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Performance of indicators used in regular risk assessments for COVID-19 in association with contextual factors
Sujin Hong, Jiyoung Oh, Jia Lee, Yongmoon Kim, Bryan Inho Kim, Min Jei Lee, Hyunjung Kim, Sangwoo Tak
Osong Public Health Res Perspect. 2024;15(5):420-428.   Published online October 31, 2024
DOI: https://doi.org/10.24171/j.phrp.2024.0141
  • 120 View
  • 6 Download
Graphical AbstractGraphical Abstract AbstractAbstract PDF
Objectives
This study aimed to summarize the results of coronavirus disease 2019 (COVID-19) risk assessments and to examine the associations between risk levels and various indicators, including COVID-19 incidence, risk perception, community mobility, and government policy.
Methods
The results of the risk assessment and the indicators utilized were summarized. From November 2021 to May 2022, the COVID-19 risk level was evaluated on a weekly basis, and its correlation with these indicators was analyzed. Data were obtained from press releases by the Korea Disease Control and Prevention Agency, regular surveys conducted by Hankook Research, and information available on the Google and Oxford websites.
Results
Weekly risk assessments were conducted for 30 weeks, using different indices depending on the phases. Correlation analysis revealed the strongest positive correlation between risk level and risk perception (r=0.841). The risk level from “1-week lead” demonstrated a strong positive correlation with the time-varying reproduction number (Rt). Similarly, the risk level from “week lagged value” showed a strong positive correlation with the number of severe cases in the hospital.
Conclusion
At the time of risk assessment, the Rt precedes the risk level, while severe cases in hospitals follow. Therefore, the assessed risk level functioned as an early warning system. Risk perception demonstrated the strongest correlation with the risk level, suggesting consistency throughout the assessment period. Contextual indicators (e.g., risk perception) that consider time lags and implementation scales, could improve the evaluation of future risk assessment results, particularly when there are challenges in reflecting specific situations in coordinated emergency response.
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
  • 6,855 View
  • 95 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary Material
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  
  • 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
    Emmanuel Musa, Zahra Movahhedi Nia, Nicola Luigi Bragazzi, Doris Leung, Nelson Lee, Jude Dzevela Kong
    Healthcare.2024; 12(19): 1959.     CrossRef
  • 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
  • 13,879 View
  • 635 Download
  • 18 Web of Science
  • 17 Crossref
AbstractAbstract 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 8th to February 28th, 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

Citations to this article as recorded by  
  • COVID-19 Pandemic Risk Assessment: Systematic Review
    Amanda Chu, Patrick Kwok, Jacky Chan, Mike So
    Risk Management and Healthcare Policy.2024; Volume 17: 903.     CrossRef
  • COVID-19 Cases and Deaths among Healthcare Personnel with the Progression of the Pandemic in Korea from March 2020 to February 2022
    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
  • A resposta da Coreia do Sul à pandemia de COVID-19: lições aprendidas e recomendações a gestores
    Thais Regis Aranha Rossi, Catharina Leite Matos Soares, Gerluce Alves Silva, Jairnilson Silva Paim, Lígia Maria Vieira-da-Silva
    Cadernos de Saúde Pública.2022;[Epub]     CrossRef
  • Nursing Experience of New Nurses Caring for COVID-19 Patients in Military Hospitals: A Qualitative Study
    Young-Hoon Kwon, Hye-Ju Han, Eunyoung Park
    Healthcare.2022; 10(4): 744.     CrossRef
  • South Korea’s fast response to coronavirus disease: implications on public policy and public management theory
    Pan Suk Kim
    Public Management Review.2021; 23(12): 1736.     CrossRef
  • Detection of SARS-CoV-2 in Fecal Samples From Patients With Asymptomatic and Mild COVID-19 in Korea
    Soo-kyung Park, Chil-Woo Lee, Dong-Il Park, Hee-Yeon Woo, Hae Suk Cheong, Ho Cheol Shin, Kwangsung Ahn, Min-Jung Kwon, Eun-Jeong Joo
    Clinical Gastroenterology and Hepatology.2021; 19(7): 1387.     CrossRef
  • Systematic assessment of South Korea’s capabilities to control COVID-19
    Katelyn J. Yoo, Soonman Kwon, Yoonjung Choi, David M. Bishai
    Health Policy.2021; 125(5): 568.     CrossRef
  • Environmental risk assessment and comprehensive index model of disaster loss for COVID-19 transmission
    Sulin Pang, Xiaofeng Hu, Zhiming Wen
    Environmental Technology & Innovation.2021; 23: 101597.     CrossRef
  • Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea
    Sukhyun Ryu, Sheikh Taslim Ali, Eunbi Noh, Dasom Kim, Eric H. Y. Lau, Benjamin J. Cowling
    BMC Infectious Diseases.2021;[Epub]     CrossRef
  • Identifying and Prioritizing Ways to Improve Oman’s Tourism Sector in the Corona Period
    Zakiya Salim Al-Hasni
    Journal of Intercultural Management.2021; 13(1): 144.     CrossRef
  • Decreased Use of Broad-Spectrum Antibiotics During the Coronavirus Disease 2019 Epidemic in South Korea
    Sukhyun Ryu, Youngsik Hwang, Sheikh Taslim Ali, Dong-Sook Kim, Eili Y Klein, Eric H Y Lau, Benjamin J Cowling
    The Journal of Infectious Diseases.2021; 224(6): 949.     CrossRef
  • COVID-19 and Cancer Therapy: Interrelationships and Management of Cancer Cases in the Era of COVID-19
    Simon N. Mbugua, Lydia W. Njenga, Ruth A. Odhiambo, Shem O. Wandiga, Martin O. Onani, Nenad Ignjatovic
    Journal of Chemistry.2021; 2021: 1.     CrossRef
  • Challenges to manage pandemic of coronavirus disease (COVID-19) in Iran with a special situation: a qualitative multi-method study
    Hamidreza Khankeh, Mehrdad Farrokhi, Juliet Roudini, Negar Pourvakhshoori, Shokoufeh Ahmadi, Masoumeh Abbasabadi-Arab, Nader Majidi Bajerge, Babak Farzinnia, Pirhossain Kolivand, Vahid Delshad, Mohammad Saeed Khanjani, Sadegh Ahmadi-Mazhin, Ali Sadeghi-Mo
    BMC Public Health.2021;[Epub]     CrossRef
  • Effect of Nonpharmaceutical Interventions on Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, South Korea, 2020
    Sukhyun Ryu, Seikh Taslim Ali, Cheolsun Jang, Baekjin Kim, Benjamin J. Cowling
    Emerging Infectious Diseases.2020; 26(10): 2406.     CrossRef
  • Early Trend of Imported COVID-19 Cases in South Korea

    Osong Public Health and Research Perspectives.2020; 11(3): 140.     CrossRef
  • Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: a Nationwide Case-Control Study
    Wonjun Ji, Kyungmin Huh, Minsun Kang, Jinwook Hong, Gi Hwan Bae, Rugyeom Lee, Yewon Na, Hyoseon Choi, Seon Yeong Gong, Yoon-Hyeong Choi, Kwang-Pil Ko, Jeong-Soo Im, Jaehun Jung
    Journal of Korean Medical Science.2020;[Epub]     CrossRef
  • Innovative countermeasures can maintain cancer care continuity during the coronavirus disease-2019 pandemic in Korea
    Soohyeon Lee, Ah-reum Lim, Min Ja Kim, Yoon Ji Choi, Ju Won Kim, Kyong Hwa Park, Sang Won Shin, Yeul Hong Kim
    European Journal of Cancer.2020; 136: 69.     CrossRef
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
  • 7,723 View
  • 164 Download
  • 26 Crossref
AbstractAbstract 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

Citations to this article as recorded by  
  • 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
  • Adıyaman İlinde Satışa Sunulan Çiğ Sütlerde Aflatoksin M1 Varlığının Araştırılması ve Potansiyel Risk Değerlendirmesi
    Sinan Çilenti, Zozan Garip, Füsun Temamoğulları
    Etlik Veteriner Mikrobiyoloji Dergisi.2024; 35(1): 70.     CrossRef
  • An overview of regional mycotoxin contamination in Iranian food
    Kousar Aghebatbinyeganeh, Mohamed F. Abdallah
    Food and Humanity.2024; 3: 100370.     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
  • Molecular identification and biocontrol of ochratoxigenic fungi and ochratoxin A in animal feed marketed in the state of Qatar
    Fatma Ali Alsalabi, Zahoor Ul Hassan, Roda F. Al-Thani, Samir Jaoua
    Heliyon.2023; 9(1): e12835.     CrossRef
  • Risk assessments for the dietary intake aflatoxins in food: A systematic review (2016–2022)
    Kiran Bhardwaj, Julie P. Meneely, Simon A. Haughey, Moira Dean, Patrick Wall, Guangtao Zhang, Bob Baker, Christopher T. Elliott
    Food Control.2023; 149: 109687.     CrossRef
  • A systematic literature review for aflatoxin M1 of various milk types in Iran: Human health risk assessment, uncertainty, and sensitivity analysis
    Tooraj Massahi, Amir Kiani, Kiomars Sharafi, Behzad Karami Matin, Abdullah Khalid Omer, Gholamreza Ebrahimzadeh, Jalil Jaafari, Nazir Fattahi
    Food Control.2023; 150: 109733.     CrossRef
  • The occurrence of aflatoxin M1 in milk samples of Iran: a systematic review and meta-analysis
    Neda Mollakhalili-Meybodi, Amene Nematollahi
    Environmental Monitoring and Assessment.2023;[Epub]     CrossRef
  • Effect of basil seed and xanthan gum on physicochemical, textural, and sensory characteristics of low‐fat cream cheese
    Jalal Portaghi, Ali Heshmati, Mehdi Taheri, Ebrahim Ahmadi, Amin Mousavi Khaneghah
    Food Science & Nutrition.2023; 11(10): 6060.     CrossRef
  • Evaluation of aflatoxin M1 content in milk and dairy products by high-performance liquid chromatography in Tehran, Iran
    Nazanin SHABANSALMANİ, Mohammadhosein MOVASSAGHGHAZANİ
    Harran Tarım ve Gıda Bilimleri Dergisi.2023; 27(3): 435.     CrossRef
  • Seasonal Study of Aflatoxin M1 Contamination in Cow Milk on the Retail Dairy Market in Gorgan, Iran
    Hadi Rahimzadeh Barzoki, Hossein Faraji, Somayeh Beirami, Fatemeh Zahra Keramati, Gulzar Ahmad Nayik, Zahra Izadi Yazdanaabadi, Amir Sasan Mozaffari Nejad
    Dairy.2023; 4(4): 571.     CrossRef
  • Aflatoxin M1 in milk and dairy products: global occurrence and potential decontamination strategies
    Khurram Muaz, Muhammad Riaz, Carlos Augusto Fernandes de Oliveira, Saeed Akhtar, Shinawar Waseem Ali, Habibullah Nadeem, Sungkwon Park, Balamuralikrishnan Balasubramanian
    Toxin Reviews.2022; 41(2): 588.     CrossRef
  • Feed to fork risk assessment of mycotoxins under climate change influences - recent developments
    Rhea Sanjiv Chhaya, John O'Brien, Enda Cummins
    Trends in Food Science & Technology.2022; 126: 126.     CrossRef
  • The behavior of aflatoxin M1 during lactic cheese production and storage
    Mahtab Einolghozati, Ali Heshmati, Freshteh Mehri
    Toxin Reviews.2022; 41(4): 1163.     CrossRef
  • Exposure assessment on aflatoxin M1 from milk and dairy products-relation to public health
    Eleni Malissiova, Georgia Soultani, Konstantina Tsokana, Mary Alexandraki, Athanasios Manouras
    Clinical Nutrition ESPEN.2022; 47: 189.     CrossRef
  • Aflatoxin M1 in distributed milks in northwestern Iran: occurrence, seasonal variation, and risk assessment
    Seyyed Ahmad Mokhtari, Ali Nemati, Mehdi Fazlzadeh, Eslam Moradi-Asl, Vahid Taefi Ardabili, Anoshirvan Seddigh
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  • Brucellosis in Humans with the Approach of Brucella Species Contamination in Unpasteurized Milk and Dairy Products from Hamadan, Iran
    Mohammad Mahdi Majzobi, Pejman Karami, Amir Khodavirdipour, Mohammad Yousef Alikhani
    Iranian Journal of Medical Microbiology.2022; 16(4): 282.     CrossRef
  • Probabilistic modeling and risk characterization of the chronic aflatoxin M1 exposure of Hungarian consumers
    Zsuzsa Farkas, Kata Kerekes, Árpád Ambrus, Miklós Süth, Ferenc Peles, Tünde Pusztahelyi, István Pócsi, Attila Nagy, Péter Sipos, Gabriella Miklós, Anna Lőrincz, Szilveszter Csorba, Ákos Bernard Jóźwiak
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • The occurrence of aflatoxin M1 in doogh, kefir, and kashk in Hamadan, Iran
    Mina KHORSHIDI, Ali HESHMATI, Zahra HADIAN, Slim SMAOUI, Amin MOUSAVI KHANEGHAH
    Food Science and Technology.2022;[Epub]     CrossRef
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    Xiaofeng Yue, Xianfeng Ren, Jiayun Fu, Na Wei, Claudio Altomare, Miriam Haidukowski, Antonio F. Logrieco, Qi Zhang, Peiwu Li
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • Simultaneous multi-determination of pesticide residues in black tea leaves and infusion: a risk assessment study
    Ali Heshmati, Fereshteh Mehri, Amin Mousavi Khaneghah
    Environmental Science and Pollution Research.2021; 28(11): 13725.     CrossRef
  • Development of a specific anti-idiotypic nanobody for monitoring aflatoxin M1 in milk and dairy products
    Chong Cai, Qi Zhang, Seyni Nidiaye, Honglin Yan, Wen Zhang, Xiaoqian Tang, Peiwu Li
    Microchemical Journal.2021; 167: 106326.     CrossRef
  • Prevalence of aflatoxin M1 in pasteurized and ultra-high temperature (UHT) milk marketed in Dar es Salaam, Tanzania
    F. Mwakosya Hilda, K. Mugula Jovin
    African Journal of Microbiology Research.2021; 15(9): 461.     CrossRef
  • Multi-mycotoxin occurrence in feed, metabolism and carry-over to animal-derived food products: A review
    J. Tolosa, Y. Rodríguez-Carrasco, M.J. Ruiz, P. Vila-Donat
    Food and Chemical Toxicology.2021; 158: 112661.     CrossRef
  • Presence of Aflatoxin M1 in Commercial Milk in Paraguay
    Andrea Alejandra Arrúa, Pablo David Arrúa, Juliana Moura-Mendes, Cinthia Cazal, Francisco Paulo Ferreira, Cristhian Javier Grabowski, Horacio Daniel Lopez-Nicora, Danilo Fernández Rios
    Journal of Food Protection.2021; 84(12): 2128.     CrossRef
  • The Occurrence and Risk Assessment of Aflatoxin M1 in Yoghurt Samples from Hamadan, Iran
    Ali Heshmati, Amir Sasan Mozaffari Mozaffari Nejad, Tayebeh Ghyasvand
    The Open Public Health Journal.2020; 13(1): 512.     CrossRef

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