilan province because of the high prevalence of the disease in this year. The weights of the variables were computed through combination of Boosted Regression Trees (BRT) analysis and Geographically Weighted Regression (GWR), and then prevalence of the disease was prepared and evaluated by the Receiver Operating Characteristic (ROC) curve.
Results: The variables of wetlands, live poultry markets, and pools have the highest weights according to BRT analysis, with 18.91, 15.59, and 12.8 percent, respectively. Also, in terms of time, the month of February has the highest prevalence among the three cold months of the year.
Conclusion: The disease has been observed in the areas around wetlands, pools, and live poultry markets. Therefore, the General Veterinary Administration, as a regulatory and policy-making body, and poultry producers and sellers as executive agents can play a significant role in monitoring, controlling, and preventing the spread of the disease.
Key words: Avian influenza, Spatial analysis, Boosted regression, Geographically weighted regression
Funding: This study did not have any funds.
Conflict of interest: None declared.
Ethical approval: The Ethics Committee of Babol Noshirvani University of Technology approved the study.
How to cite this article: Hashemi Foumani S M, Motieyan H. Modeling the Prevalence of Avian Influenza in Guilan Province Using Data Mining Models and Spatial Information System in 2016: An Ecological Study. J Rafsanjan Univ Med Sci 2020; 19 (7): 677-92. [Farsi]
Type of Study: Research |
Subject:
Statistics& Epidemiology
Received: 2020/05/30 | Accepted: 2020/08/1 | Published: 2020/10/19
Received: 2020/05/30 | Accepted: 2020/08/1 | Published: 2020/10/19
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