Risk Prediction of Three Different Subtypes of Highly Pathogenic Avian Influenza Outbreaks in Poultry Farms: Based on Spatial Characteristics of Infected Premises in South Korea

Abstract

From 2003 to 2017, highly pathogenic avian influenza (HPAI) epidemics, particularly H5N1, H5N8, and H5N6 infections in poultry farms, increased in South Korea. More recently, these subtypes of HPAI virus resurged and spread nationwide, heavily impacting the entire poultry production and supply system. Most outbreaks in poultry holdings were concentrated in the southwestern part of the country, accounting for 58.3% of the total occurrences. This geographically persistent occurrence demanded the investigation of spatial risk factors related to the HPAI outbreak and the prediction of the risk of emerging HPAI outbreaks. Therefore, we investigated 12 spatial variables for the three subtypes of HPAI virus-infected premises [(IPs), 88 H5N1, 339 H5N8, and 335 H5N6 IPs]. Then, two prediction models using statistical and machine learning algorithm approaches were built from a case-control study on HPAI H5N8 epidemic, the most prolonged outbreak, in 339 IPs and 626 non-IPs. Finally, we predicted the risk of HPAI H5N1 and H5N6 occurrence at poultry farms using a Bayesian logistic regression and machine learning algorithm model [extreme gradient boosting (XGBoost) model] built on the case-control study. Several spatial variables showed similar distribution between two subtypes of IPs, although there were distinct heterogeneous distributions of spatial variables among the three IP subtypes. The case-control study indicated that the density of domestic duck farms and the minimum distance to live bird markets were leading risk factors for HPAI outbreaks. The two prediction models showed high predictive performance for H5N1 and H5N6 occurrences [an area under the curve (AUC) of receiver operating characteristic of Bayesian model > 0.82 and XGBoost model > 0.97]. This finding emphasizes that spatial characteristics of the poultry farm play a vital role in the occurrence and forecast of HPAI outbreaks. Therefore, this finding is expected to contributing to developing prevention and control strategies.

Evolution and Spread of Highly Pathogenic Avian Influenza A(H5N1) Clade 2.3.4.4b Virus in Wild Birds, South Korea, 2022–2023

“…Monitoring the geographic distribution of HPAI viruses in wild birds and locations of poultry farms is vital for assessing the risk for virus spillover. A previous study analyzed the geographic distribution of HPAI H5N1 outbreaks in poultry farms in South Korea during 2013–2017 and found high number of cases in southern GG, northern CN, and JL ( 24 ), which were also high-density areas of HPAI detections in wild birds during 2022–2023. In particular, JL is the region with the highest poultry farm density in South Korea ( 25 ).…”

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

“…The final structure consists of a total of 7 major categories and 13 sub-categories. Firstly, Table 1 is composed of terrain items, including sub-categories of mountain ranges [12] and proportion of river size [13]. Table 2 addresses the status around the farm, with subcategories including proximity to roads [14], population density [13], farm density [14], farmland ratio [12], and proximity to traditional markets.…”

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

“…Yoo et al, 2022 [ 45 ] used a Bayesian logistic regression and an extreme gradient boosting model to predict the risk of Avian influenza virus occurrence at poultry farms using 12 spatial variables. According to their study, domestic duck farms and the minimum distance to live bird markets were the leading risk factors for outbreaks.…”