دورية أكاديمية
Identification of Pre-Emptive Biosecurity Zone Areas for Highly Pathogenic Avian Influenza Based on Machine Learning-Driven Risk Analysis.
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. |
References: | Pathogens. 2021 Jun 02;10(6):. (PMID: 34199439) Vet Res. 2011 Jun 29;42:81. (PMID: 21714865) Transbound Emerg Dis. 2022 Sep;69(5):2667-2681. (PMID: 34902223) IEEE Trans Syst Man Cybern B Cybern. 2009 Feb;39(1):281-8. (PMID: 19068445) PLoS One. 2018 Jan 31;13(1):e0190824. (PMID: 29385158) J Vet Sci. 2022 May;23(3):e36. (PMID: 35618317) Front Vet Sci. 2022 May 31;9:897763. (PMID: 35711796) J Microbiol. 2015 Jul;53(7):475-80. (PMID: 26115997) PLoS One. 2022 Jun 7;17(6):e0269311. (PMID: 35671297) Transbound Emerg Dis. 2022 Sep;69(5):e3128-e3140. (PMID: 35894239) Sci Rep. 2020 Nov 4;10(1):19011. (PMID: 33149144) Nature. 2023 Oct;622(7984):810-817. (PMID: 37853121) Sci Rep. 2021 Dec 17;11(1):24163. (PMID: 34921165) Nat Sustain. 2019 Sep 17;2(9):834-840. (PMID: 31535037) PLoS One. 2017 Jun 2;12(6):e0177678. (PMID: 28574989) |
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 |
تدمد: | 2076-2615 |
---|---|
DOI: | 10.3390/ani13233728 |