Osong Public Health and Research Perspectives

Original Articles

Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm: Hachung Yoon, Ah-Reum Jang, Chungsik Jung, Hunseok Ko, Kwang-Nyeong Lee, Eunesub Lee; Osong Public Health Res Perspect. 2020;11(4):239-244. Published online August 31, 2020; DOI: https://doi.org/10.24171/j.phrp.2020.11.4.13

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Objectives

This 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).

Methods

Our 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.

Results

After 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 (p < 0.001).

Conclusion

The 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.

Citations

Citations to this article as recorded by

Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea
Hachung Yoon, Ilseob Lee, Hyeonjeong Kang, Kyung-Sook Kim, Eunesub Lee, Mathilde Richard
PLOS ONE.2022; 17(6): e0269311. CrossRef
Artificial Intelligence Models for Zoonotic Pathogens: A Survey
Nisha Pillai, Mahalingam Ramkumar, Bindu Nanduri
Microorganisms.2022; 10(10): 1911. CrossRef

KCDC Risk Assessments on the Initial Phase of the COVID-19 Outbreak in Korea: Inho Kim, Jia Lee, Jihee Lee, Eensuk Shin, Chaeshin Chu, Seon Kui Lee; Osong Public Health Res Perspect. 2020;11(2):67-73. Published online April 30, 2020; DOI: https://doi.org/10.24171/j.phrp.2020.11.2.02

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622 Download
17 Web of Science
17 Crossref

Abstract PDF

Objectives

This study aims to evaluate the risk assessments of coronavirus 2019 (COVID-19) in the Korea Centers for Disease Control and Prevention (KCDC), from the point of detection to the provision of basic information to the relevant public health authorities.

Methods

To estimate the overall risk of specific public health events, probability, and impact at the country-level were evaluated using available information. To determine the probability of particular public health events, the risk of importation and risk of transmission were taken into consideration. KCDC used 5 levels (“very low,” “low,” “moderate,” “high,” and “very high”) for each category and overall risk was eventually decided.

Results

A total of 8 risk assessments were performed on 8 separate occasions between January 8^th to February 28^th, 2020, depending on the detection and report of COVID-19 cases in other countries. The overall risk of the situation in each assessment increased in severity over this period: “low” (first), “moderate” (second), “high” (third), “high” (fourth), “high” (fifth), “high” (sixth), “high” (seventh), and “very high” (eighth).

Conclusion

The KCDC’s 8 risk assessments were utilized to activate national emergency response mechanisms and eventually prepare for the pandemic to ensure the containment and mitigation of COVID-19 with non-pharmaceutical public health measures.

Citations

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Yeonju Kim, Sung-Chan Yang, Jinhwa Jang, Shin Young Park, Seong Sun Kim, Chansoo Kim, Donghyok Kwon, Sang-Won Lee
Tropical Medicine and Infectious Disease.2023; 8(6): 308. CrossRef
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The Occurrence and Risk Assessment of Exposure to Aflatoxin M₁ in Ultra-High Temperature and Pasteurized Milk in Hamadan Province of Iran: Amir Sasan Mozaffari Nejad, Ali Heshmati, Tayebe Ghiasvand; Osong Public Health Res Perspect. 2019;10(4):228-233. Published online August 31, 2019; DOI: https://doi.org/10.24171/j.phrp.2019.10.4.05

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151 Download
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Abstract PDF

Objectives

Aflatoxins are a category of poisonous compounds found in most plants, milk and dairy products. The present research was carried out to detect the presence of aflatoxin M₁ (AFM₁) in samples of milk collected from Hamadan province, Iran.

Methods

Twenty five samples of ultra-high temperature (UHT) and 63 samples of pasteurized milk were collected and the amount of AFM₁ was measured by an Enzyme-Linked Immunosorbent Assay method. In addition, the estimated daily intake (EDI) and hazard index (HI) of AFM₁ was determined by the following equations:(EDI= mean concentration of AFM₁ × daily consumption of milk/body weight; HI= EDI/Tolerance Daily Intake).

Results

AFM₁ was detected in 21 (84%) UHT milk samples and in 55 (87.30%) pasteurized milk samples. Seven (28%) samples of UHT and 21 (33.33%) pasteurized milk samples had higher AFM₁ content than the limit allowed in the European Union and Iranian National Standard Limits (0.05 μg/kg). None of the samples exceeded the US Food and Drug Administration limit (0.5 μg/kg) for AFM₁. EDI and HI for AM₁ through milk were 0.107 ng/kg body weight/day, and 0.535, respectively.

Conclusion

A significant percentage of milk produced by different factories in Iran (84% of UHT and 87.3% of pasteurized milk) was contaminated with AFM₁. Therefore, more control and monitoring of livestock feeding in dairy companies may help reduce milk contamination with AFM₁. As the HI value was lower than 1, it can be assumed that there was no risk of developing liver cancer due to milk consumption.

Citations

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Seasonal variation and risk assessment of exposure to aflatoxin M1 in milk, yoghurt, and cheese samples from Ilam and Lorestan Provinces of Iran
Kousar Aghebatbinyeganeh, Mohammadhosein Movassaghghazani, Mohamed Fathi Abdallah
Journal of Food Composition and Analysis.2024; 128: 106083. CrossRef
Review, meta-analysis and carcinogenic risk assessment of aflatoxin M1 in different types of milks in Iran
Fatemeh Mortezazadeh, Fathollah Gholami-Borujeni
Reviews on Environmental Health.2023; 38(3): 511. CrossRef
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Heliyon.2023; 9(1): e12835. CrossRef
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