دورية أكاديمية
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Identification of Pre-Emptive Biosecurity Zone Areas for Highly Pathogenic Avian Influenza Based on Machine Learning-Driven Risk Analysis.

التفاصيل البيبلوغرافية
العنوان: Identification of Pre-Emptive Biosecurity Zone Areas for Highly Pathogenic Avian Influenza Based on Machine Learning-Driven Risk Analysis.
المؤلفون: Jeon KM; AI Convergence Technology Laboratory, Intflow Inc., Gwangju 61472, Republic of Korea., Jung J; AI Convergence Technology Laboratory, Intflow Inc., Gwangju 61472, Republic of Korea., Lee CM; Department of Veterinary Internal Medicine, Chonnam National University, Gwangju 61186, Republic of Korea., Yoo DS; Department of Veterinary Internal Medicine, Chonnam National University, Gwangju 61186, Republic of Korea.
المصدر: Animals : an open access journal from MDPI [Animals (Basel)] 2023 Dec 01; Vol. 13 (23). Date of Electronic Publication: 2023 Dec 01.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Molecular Diversity Preservation International Country of Publication: Switzerland NLM ID: 101635614 Publication Model: Electronic Cited Medium: Print ISSN: 2076-2615 (Print) Linking ISSN: 20762615 NLM ISO Abbreviation: Animals (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : Molecular Diversity Preservation International, 2011-
مستخلص: Over the last decade, highly pathogenic avian influenza (HPAI) has severely affected poultry production systems across the globe. In particular, massive pre-emptive depopulation of all poultry within a certain distance has raised concerns regarding animal welfare and food security. Thus, alternative approaches to reducing unnecessary depopulation, such as risk-based depopulation, are highly demanded. This paper proposes a data-driven method to generate a rule table and risk score for each farm to identify preventive measures against HPAI. To evaluate the proposed method, 105 cases of HPAI occurring in a total of 381 farms in Jeollanam-do from 2014 to 2023 were evaluated. The accuracy of preventive measure identification was assessed for each case using both the conventional culling method and the proposed data-driven method. The evaluation showed that the proposed method achieved an accuracy of 84.19%, significantly surpassing the previous 10.37%. The result was attributed to the proposed method reducing the false-positive rate by 83.61% compared with the conventional method, thereby enhancing the reliability of identification. The proposed method is expected to be utilized in selecting farms for monitoring and management of HPAI.
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معلومات مُعتمدة: no.322002-02 Ministry of Agriculture, Food and Rural Affairs (MAFRA)
فهرسة مساهمة: Keywords: biosecurity zones; highly pathogenic avian influenza (HPAI); machine learning; pre-emptive depopulation; risk analysis
تواريخ الأحداث: Date Created: 20231209 Latest Revision: 20231209
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC10705361
DOI: 10.3390/ani13233728
PMID: 38067079
قاعدة البيانات: MEDLINE
الوصف
تدمد:2076-2615
DOI:10.3390/ani13233728