Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea

Abstract

Outbreaks of H5-type highly pathogenic avian influenza (HPAI) in poultry have been reported in various parts of the world. To respond to these continuous threats, numerous surveillance programs have been applied to poultry raising facilities as well as wild birds. In Korea, a surveillance program was developed aimed at providing a preemptive response to possible outbreaks at poultry farms. The purpose of this study is to comprehensively present the risks of HPAI evaluated by this program in relation to actual outbreak farms during the epidemic of 2020/2021. A deep learning-based risk assessment program was trained based on the pattern of livestock vehicles visiting poultry farms and HPAI outbreaks to calculate the risk of HPAI for farms linked by the movement of livestock vehicles (such farms are termed “epidemiologically linked farms”). A total of 7,984 risk assessments were conducted, and the results were categorized into four groups. The proportion of the highest risk level was greater in duck farms (13.6%) than in chicken farms (8.8%). Among the duck farms, the proportion of the highest risk level was much greater in farms where breeder ducks were raised (accounting for 26.4% of the risk) than in farms where ducks were raised to obtain meat (12.8% of the risk). A higher risk level was also found in cases where the species of the outbreak farm and epidemiologically linked farms were the same (proportion of the highest risk level = 13.2%) compared to that when the species between the two farms were different (7.9%). The overall proportion of farms with HPAI outbreaks among epidemiologically linked farms (attack rate, AR) was 1.7% as HPAI was confirmed on 67 of the 3,883 epidemiologically linked farms. The AR was highest for breeder ducks (15.3%) among duck farms and laying hens (4.8%) among chicken farms. The AR of the pairs where livestock vehicles entered the inner farm area was 1.3 times (95% confidence interval: 1.4–2.9) higher than that of all pairs. With the risk information provided, customized preventive measures can be implemented for each epidemiologically linked farm. The use of this risk assessment program would be a good example of information-based surveillance and support decision-making for controlling animal diseases.

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

“…The high rate of culling and rapid spread of the disease indicates that there is a significant need for improvement in current disease response strategies [7]. Therefore, more effective and efficient response measures are urgently needed to mitigate the impact of this spread [8,9]. To this end, Korean health authorities are considering options including risk-based prevention and response systems in collaboration with industry, universities, and research institutes, as well as artificial intelligence (AI)-based risk prediction systems.…”