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

Authors :
Kwang-Myung Jeon
Jinwoo Jung
Chang-Min Lee
Dae-Sung Yoo
Source :
Animals, Vol 13, Iss 23, p 3728 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
20762615
Volume :
13
Issue :
23
Database :
Directory of Open Access Journals
Journal :
Animals
Publication Type :
Academic Journal
Accession number :
edsdoj.0c233af15da54f5fb8bf11cbbceac51a
Document Type :
article
Full Text :
https://doi.org/10.3390/ani13233728