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Eunok Jung 5 Articles
Korea Seroprevalence Study of Monitoring of SARS-COV-2 Antibody Retention and Transmission (K-SEROSMART): findings from national representative sample
Jina Han, Hye Jin Baek, Eunbi Noh, Kyuhyun Yoon, Jung Ae Kim, Sukhyun Ryu, Kay O Lee, No Yai Park, Eunok Jung, Sangil Kim, Hyukmin Lee, Yoo-Sung Hwang, Jaehun Jung, Hun Jae Lee, Sung-il Cho, Sangcheol Oh, Migyeong Kim, Chang-Mo Oh, Byengchul Yu, Young-Seoub Hong, Keonyeop Kim, Sun Jae Jung, Mi Ah Han, Moo-Sik Lee, Jung-Jeung Lee, Young Hwangbo, Hyeon Woo Yim, Yu-Mi Kim, Joongyub Lee, Weon-Young Lee, Jae-Hyun Park, Sungsoo Oh, Heui Sug Jo, Hyeongsu Kim, Gilwon Kang, Hae-Sung Nam, Ju-Hyung Lee, Gyung-Jae Oh, Min-Ho Shin, Soyeon Ryu, Tae-Yoon Hwang, Soon-Woo Park, Sang Kyu Kim, Roma Seol, Ki-Soo Park, Su Young Kim, Jun-wook Kwon, Sung Soon Kim, Byoungguk Kim, June-Woo Lee, Eun Young Jang, Ah-Ra Kim, Jeonghyun Nam, The Korea Community Health Survey Group, Soon Young Lee, Dong-Hyun Kim
Epidemiol Health. 2023;45:e2023075.   Published online August 17, 2023
DOI: https://doi.org/10.4178/epih.e2023075
  • 8,115 View
  • 222 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
We estimated the population prevalence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including unreported infections, through a Korea Seroprevalence Study of Monitoring of SARS-CoV-2 Antibody Retention and Transmission (K-SEROSMART) in 258 communities throughout Korea.
METHODS
In August 2022, a survey was conducted among 10,000 household members aged 5 years and older, in households selected through two stage probability random sampling. During face-to-face household interviews, participants self-reported their health status, COVID-19 diagnosis and vaccination history, and general characteristics. Subsequently, participants visited a community health center or medical clinic for blood sampling. Blood samples were analyzed for the presence of antibodies to spike proteins (anti-S) and antibodies to nucleocapsid proteins (anti-N) SARS-CoV-2 proteins using an electrochemiluminescence immunoassay. To estimate the population prevalence, the PROC SURVEYMEANS statistical procedure was employed, with weighting to reflect demographic data from July 2022.
RESULTS
In total, 9,945 individuals from 5,041 households were surveyed across 258 communities, representing all basic local governments in Korea. The overall population-adjusted prevalence rates of anti-S and anti-N were 97.6% and 57.1%, respectively. Since the Korea Disease Control and Prevention Agency has reported a cumulative incidence of confirmed cases of 37.8% through July 31, 2022, the proportion of unreported infections among all COVID-19 infection was suggested to be 33.9%.
CONCLUSIONS
The K-SEROSMART represents the first nationwide, community-based seroepidemiologic survey of COVID-19, confirming that most individuals possess antibodies to SARS-CoV-2 and that a significant number of unreported cases existed. Furthermore, this study lays the foundation for a surveillance system to continuously monitor transmission at the community level and the response to COVID-19.
Summary
Korean summary
인구집단을 기반으로 하여 대표성 있는 표본을 추출하여 COVID-19 항체유병률 조사를 전국적으로 수행함으로 지역사회 단위에서 지속적으로 모니터링할 수 있는 COVID-19 감시체계 구축의 기반을 마련하였다. 2022년 8월 우리나라 국민의 대부분이 COVID-19에 대한 항체를 보유하고 있었고 인구 3명 중 1명은 미확진 감염자로 추정되었다.
Key Message
The K-SEROSMART represents the first nationwide, community-based seroepidemiologic survey of COVID-19. In August 2022, most of the Korean people had antibodies to COVID-19, and one in three people was estimated to have an unreported infection. This study lays the foundation for a surveillance system to continuously monitor transmission at the community level and the response to COVID-19.

Citations

Citations to this article as recorded by  
  • Infection-mediated immune response in SARS-CoV-2 breakthrough infection and implications for next-generation COVID-19 vaccine development
    Sho Miyamoto, Tadaki Suzuki
    Vaccine.2024; 42(6): 1401.     CrossRef
  • Changes in the intrinsic severity of severe acute respiratory syndrome coronavirus 2 according to the emerging variant: a nationwide study from February 2020 to June 2022, including comparison with vaccinated populations
    Boyeong Ryu, Eunjeong Shin, Dong Hwi Kim, HyunJu Lee, So Young Choi, Seong-Sun Kim, Il-Hwan Kim, Eun-Jin Kim, Sangwon Lee, Jaehyun Jeon, Donghyok Kwon, Sungil Cho
    BMC Infectious Diseases.2024;[Epub]     CrossRef
  • Seroprevalence of SARS-CoV-2 infection in pediatric patients in a tertiary care hospital setting
    Ploy Pattanakitsakul, Chanya Pongpatipat, Chavachol Setthaudom, Mongkol Kunakorn, Thiantip Sahakijpicharn, Anannit Visudtibhan, Nopporn Apiwattanakul, Surapat Assawawiroonhakarn, Uthen Pandee, Chonnamet Techasaensiri, Sophida Boonsathorn, Sujittra Chaisav
    PLOS ONE.2024; 19(9): e0310860.     CrossRef
  • Realistic Estimation of COVID-19 Infection by Seroprevalence Surveillance of SARS-CoV-2 Antibodies: An Experience From Korea Metropolitan Area From January to May 2022
    In Hwa Jeong, Jong-Hun Kim, Min-Jung Kwon, Jayoung Kim, Hee Jin Huh, Byoungguk Kim, Junewoo Lee, Jeong-hyun Nam, Eun-Suk Kang
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
Effective vaccination strategies to control COVID-19 in Korea: a modeling study
Youngsuk Ko, Kyong Ran Peck, Yae-Jean Kim, Dong-Hyun Kim, Eunok Jung
Epidemiol Health. 2023;45:e2023084.   Published online September 7, 2023
DOI: https://doi.org/10.4178/epih.e2023084
  • 7,095 View
  • 141 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
In Korea, as immunity levels of the coronavirus disease 2019 (COVID-19) in the population acquired through previous infections and vaccinations have decreased, booster vaccinations have emerged as a necessary measure to control new outbreaks. The objective of this study was to identify the most suitable vaccination strategy for controlling the surge in COVID-19 cases.
METHODS
A mathematical model was developed to concurrently evaluate the immunity levels induced by vaccines and infections. This model was then employed to investigate the potential for future resurgence and the possibility of control through the use of vaccines and antivirals.
RESULTS
As of May 11, 2023, if the current epidemic trend persists without further vaccination efforts, a peak in resurgence is anticipated to occur around mid-October of the same year. Under the most favorable circumstances, the peak number of severely hospitalized patients could be reduced by 43% (n=480) compared to the scenario without vaccine intervention (n=849). Depending on outbreak trends and vaccination strategies, the best timing for vaccination in terms of minimizing this peak varies from May 2023 to August 2023.
CONCLUSIONS
Our findings suggest that if the epidemic persist, the best timing for administering vaccinations would need to be earlier than currently outlined in the Korean plan. It is imperative to continue monitoring outbreak trends, as this is key to determining the best vaccination timing in order to manage potential future surges.
Summary
Korean summary
본 연구는 자연감염 혹은 백신으로 획득된 면역의 저하를 고려한 수리모델을 사용하여 COVID-19에 대한 백신 접종 전략 분석 결과를 보인다. 시뮬레이션 결과는 추가 백신 접종이 없을 경우 재유행의 정점이 800명을 넘을 것임을 나타내며, 적절한 시기에 백신을 접종하면 최대 재원 위중증환자수를 약 40%까지 줄일 수 있음을 보인다. 본 연구는 확진자 추세의 지속적인 모니터링이 백신 접종의 적정 시기를 결정하고 미래 COVID-19의 재유행을 효과적으로 관리하는 데 필요하다는 점을 강조한다.
Key Message
Our study analyzes strategies for COVID-19 through vaccination, using a mathematical model considering waning immunity from past infections and vaccinations. Results indicate that a resurgence peak would reach more than 800 without further vaccination, and suggest vaccination in proper timing can reduce the peak size of administered severe patients by up to approximately 40%. The study emphasizes the importance of ongoing monitoring of outbreak trends to manage vaccination timing and future COVID-19 surges effectively.

Citations

Citations to this article as recorded by  
  • Global stability analysis of an extended SUC epidemic mathematical model
    Mengxin Chen, Soobin Kwak, Seokjun Ham, Youngjin Hwang, Junseok Kim
    Zeitschrift für Naturforschung A.2024;[Epub]     CrossRef
Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study
Youngsuk Ko, Jacob Lee, Yubin Seo, Eunok Jung
Epidemiol Health. 2021;43:e2021059.   Published online September 8, 2021
DOI: https://doi.org/10.4178/epih.e2021059
  • 11,022 View
  • 158 Download
  • 5 Web of Science
  • 7 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
This study aims to analyze the possibility and conditions of maintaining an effective reproductive number below 1 using a mathematical model.
METHODS
The total population was divided into five age groups (0-17, 18-29, 30-59, 60-74, and ≥75 years). Maximum likelihood estimation (MLE) was used to estimate the transmission rate of each age group. Mathematical model simulation was conducted until December 31, 2021, by establishing various strategies for vaccination and social distancing without considering variants.
RESULTS
MLE results revealed that the group aged 0-17 years had a lower risk of transmission than other age groups, and the older age group had relatively high risks of infection. If 70% of the population will be vaccinated by the end of 2021, then simulations showed that even if social distancing was eased, the effective reproductive number would remain below 1 near August if it was not at the level of the third re-spreading period. However, if social distancing was eased and it reached the level of the re-spreading period, the effective reproductive number could be below 1 at the end of 2021.
CONCLUSIONS
Considering both stable and worsened situations, simulation results emphasized that sufficient vaccine supply and control of the epidemic by maintaining social distancing to prevent an outbreak at the level of the re-spreading period are necessary to minimize mortality and maintain the effective reproductive number below 1.
Summary
Korean summary
본 연구에서는 질병관리청에서 제공하는 개별 확진자 데이터에 확률통계적 방법을 적용하여 연령군 간의 감염전파 행렬을 추정하였으며 연령군을 고려한 수리모델에 적용되었다. 본 연구에서 우리는 2020년 10월부터 2021년 5월까지 한국에서의 코로나19 유행상황을 정책 구간에 따라 분석하였으며 이를 토대로 거리두기 완화 수준에 따라 거리두기 완화 상태에서도 지속적으로 유효감염재생산지수가 1보다 작아지는 시점이 달라질 수 있음을 보인다.
Key Message
In this research, we estimated age-group-specified transmission rate matrix by applying maximum likelihood estimation into individual based data which was provided by Korea Disease Control and Prevention Agency. Our model simulation showed the moment, when the effective reproductive number is consistently below 1 even the distancing is eased, is ranged from August to the end of 2021 depending on the intensity of the social distancing during eased phase.

Citations

Citations to this article as recorded by  
  • Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review
    Oscar Espinosa, Laura Mora, Cristian Sanabria, Antonio Ramos, Duván Rincón, Valeria Bejarano, Jhonathan Rodríguez, Nicolás Barrera, Carlos Álvarez-Moreno, Jorge Cortés, Carlos Saavedra, Adriana Robayo, Oscar H. Franco
    Systematic Reviews.2024;[Epub]     CrossRef
  • Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in Seoul, Korea
    Jiwoo Sim, Euncheol Son, Minsu Kwon, Eun Jin Hwang, Young Hwa Lee, Young June Choe
    Infection & Chemotherapy.2024; 56(2): 204.     CrossRef
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia
    Karan Thakkar, Julia Regazzini Spinardi, Jingyan Yang, Moe H. Kyaw, Egemen Ozbilgili, Carlos Fernando Mendoza, Helen May Lin Oh
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • Effective vaccination strategies to control COVID-19 in Korea: a modeling study
    Youngsuk Ko, Kyong Ran Peck, Yae-Jean Kim, Dong-Hyun Kim, Eunok Jung
    Epidemiology and Health.2023; : e2023084.     CrossRef
  • Quantifying the Effects of Non-Pharmaceutical and Pharmaceutical Interventions Against Covid-19 Epidemic in the Republic of Korea: Mathematical Model-Based Approach Considering Age Groups and the Delta Variant
    Youngsuk Ko, Victoria May P. Mendoza, Yubin Seo, Jacob Lee, Yeonju Kim, Donghyok Kwon, Eunok Jung, E. Augeraud, M. Banerjee, J.-S. Dhersin, A. d'Onofrio, T. Lipniacki, S. Petrovskii, Chi Tran, A. Veber-Delattre, E. Vergu, V. Volpert
    Mathematical Modelling of Natural Phenomena.2022; 17: 39.     CrossRef
  • Dietary Behavior and Diet Quality in the Korean Adult Population by Income Level before and after the COVID-19 Pandemic: Using the Korean National Health and Nutrition Examination Survey (2019-2020)
    Hye-Min Na, Bok-Mi Jung
    The Korean Journal of Community Living Science.2022; 33(3): 397.     CrossRef
Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea
Soyoung Kim, Yu Bin Seo, Eunok Jung
Epidemiol Health. 2020;42:e2020026.   Published online April 13, 2020
DOI: https://doi.org/10.4178/epih.e2020026
  • 23,504 View
  • 1,327 Download
  • 51 Web of Science
  • 50 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Since the report of the first confirmed case in Daegu on February 18, 2020, local transmission of coronavirus disease 2019 (COVID-19) in Korea has continued. In this study, we aimed to identify the pattern of local transmission of COVID-19 using mathematical modeling and predict the epidemic size and the timing of the end of the spread.
METHODS
We modeled the COVID-19 outbreak in Korea by applying a mathematical model of transmission that factors in behavioral changes. We used the Korea Centers for Disease Control and Prevention data of daily confirmed cases in the country to estimate the nationwide and Daegu/Gyeongbuk area-specific transmission rates as well as behavioral change parameters using a least-squares method.
RESULTS
The number of transmissions per infected patient was estimated to be about 10 times higher in the Daegu/Gyeongbuk area than the average of nationwide. Using these estimated parameters, our models predicts that about 13,800 cases will occur nationwide and 11,400 cases in the Daegu/Gyeongbuk area until mid-June.
CONCLUSIONS
We mathematically demonstrate that the relatively high per-capita rate of transmission and the low rate of changes in behavior have caused a large-scale transmission of COVID-19 in the Daegu/Gyeongbuk area in Korea. Since the outbreak is expected to continue until May, non-pharmaceutical interventions that can be sustained over the long term are required.
Summary
Korean summary
본 논문은 행동변화를 고려한 수학적 모델을 이용하여 코로나바이러스병-19의 유행 양상을 분석하고 총 환자수와 유행기간을 예측하고자 한다. 질병관리본부 확진자 데이터를 이용하여 전국과 대구·경북 지역의 감염전파율과 행동변화율을 추정하였다. 3월 10일까지의 데이터를 기준으로 전국적으로 6월 중순까지 약 13,000명, 대구·경북지역의 경우 5월 말까지 약 11,000명의 환자가 발생할 것으로 예측된다.

Citations

Citations to this article as recorded by  
  • A framework for incorporating behavioural change into individual‐level spatial epidemic models
    Madeline A. Ward, Rob Deardon, Lorna E. Deeth
    Canadian Journal of Statistics.2024;[Epub]     CrossRef
  • Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data
    Kyulhee Han, Bogyeom Lee, Doeun Lee, Gyujin Heo, Jooha Oh, Seoyoung Lee, Catherine Apio, Taesung Park
    Scientific Reports.2024;[Epub]     CrossRef
  • Mathematical modeling and control of Covid‐19
    Atena Ghasemabadi
    Mathematical Methods in the Applied Sciences.2024; 47(12): 10478.     CrossRef
  • Assessment of age-dependent effects during the transmission of Omicron and the outcomes of booster campaign vaccination strategies
    Yang Deng, Daihai He, Yi Zhao
    Applied Mathematical Modelling.2024; 133: 148.     CrossRef
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
    Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa
    F1000Research.2023; 12: 445.     CrossRef
  • Factors Influencing Infection Anxiety in Korean Male Firefighters Due to COVID-19 Infection Status
    Seung-Woo Han, Hyun-Ok Jung
    Healthcare.2023; 11(11): 1623.     CrossRef
  • Neural network method and multiscale modeling of the COVID-19 epidemic in Korea
    Ziqian Li, Jiwei Jia, Guidong Liao, Young Ju Lee, Siyu Liu
    The European Physical Journal Plus.2023;[Epub]     CrossRef
  • Numerical Scheme for Compartmental Models: New Matlab Software Codes for Numerical Simulation
    Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Samuel Akwasi Adarkwa
    F1000Research.2023; 12: 445.     CrossRef
  • Studying the Effect of Particulate Matter as SARS-CoV-2 Transmitters
    Abdulrahim R. Hakami, Gasim Dobie
    Journal of Public Health Research.2022;[Epub]     CrossRef
  • Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective
    Manimuthu Arunmozhi, Jinil Persis, V. Raja Sreedharan, Ayon Chakraborty, Tarik Zouadi, Hanane Khamlichi
    International Journal of Quality & Reliability Management.2022; 39(9): 2184.     CrossRef
  • Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic
    Ming Guan
    Journal of Epidemiology and Global Health.2022; 12(1): 40.     CrossRef
  • Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics
    Ali AlArjani, Md Taufiq Nasseef, Sanaa M. Kamal, B. V. Subba Rao, Mufti Mahmud, Md Sharif Uddin
    Arabian Journal for Science and Engineering.2022; 47(8): 10163.     CrossRef
  • Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges
    Jinxing Guan, Yang Zhao, Yongyue Wei, Sipeng Shen, Dongfang You, Ruyang Zhang, Theis Lange, Feng Chen
    Medical Review.2022; 2(1): 89.     CrossRef
  • Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    ISA Transactions.2022; 124: 57.     CrossRef
  • Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model
    Hadeel AlQadi, Majid Bani-Yaghoub, Yury E. Khudyakov
    PLOS ONE.2022; 17(4): e0265815.     CrossRef
  • A Mathematical Model of Transmission Dynamics of SARS-CoV-2 (COVID-19) with an Underlying Condition of Diabetes
    Samuel Okyere, Joseph Ackora-Prah, Shih Pin Chen
    International Journal of Mathematics and Mathematical Sciences.2022; 2022: 1.     CrossRef
  • Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore
    Murali Krishna Gurram, Min Xian Wang, Yi-Chen Wang, Junxiong Pang
    Scientific Reports.2022;[Epub]     CrossRef
  • Compartmental structures used in modeling COVID-19: a scoping review
    Lingcai Kong, Mengwei Duan, Jin Shi, Jie Hong, Zhaorui Chang, Zhijie Zhang
    Infectious Diseases of Poverty.2022;[Epub]     CrossRef
  • Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study
    Youngsuk Ko, Victoria May Mendoza, Renier Mendoza, Yubin Seo, Jacob Lee, Jonggul Lee, Donghyok Kwon, Eunok Jung
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
  • Investigating Online Learning Process in Business School: Case Study from Business School in Jakarta, Indonesia
    Sekar W. Prasetyaningtyas, Agustian B. Prasetya
    International Journal of Information and Education Technology.2022; 12(9): 964.     CrossRef
  • Evolution and consequences of individual responses during the COVID-19 outbreak
    Wasim Abbas, Masud M. A., Anna Park, Sajida Parveen, Sangil Kim, Siew Ann Cheong
    PLOS ONE.2022; 17(9): e0273964.     CrossRef
  • Managing bed capacity and timing of interventions: a COVID-19 model considering behavior and underreporting
    Victoria May P. Mendoza, Renier Mendoza, Youngsuk Ko, Jongmin Lee, Eunok Jung
    AIMS Mathematics.2022; 8(1): 2201.     CrossRef
  • Model-informed COVID-19 exit strategy with projections of SARS-CoV-2 infections generated by variants in the Republic of Korea
    Sung-mok Jung, Kyungmin Huh, Munkhzul Radnaabaatar, Jaehun Jung
    BMC Public Health.2022;[Epub]     CrossRef
  • Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism
    Oualid Limam, Mohamed Limam
    Biomechanics and Modeling in Mechanobiology.2021; 20(1): 379.     CrossRef
  • Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    Journal of Control, Automation and Electrical Systems.2021; 32(2): 326.     CrossRef
  • Non-pharmaceutical interventions during the COVID-19 pandemic: A review
    Nicola Perra
    Physics Reports.2021; 913: 1.     CrossRef
  • Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    IEEE Journal of Biomedical and Health Informatics.2021; 25(3): 615.     CrossRef
  • Graphs in the COVID-19 news: a mathematics audit of newspapers in Korea
    Oh Nam Kwon, Chaereen Han, Changsuk Lee, Kyungwon Lee, Kyeongjun Kim, Gyeongha Jo, Gangwon Yoon
    Educational Studies in Mathematics.2021; 108(1-2): 183.     CrossRef
  • Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model
    Gregory L. Watson, Di Xiong, Lu Zhang, Joseph A. Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W. Rimoin, Marc A. Suchard, Christina M. Ramirez, Virginia E. Pitzer
    PLOS Computational Biology.2021; 17(3): e1008837.     CrossRef
  • Using the Weibull distribution to model COVID-19 epidemic data
    Vitor Hugo Moreau
    Model Assisted Statistics and Applications.2021; 16(1): 5.     CrossRef
  • Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown
    Bram A. D. van Bunnik, Alex L. K. Morgan, Paul R. Bessell, Giles Calder-Gerver, Feifei Zhang, Samuel Haynes, Jordan Ashworth, Shengyuan Zhao, Roo Nicola Rose Cave, Meghan R. Perry, Hannah C. Lepper, Lu Lu, Paul Kellam, Aziz Sheikh, Graham F. Medley, Mark
    Philosophical Transactions of the Royal Society B: Biological Sciences.2021;[Epub]     CrossRef
  • A model and predictions for COVID-19 considering population behavior and vaccination
    Thomas Usherwood, Zachary LaJoie, Vikas Srivastava
    Scientific Reports.2021;[Epub]     CrossRef
  • Behavioral Risk Modeling for Pandemics: Overcoming Challenges and Advancing the Science
    Ellen P. Carlin, Koya C. Allen, Jeffrey J. Morgan, Jean-Paul Chretien, Suzan Murray, Deborah Winslow, Dawn Zimmerman
    Health Security.2021; 19(4): 447.     CrossRef
  • Structure of epidemic models: toward further applications in economics
    Toshikazu Kuniya
    The Japanese Economic Review.2021; 72(4): 581.     CrossRef
  • How Important Is Behavioral Change during the Early Stages of the COVID-19 Pandemic? A Mathematical Modeling Study
    Jongmin Lee, Seok-Min Lee, Eunok Jung
    International Journal of Environmental Research and Public Health.2021; 18(18): 9855.     CrossRef
  • Factors shaping the COVID-19 epidemic curve: a multi-country analysis
    Su Yeon Jang, Laith Hussain-Alkhateeb, Tatiana Rivera Ramirez, Ahmed Asa’ad Al-Aghbari, Dhia Joseph Chackalackal, Rocio Cardenas-Sanchez, Maria Angelica Carrillo, In-Hwan Oh, Eduardo Andrés Alfonso-Sierra, Pia Oechsner, Brian Kibiwott Kirui, Martin Anto,
    BMC Infectious Diseases.2021;[Epub]     CrossRef
  • A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries
    Theyazn H. H. Aldhyani, Hasan Alkahtani
    Life.2021; 11(11): 1118.     CrossRef
  • Health and pathology: a brief history of the biopolitics of US mathematics education
    Ryan Ziols, Kathryn L. Kirchgasler
    Educational Studies in Mathematics.2021; 108(1-2): 123.     CrossRef
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Ebola virus disease outbreak in Korea: use of a mathematical model and stochastic simulation to estimate risk
Youngsuk Ko, Seok-Min Lee, Soyoung Kim, Moran Ki, Eunok Jung
Epidemiol Health. 2019;41:e2019048.   Published online November 24, 2019
DOI: https://doi.org/10.4178/epih.e2019048
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AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
According to the World Health Organization, there have been frequent reports of Ebola virus disease (EVD) since the 2014 EVD pandemic in West Africa. We aim to estimate the outbreak scale when an EVD infected person arrives in Korea.
METHODS
Western Africa EVD epidemic mathematical model SEIJR or SEIJQR was modified to create a Korean EVD outbreak model. The expected number of EVD patients and outbreak duration were calculated by stochastic simulation under the scenarios of Best case, Diagnosis delay, and Case missing.
RESULTS
The 2,000 trials of stochastic simulation for each scenario demonstrated the following results: The possible median number of patients is 2 and the estimated maximum number is 11 when the government intervention is proceeded immediately right after the first EVD case is confirmed. With a 6-day delay in diagnosis of the first case, the median number of patients becomes 7, and the maximum, 20. If the first case is missed and the government intervention is not activated until 2 cases of secondary infection occur, the median number of patients is estimated at 15, and the maximum, at 35.
CONCLUSIONS
Timely and rigorous diagnosis is important to reduce the spreading scale of infection when a new communicable disease is inflowed into Korea. Moreover, it is imperative to strengthen the local surveillance system and diagnostic protocols to avoid missing cases of secondary infection.
Summary
Korean summary
본 연구는 수학적 모델과 확률 시뮬레이션 기법을 이용하여 국내에 유입되지 않았던 에볼라바이러스병(EVD)의 확산 위험도를 정량적으로 예측하는 첫 번째 연구이다. 또한 이 연구를 통해 에볼라바이러스병 환자의 유입 시 발생 가능한 진단 지연 혹은 유입 미인지 상황을 가정하여 발생할 수 있는 2차 감염자 수 및 감염 종식까지의 기간을 계산했고 에볼라바이러스 유입 대비 실시간모니터링의 중요성과 확산 시 상황에 따른 최대 일일 환자수를 합리적으로 제시할 수 있다.

Citations

Citations to this article as recorded by  
  • Estimating the Transmission Risk of COVID-19 in Nigeria: A Mathematical Modelling Approach
    Irany FA, Akwafuo SE, Abah T, Mikler AR
    Journal of Health Care and Research.2020; 1(3): 135.     CrossRef
Epidemiol Health : Epidemiology and Health
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