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5 "Disease outbreaks"
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Original Articles
COVID-19 outbreak in a religious village community in Republic of Korea and risk factors for transmission
Jiae Shim, Eunju Lee, Eunyoung Kim, Yeonhwa Choi, Giseok Kang, Bryan Inho Kim
Osong Public Health Res Perspect. 2023;14(2):110-118.   Published online April 5, 2023
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  • 36 Download
Graphical AbstractGraphical Abstract AbstractAbstract PDF
This study aimed to assess the scale and transmission patterns of coronavirus disease 2019 (COVID-19) in a religious village community in South Korea, to determine the risk factors of transmission, and to evaluate vaccine effectiveness.
An epidemiological survey was conducted, and data were collected and analyzed from 602 villagers in the religious village community. Multivariate logistic regression analysis was used to identify the risk factors for COVID-19 transmission and to evaluate vaccine effectiveness.
The outbreak attack rate was 72.1% (434/602). The attack rate was high among women in their 60s, the unemployed, residents living near religious facility (<500 m), and the unvaccinated. Age, the distance between religious facility and residences, and the absence of vaccination were identified as risk factors for transmission. Vaccine effectiveness was 49.0%, and the highest effectiveness was seen in the age group of 59 years or younger (65.8%).
This village community was isolated, with little communication with the outside world. However, the frequency of close contact between residents was relatively high, contributing to the spread of COVID-19 in the village even with relatively short exposure. Vaccination rates in the village community were also lower than those in the general public. Public health authorities should consider the potential impact of cultural factors, including religion, that could lead to the exponential spread of COVID-19 in closed village communities.
COVID-19 outbreak response at a nursing hospital in South Korea in the post-vaccination era, including an estimation of the effectiveness of the first shot of the Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1-S)
Chanhee Kim, Geon Kang, Sun Gu Kang, Heeyoung Lee
Osong Public Health Res Perspect. 2022;13(2):114-122.   Published online April 26, 2022
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  • 78 Download
  • 1 Citations
AbstractAbstract PDF
We descriptively reviewed a coronavirus disease 2019 (COVID-19) outbreak at a nursing hospital in Gyeonggi Province (South Korea) and assessed the effectiveness of the first dose of the Oxford-AstraZeneca vaccine in a real-world population. Methods: The general process of the epidemiological investigation included a public health intervention. The relative risk (RR) of vaccinated and unvaccinated groups was calculated and compared to confirm the risk of severe acute respiratory syndrome coronavirus-2 (SARSCoV-2) infection, and vaccine effectiveness was evaluated based on the calculated RR. Results: The population at risk was confined to ward E among 8 wards of Hospital X, where the outbreak occurred. This population comprised 55 people, including 39 patients, 12 nurses, and 4 caregivers, and 19 cases were identified. The RR between the vaccinated and unvaccinated groups was 0.04, resulting in a vaccine effectiveness of 95.3%. The vaccination rate of the nonpatients in ward E was the lowest in the entire hospital, whereas the overall vaccination rate of the combined patient and non-patient groups in ward E was the third lowest. Conclusion: The first dose of the Oxford-AstraZeneca vaccine (ChAdOx1-S) was effective in preventing SARS-CoV-2 infection. To prevent COVID-19 outbreaks in medical facilities, it is important to prioritize the vaccination of healthcare providers.


Citations to this article as recorded by  
  • COVID-19 Vaccination in Korea: Past, Present, and the Way Forward
    Eliel Nham, Joon Young Song, Ji Yun Noh, Hee Jin Cheong, Woo Joo Kim
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
Delays in the diagnosis and treatment of tuberculosis during the COVID-19 outbreak in the Republic of Korea in 2020
Jiyeon Yang, Yunhyung Kwon, Jaetae Kim, Yoojin Jang, Jiyeon Han, Daae Kim, Hyeran Jeong, Hyekyung Park, Eunhye Shim
Osong Public Health Res Perspect. 2021;12(5):293-303.   Published online September 23, 2021
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  • 169 Download
  • 7 Citations
Graphical AbstractGraphical Abstract AbstractAbstract PDF
We investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on tuberculosis (TB) management in the Republic of Korea (ROK).
This retrospective cross-sectional study used nationwide ROK TB notification data (98,346 cases) from 2017 to 2020. The median time from the onset of TB symptoms to treatment initiation and the compliance rates with the required timing for notification and individual case investigations were measured and compared across periods and regions affected by the COVID-19 epidemic.
TB diagnosis during the COVID-19 pandemic was delayed. The median time to TB treatment initiation (25 days) in 2020 increased by 3 days compared to that of the previous 3 years (22 days) (p<0.0001). In the outbreak in Seoul, Incheon, and Gyeonggi province during August, the time to TB diagnosis was 4 days longer than in the previous 3 years (p=0.0303). In the outbreak in Daegu and Gyeongbuk province from February to March 2020, the compliance rate with the required timing for individual case investigations was 2.2%p points lower than in other areas in 2020 (p=0.0148). For public health centers, the rate was 13%p lower than in other areas (80.3% vs. 93.3%, p=0.0003).
TB diagnoses during the COVID-19 pandemic in the ROK were delayed nationwide, especially for patients notified by public-private mix TB control hospitals. TB individual case investigations were delayed in regional COVID-19 outbreak areas (Daegu and Gyeongbuk province), especially in public health centers. Developing strategies to address this issue will be helpful for sustainable TB management during future outbreaks.


Citations to this article as recorded by  
  • Tuberculosis: Republic of Korea, 2021
    Jinsoo Min, Hyung Woo Kim, Ju Sang Kim
    Tuberculosis and Respiratory Diseases.2023; 86(1): 67.     CrossRef
  • Prevalence and associated factors of diabetes mellitus among patients with tuberculosis in South Korea from 2011 to 2018: a nationwide cohort study
    Dawoon Jeong, Jeongha Mok, Doosoo Jeon, Hee-Yeon Kang, Hee Jin Kim, Hee-Sun Kim, Jeong Mi Seo, Hongjo Choi, Young Ae Kang
    BMJ Open.2023; 13(3): e069642.     CrossRef
  • Increased Healthcare Delays in Tuberculosis Patients During the First Wave of COVID-19 Pandemic in Korea: A Nationwide Cross-Sectional Study
    Jinsoo Min, Yousang Ko, Hyung Woo Kim, Hyeon-Kyoung Koo, Jee Youn Oh, Yun-Jeong Jeong, Hyeon Hui Kang, Kwang Joo Park, Yong Il Hwang, Jin Woo Kim, Joong Hyun Ahn, Yangjin Jegal, Ji Young Kang, Sung-Soon Lee, Jae Seuk Park, Ju Sang Kim
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
  • Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020
    Wang Yun, Chen Huijuan, Liao Long, Lu Xiaolong, Zhang Aihua
    BMC Infectious Diseases.2022;[Epub]     CrossRef
  • Real-world association of adherence with outcomes and economic burden in patients with tuberculosis from South Korea claims data
    Sun-Hong Kwon, Jin Hyun Nam, Hye-Lin Kim, Hae-Young Park, Jin-Won Kwon
    Frontiers in Pharmacology.2022;[Epub]     CrossRef
  • The Impact of the COVID-19 Pandemic on Tuberculosis Case Notification and Treatment Outcomes in Eswatini
    Hloniphile Victory Masina, I-Feng Lin, Li-Yin Chien
    International Journal of Public Health.2022;[Epub]     CrossRef
  • Trends in incidences of newly notified tuberculosis in Jeju Province, Korea, 2017-2021
    Jinhee Kim, Nam-Hun Kang, Jong-Myon Bae
    Journal of Medicine and Life Science.2022; 19(3): 103.     CrossRef
2019 Tabletop Exercise for Laboratory Diagnosis and Analyses of Unknown Disease Outbreaks by the Korea Centers for Disease Control and Prevention
Il-Hwan Kim, Jun Hyeong Jang, Su-Kyoung Jo, Jin Sun No, Seung-Hee Seo, Jun-Young Kim, Sang-Oun Jung, Jeong-Min Kim, Sang-Eun Lee, Hye-Kyung Park, Eun-Jin Kim, Jun Ho Jeon, Myung-Min Choi, Boyeong Ryu, Yoon Suk Jang, Hwami Kim, Jin Lee, Seung-Hwan Shin, Hee Kyoung Kim, Eun-Kyoung Kim, Ye Eun Park, Cheon-Kwon Yoo, Sang-Won Lee, Myung-Guk Han, Gi-Eun Rhie, Byung Hak Kang
Osong Public Health Res Perspect. 2020;11(5):280-285.   Published online October 22, 2020
  • 4,603 View
  • 100 Download
AbstractAbstract PDF

The Korea Centers for Disease Control and Prevention has published “A Guideline for Unknown Disease Outbreaks (UDO).” The aim of this report was to introduce tabletop exercises (TTX) to prepare for UDO in the future.


The UDO Laboratory Analyses Task Force in Korea Centers for Disease Control and Prevention in April 2018, assigned unknown diseases into 5 syndromes, designed an algorithm for diagnosis, and made a panel list for diagnosis by exclusion. Using the guidelines and laboratory analyses for UDO, TTX were introduced.


Since September 9th, 2018, the UDO Laboratory Analyses Task Force has been preparing TTX based on a scenario of an outbreak caused by a novel coronavirus. In December 2019, through TTX, individual missions, epidemiological investigations, sample treatments, diagnosis by exclusions, and next generation sequencing analysis were discussed, and a novel coronavirus was identified as the causal pathogen.


Guideline and laboratory analyses for UDO successfully applied in TTX. Conclusions drawn from TTX could be applied effectively in the analyses for the initial response to COVID-19, an ongoing epidemic of 2019 – 2020. Therefore, TTX should continuously be conducted for the response and preparation against UDO.

Invited Original Article
Incubation Period of Ebola Hemorrhagic Virus Subtype Zaire
Martin Eichner, Scott F. Dowell, Nina Firese
Osong Public Health Res Perspect. 2011;2(1):3-7.   Published online June 30, 2011
  • 3,316 View
  • 15 Download
  • 44 Citations
AbstractAbstract PDF
Ebola hemorrhagic fever has killed over 1300 people, mostly in equatorial Africa. There is still uncertainty about the natural reservoir of the virus and about some of the factors involved in disease transmission. Until now, a maximum incubation period of 21 days has been assumed.
We analyzed data collected during the Ebola outbreak (subtype Zaire) in Kikwit, Democratic Republic of the Congo, in 1995 using maximum likelihood inference and assuming a log-normally distributed incubation period.
The mean incubation period was estimated to be 12.7 days (standard deviation 4.31 days), indicating that about 4.1% of patients may have incubation periods longer than 21 days.
If the risk of new cases is to be reduced to 1% then 25 days should be used when investigating the source of an outbreak, when determining the duration of surveillance for contacts, and when declaring the end of an outbreak.


Citations to this article as recorded by  
  • Isothermal nucleic acid amplification technology for rapid detection of virus
    Zhenting Wei, Xiaowen Wang, Huhu Feng, Fanpu Ji, Dan Bai, Xiaoping Dong, Wei Huang
    Critical Reviews in Biotechnology.2023; 43(3): 415.     CrossRef
  • Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks
    Aliou Bouba, Kristina Barbara Helle, Kristan Alexander Schneider, Jan Rychtář
    PLOS ONE.2023; 18(1): e0276351.     CrossRef
  • Stability analysis of an SIR epidemic model with homestead-isolation on the susceptible and infectious, immunity, relapse and general incidence rate
    Amine Bernoussi
    International Journal of Biomathematics.2023;[Epub]     CrossRef
  • Path analysis method in an epidemic model and stability analysis
    Yong Zhou, Yiming Ding, Minrui Guo
    Frontiers in Physics.2023;[Epub]     CrossRef
  • Tradeoff between speed and reproductive number in pathogen evolution
    Andreas Eilersen, Bjarke Frost Nielsen, Kim Sneppen
    Physical Review Research.2023;[Epub]     CrossRef
  • A Reinforcement Learning Based Decision Support Tool for Epidemic Control: Validation Study for COVID-19
    Mohamed-Amine Chadi, Hajar Mousannif
    Applied Artificial Intelligence.2022;[Epub]     CrossRef
  • Spatially-heterogeneous embedded stochastic SEIR models for the 2014–2016 Ebola outbreak in West Africa
    Kaitlyn Martinez, Grant Brown, Stephen Pankavich
    Spatial and Spatio-temporal Epidemiology.2022; 41: 100505.     CrossRef
  • Staff Scheduling During a Pandemic: The Case of Radiation Therapy Department
    Hossein Abouee Mehrizi, Arian Aminoleslami, Johnson Darko, Ernest Osei, Houra Mahmoudzadeh
    SSRN Electronic Journal .2022;[Epub]     CrossRef
  • Review of Ebola virus disease in children – how far have we come?
    Devika Dixit, Kasereka Masumbuko Claude, Lindsey Kjaldgaard, Michael T. Hawkes
    Paediatrics and International Child Health.2021; 41(1): 12.     CrossRef
  • Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity
    Rachid Muleia, Marc Aerts, Christel Faes, Maria Vittoria Barbarossa
    PLOS ONE.2021; 16(5): e0250765.     CrossRef
  • Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates
    Shakti Davis, Lauren Milechin, Tejash Patel, Mark Hernandez, Greg Ciccarelli, Siddharth Samsi, Lisa Hensley, Arthur Goff, John Trefry, Sara Johnston, Bret Purcell, Catherine Cabrera, Jack Fleischman, Albert Reuther, Kajal Claypool, Franco Rossi, Anna Honk
    Frontiers in Physiology.2021;[Epub]     CrossRef
  • Advances and insights in the diagnosis of viral infections
    Julija Dronina, Urte Samukaite-Bubniene, Arunas Ramanavicius
    Journal of Nanobiotechnology.2021;[Epub]     CrossRef
  • Treatment of Ebola-related critical illness
    Peter Kiiza, S. Mullin, K. Teo, N. K. J. Adhikari, R. A. Fowler
    Intensive Care Medicine.2020; 46(2): 285.     CrossRef
  • AAV Vectored Immunoprophylaxis for Filovirus Infections
    Amira D. Rghei, Laura P. van Lieshout, Lisa A. Santry, Matthew M. Guilleman, Sylvia P. Thomas, Leonardo Susta, Khalil Karimi, Byram W. Bridle, Sarah K. Wootton
    Tropical Medicine and Infectious Disease.2020; 5(4): 169.     CrossRef
  • Vaccination strategies to control Ebola epidemics in the context of variable household inaccessibility levels
    Gerardo Chowell, Amna Tariq, Maria Kiskowski, Benjamin Althouse
    PLOS Neglected Tropical Diseases.2019; 13(11): e0007814.     CrossRef
  • Application of the CDC EbolaResponse Modeling tool to disease predictions
    Robert H. Gaffey, Cécile Viboud
    Epidemics.2018; 22: 22.     CrossRef
  • A mathematical model with isolation for the dynamics of Ebola virus
    Amira Rachah
    Journal of Physics: Conference Series.2018; 1132: 012058.     CrossRef
  • Multiscale model for pedestrian and infection dynamics during air travel
    Sirish Namilae, Pierrot Derjany, Anuj Mubayi, Mathew Scotch, Ashok Srinivasan
    Physical Review E.2017;[Epub]     CrossRef
  • Modeling ebola virus disease transmissions with reservoir in a complex virus life ecology
    Tsanou Berge, Samuel Bowong, Jean Lubuma, Martin Luther Mann Manyombe
    Mathematical Biosciences and Engineering.2017; 15(1): 21.     CrossRef
  • Application of a quantitative entry assessment model to compare the relative risk of incursion of zoonotic bat-borne viruses into European Union Member States
    Verity Horigan, Paul Gale, Rowena D. Kosmider, Christopher Minnis, Emma L. Snary, Andrew C. Breed, Robin R.L. Simons
    Microbial Risk Analysis.2017; 7: 8.     CrossRef
  • Multigroup deterministic and stochasticSEIRIepidemic models with nonlinear incidence rates and distributed delays: A stability analysis
    Hong Zhang, Juan Xia, Paul Georgescu
    Mathematical Methods in the Applied Sciences.2017; 40(18): 6254.     CrossRef
  • Modeling spatial invasion of Ebola in West Africa
    Jeremy P. D’Silva, Marisa C. Eisenberg
    Journal of Theoretical Biology.2017; 428: 65.     CrossRef
  • The potential impact of a prophylactic vaccine for Ebola in Sierra Leone
    Erin N. Bodine, Connor Cook, Mikayla Shorten
    Mathematical Biosciences and Engineering.2017; 15(2): 337.     CrossRef
  • Ebola virus – from neglected threat to global emergency state
    Daniela Alexandra de Meneses Rocha Aguiar Pacheco, Acácio Agostinho Gonçalves Rodrigues, Carmen Maria Lisboa da Silva
    Revista da Associação Médica Brasileira.2016; 62(5): 458.     CrossRef
  • Neglected filoviruses
    Robin Burk, Laura Bollinger, Joshua C. Johnson, Jiro Wada, Sheli R. Radoshitzky, Gustavo Palacios, Sina Bavari, Peter B. Jahrling, Jens H. Kuhn, Urs Greber
    FEMS Microbiology Reviews.2016; 40(4): 494.     CrossRef
  • Treatment–donation-stockpile dynamics in ebola convalescent blood transfusion therapy
    Xi Huo, Xiaodan Sun, Kunquan Lan, Jianhong Wu
    Journal of Theoretical Biology.2016; 392: 53.     CrossRef
  • Ebola virus disease in nonendemic countries
    Samson Sai-Yin Wong, Sally Cheuk-Ying Wong
    Journal of the Formosan Medical Association.2015; 114(5): 384.     CrossRef
  • Ebola Hemorrhagic Fever
    Maryam Keshtkar Jahromi, Batool Sharifi Mood
    International Journal of Infection.2015;[Epub]     CrossRef
  • Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization
    Wayne M. Getz, Jean-Paul Gonzalez, Richard Salter, James Bangura, Colin Carlson, Moinya Coomber, Eric Dougherty, David Kargbo, Nathan D. Wolfe, Nadia Wauquier
    Computational and Mathematical Methods in Medicine.2015; 2015: 1.     CrossRef
  • What is Ebola?
    R. A. Stein
    International Journal of Clinical Practice.2015; 69(1): 49.     CrossRef
  • Ebola virus disease outbreak in Nigeria: Transmission dynamics and rapid control
    C.L. Althaus, N. Low, E.O. Musa, F. Shuaib, S. Gsteiger
    Epidemics.2015; 11: 80.     CrossRef
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    Maria D. Van Kerkhove, Ana I. Bento, Harriet L. Mills, Neil M. Ferguson, Christl A. Donnelly
    Scientific Data.2015;[Epub]     CrossRef
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    Joshua S. Weitz, Jonathan Dushoff
    Scientific Reports.2015;[Epub]     CrossRef
  • Transmission Models of Historical Ebola Outbreaks
    John M. Drake, Iurii Bakach, Matthew R. Just, Suzanne M. O’Regan, Manoj Gambhir, Isaac Chun-Hai Fung
    Emerging Infectious Diseases.2015; 21(8): 1447.     CrossRef
  • Theoretical perspectives on the infectiousness of Ebola virus disease
    Hiroshi Nishiura, Gerardo Chowell
    Theoretical Biology and Medical Modelling.2015;[Epub]     CrossRef
  • Effect of Ebola Progression on Transmission and Control in Liberia
    Dan Yamin, Shai Gertler, Martial L. Ndeffo-Mbah, Laura A. Skrip, Mosoka Fallah, Tolbert G. Nyenswah, Frederick L. Altice, Alison P. Galvani
    Annals of Internal Medicine.2015; 162(1): 11.     CrossRef
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    Gerardo Chowell, Hiroshi Nishiura
    BMC Medicine.2014;[Epub]     CrossRef
  • Analysis of Ebolavirus with Decision Tree and Apriori algorithm
    Eunby Go, Seungmin Lee, Taeseon Yoon
    International Journal of Machine Learning and Comp.2014; 4(6): 543.     CrossRef
  • Calculation of incubation period and serial interval from multiple outbreaks of Marburg virus disease
    Boris I Pavlin
    BMC Research Notes.2014; 7(1): 906.     CrossRef
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    Jean-Philippe Chippaux
    Journal of Venomous Animals and Toxins including T.2014; 20(1): 44.     CrossRef
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    S Ng, B J Cowling
    Eurosurveillance.2014;[Epub]     CrossRef
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    H Nishiura, G Chowell
    Eurosurveillance.2014;[Epub]     CrossRef
  • Transmission dynamics and control of Ebola virus disease outbreak in Nigeria, July to September 2014
    F O Fasina, A Shittu, D Lazarus, O Tomori, L Simonsen, C Viboud, G Chowell
    Eurosurveillance.2014;[Epub]     CrossRef
  • The Road Less Traveled
    Chaeshin Chu
    Osong Public Health and Research Perspectives.2011; 2(1): 1.     CrossRef

PHRP : Osong Public Health and Research Perspectives