ArticleOsong public health and research perspectives2020
Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm.
Article in Osong public health and research perspectives, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Quantitative risk assessment of avian influenza: A scoping review.Infectious Disease Modelling · 2026Review
- A risk prediction model for HPAI outbreaks in Kuwait.Frontiers in veterinary science · 2026Article
- A systematic review of mathematical and machine learning models of Avian Influenza.One health (Amsterdam, Netherlands) · 2025Review
- Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks.Healthcare (Basel, Switzerland) · 2024Review
- Artificial Intelligence Models for Zoonotic Pathogens: A Survey.Microorganisms · 2022Review
- Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea.PloS one · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).
methodsOur risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.
resultsAfter 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the "outbreak" (average ± SD, 0.20 ± 0.31) than "non-outbreak" (0.18 ± 0.30) farms (
conclusionThe risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.
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