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Original Articles
The Characteristics of Middle Eastern Respiratory Syndrome Coronavirus Transmission Dynamics in South Korea
Yunhwan Kim, Sunmi Lee, Chaeshin Chu, Seoyun Choe, Saeme Hong, Youngseo Shin
Osong Public Health Res Perspect. 2016;7(1):49-55.   Published online February 28, 2016
DOI: https://doi.org/10.1016/j.phrp.2016.01.001
  • 4,302 View
  • 26 Download
  • 68 Crossref
AbstractAbstract PDF
Objectives
The outbreak of Middle Eastern respiratory syndrome coronavirus (MERS-CoV) was one of the major events in South Korea in 2015. In particular, this study pays attention to formulating a mathematical model for MERS transmission dynamics and estimating transmission rates.
Methods
Incidence data of MERS-CoV from the government authority was analyzed for the first aim and a mathematical model was built and analyzed for the second aim of the study. A mathematical model for MERS-CoV transmission dynamics is used to estimate the transmission rates in two periods due to the implementation of intensive interventions.
Results
Using the estimates of the transmission rates, the basic reproduction number was estimated in two periods. Due to the superspreader, the basic reproduction number was very large in the first period; however, the basic reproduction number of the second period has reduced significantly after intensive interventions.
Conclusion
It turned out to be the intensive isolation and quarantine interventions that were the most critical factors that prevented the spread of the MERS outbreak. The results are expected to be useful to devise more efficient intervention strategies in the future.

Citations

Citations to this article as recorded by  
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    Afrika Matematika.2021; 32(5-6): 757.     CrossRef
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    Results in Physics.2021; 24: 104053.     CrossRef
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    Sabri T. M. Thabet, Mohammed S. Abdo, Kamal Shah
    Advances in Difference Equations.2021;[Epub]     CrossRef
  • A computational tool for trend analysis and forecast of the COVID-19 pandemic
    Henrique Mohallem Paiva, Rubens Junqueira Magalhães Afonso, Fabiana Mara Scarpelli de Lima Alvarenga Caldeira, Ester de Andrade Velasquez
    Applied Soft Computing.2021; 105: 107289.     CrossRef
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    P Rakshit, S Debnath, J Mistri, S Kumar
    Journal of Physics: Conference Series.2021; 1797(1): 012004.     CrossRef
  • Modeling the dynamics of novel coronavirus (COVID-19) via stochastic epidemic model
    Tahir Khan, Gul Zaman, Youssef El-Khatib
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  • Middle East respiratory syndrome coronavirus – The need for global proactive surveillance, sequencing and modeling
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  • Mathematical model of COVID-19 in Nigeria with optimal control
    Adesoye Idowu Abioye, Olumuyiwa James Peter, Hammed Abiodun Ogunseye, Festus Abiodun Oguntolu, Kayode Oshinubi, Abdullahi Adinoyi Ibrahim, Ilyas Khan
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  • Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review
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    Biology Letters.2021;[Epub]     CrossRef
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    Healthcare.2020; 8(2): 99.     CrossRef
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    J.A. Al-Tawfiq, A.J. Rodriguez-Morales
    Journal of Hospital Infection.2020; 105(2): 111.     CrossRef
  • Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
    Faïçal Ndaïrou, Iván Area, Juan J. Nieto, Delfim F.M. Torres
    Chaos, Solitons & Fractals.2020; 135: 109846.     CrossRef
  • A Generalized Overview of SARS-CoV-2: Where Does the Current Knowledge Stand?
    Hiya Islam, Ahsab Rahman, Jaasia Masud, Dipita Saha Shweta, Yusha Araf, Md. Asad Ullah, Syed Muktadir Al Sium, Bishajit Sarkar
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  • Optimal control strategies for the transmission risk of COVID-19
    Legesse Lemecha Obsu, Shiferaw Feyissa Balcha
    Journal of Biological Dynamics.2020; 14(1): 590.     CrossRef
  • A data-driven model to describe and forecast the dynamics of COVID-19 transmission
    Henrique Mohallem Paiva, Rubens Junqueira Magalhães Afonso, Igor Luppi de Oliveira, Gabriele Fernandes Garcia, Martin Chtolongo Simuunza
    PLOS ONE.2020; 15(7): e0236386.     CrossRef
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  • Controlling the Spread of COVID-19: Optimal Control Analysis
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    Computational and Mathematical Methods in Medicine.2020; 2020: 1.     CrossRef
  • Fractional order mathematical modeling of COVID-19 transmission
    Shabir Ahmad, Aman Ullah, Qasem M. Al-Mdallal, Hasib Khan, Kamal Shah, Aziz Khan
    Chaos, Solitons & Fractals.2020; 139: 110256.     CrossRef
  • Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models
    Seoyun Choe, Hee-Sung Kim, Sunmi Lee
    International Journal of Environmental Research an.2020; 17(17): 6137.     CrossRef
  • Study of transmission dynamics of COVID-19 mathematical model under ABC fractional order derivative
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  • A New Compartmental Epidemiological Model for COVID-19 with a Case Study of Portugal
    Ana P. Lemos-Paião, Cristiana J. Silva, Delfim F.M. Torres
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    Comfort Ohajunwa, Kirthi Kumar, Padmanabhan Seshaiyer
    Computational and Mathematical Biophysics.2020; 8(1): 216.     CrossRef
  • COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model
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  • Comparative Analysis of Eleven Healthcare-Associated Outbreaks of Middle East Respiratory Syndrome Coronavirus (Mers-Cov) from 2015 to 2017
    Sibylle Bernard-Stoecklin, Birgit Nikolay, Abdullah Assiri, Abdul Aziz Bin Saeed, Peter Karim Ben Embarek, Hassan El Bushra, Moran Ki, Mamunur Rahman Malik, Arnaud Fontanet, Simon Cauchemez, Maria D. Van Kerkhove
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  • Development of a recombinant replication-deficient rabies virus-based bivalent-vaccine against MERS-CoV and rabies virus and its humoral immunogenicity in mice
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  • Practical Guidance for Clinical Microbiology Laboratories: Viruses Causing Acute Respiratory Tract Infections
    Carmen L. Charlton, Esther Babady, Christine C. Ginocchio, Todd F. Hatchette, Robert C. Jerris, Yan Li, Mike Loeffelholz, Yvette S. McCarter, Melissa B. Miller, Susan Novak-Weekley, Audrey N. Schuetz, Yi-Wei Tang, Ray Widen, Steven J. Drews
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  • A multi-faceted approach of a nursing led education in response to MERS-CoV infection
    Jaffar A. Al-Tawfiq, Siobhan Rothwell, Heather A. Mcgregor, Zeina A. Khouri
    Journal of Infection and Public Health.2018; 11(2): 260.     CrossRef
  • MERS transmission and risk factors: a systematic review
    Ji-Eun Park, Soyoung Jung, Aeran Kim, Ji-Eun Park
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    Hee Sun Kang, Ye Dong Son, Sun‐Mi Chae, Colleen Corte
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    Minhong Choa, Jiyoung Noh, Hyun Soo Chung
    Journal of the Korean Medical Association.2017; 60(2): 149.     CrossRef
  • Estimating and modelling the transmissibility of Middle East Respiratory Syndrome CoronaVirus during the 2015 outbreak in the Republic of Korea
    Xu‐Sheng Zhang, Richard Pebody, Andre Charlett, Daniela de Angelis, Paul Birrell, Hunseok Kang, Marc Baguelin, Yoon Hong Choi
    Influenza and Other Respiratory Viruses.2017; 11(5): 434.     CrossRef
  • Influenza is more common than Middle East Respiratory Syndrome Coronavirus (MERS-CoV) among hospitalized adult Saudi patients
    Jaffar A. Al-Tawfiq, Ali A. Rabaan, Kareem Hinedi
    Travel Medicine and Infectious Disease.2017; 20: 56.     CrossRef
  • Dynamics of scientific publications on the MERS-CoV outbreaks in Saudi Arabia
    Ali A. Rabaan, Shamsah H. Al-Ahmed, Ali M. Bazzi, Jaffar A. Al-Tawfiq
    Journal of Infection and Public Health.2017; 10(6): 702.     CrossRef
  • Effects of Timely Control Intervention on the Spread of Middle East Respiratory Syndrome Coronavirus Infection
    Ilsu Choi, Dong Ho Lee, Yongkuk Kim
    Osong Public Health and Research Perspectives.2017; 8(6): 373.     CrossRef
  • Serologic responses of 42 MERS-coronavirus-infected patients according to the disease severity
    Jae-Hoon Ko, Marcel A. Müller, Hyeri Seok, Ga Eun Park, Ji Yeon Lee, Sun Young Cho, Young Eun Ha, Jin Yang Baek, So Hyun Kim, Ji-Man Kang, Yae-Jean Kim, Ik Joon Jo, Chi Ryang Chung, Myong-Joon Hahn, Christian Drosten, Cheol-In Kang, Doo Ryeon Chung, Jae-H
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  • Predictive factors for pneumonia development and progression to respiratory failure in MERS-CoV infected patients
    Jae-Hoon Ko, Ga Eun Park, Ji Yeon Lee, Ji Yong Lee, Sun Young Cho, Young Eun Ha, Cheol-In Kang, Ji-Man Kang, Yae-Jean Kim, Hee Jae Huh, Chang-Seok Ki, Byeong-Ho Jeong, Jinkyeong Park, Chi Ryang Chung, Doo Ryeon Chung, Jae-Hoon Song, Kyong Ran Peck
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  • Middle East respiratory syndrome coronavirus disease is rare in children: An update from Saudi Arabia
    Jaffar A Al-Tawfiq, Rana F Kattan, Ziad A Memish
    World Journal of Clinical Pediatrics.2016; 5(4): 391.     CrossRef
Assessment of Intensive Vaccination and Antiviral Treatment in 2009 Influenza Pandemic in Korea
Chaeshin Chu, Sunmi Lee
Osong Public Health Res Perspect. 2015;6(1):47-51.   Published online February 28, 2015
DOI: https://doi.org/10.1016/j.phrp.2014.11.007
  • 2,711 View
  • 17 Download
  • 2 Crossref
AbstractAbstract PDF
Objectives
We characterized and assessed public health measures, including intensive vaccination and antiviral treatment, implemented during the 2009 influenza pandemic in the Republic of Korea.
Methods
A mathematical model for the 2009 influenza pandemic is formulated. The transmission rate, the vaccination rate, the antiviral treatment rate, and the hospitalized rate are estimated using the least-squares method for the 2009 data of the incidence curves of the infected, vaccinated, treated, and hospitalized.
Results
The cumulative number of infected cases has reduced significantly following the implementation of the intensive vaccination and antiviral treatment. In particular, the intensive vaccination was the most critical factor that prevented severe outbreak.
Conclusion
We have found that the total infected proportion would increase by approximately six times under the half of vaccination rates.

Citations

Citations to this article as recorded by  
  • Modeling influenza transmission dynamics with media coverage data of the 2009 H1N1 outbreak in Korea
    Yunhwan Kim, Ana Vivas Barber, Sunmi Lee, Roberto Barrio
    PLOS ONE.2020; 15(6): e0232580.     CrossRef
  • Doing Mathematics with Aftermath of Pandemic Influenza 2009
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2015; 6(1): 1.     CrossRef
Assessment of the Intensive Countermeasures in the 2009 Pandemic Influenza in Korea
Jin Hyuk Choi, Yunhwan Kim, Seoyun Choe, Sunmi Lee
Osong Public Health Res Perspect. 2014;5(2):101-107.   Published online April 30, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.03.003
  • 2,642 View
  • 18 Download
AbstractAbstract PDF
Objectives
It is critical to implement effective multiple countermeasures to mitigate or retain the spread of pandemic influenza. We propose a mathematical pandemic influenza model to assess the effectiveness of multiple countermeasures implemented in 2009.
Methods
Age-specific parameters, including the transmission rate, the proportion of asymptomatic individuals, the vaccination rate, the social distancing rate, and the antiviral treatment rate are estimated using the least-square method calibrated to the incidence data.
Results
The multiple interventions (intensive vaccination, social distancing, antivrial treatment) were successfully implemented resulting in the dramatic reduction in the total number of incidence.
Conclusion
The model output is sensitive to age-specific parameters and this leads to the fact that a more elaborate age group model should be developed and extensive further studies must be followed.
What Does a Mathematical Model Tell About the Impact of Reinfection in Korean Tuberculosis Infection?
Sara Kim, Seoyun Choe, Junseong Kim, Sanga Nam, Yeon Shin, Sunmi Lee
Osong Public Health Res Perspect. 2014;5(1):40-45.   Published online February 28, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.01.002
  • 3,098 View
  • 15 Download
  • 8 Crossref
AbstractAbstract PDF
Objectives
According to the Korea Centers for Disease Control and Prevention, new active tuberculosis (TB) cases have increased since 2001. Some key factors explain and characterize the transmission dynamics of Korean TB infection, such as a higher ratio of latent individuals and a new reporting system implemented in 2001, among others.
Methods
We propose a mathematical TB model that includes exogenous reinfection to gain a better understanding of the recent trend for TB incidence. We divide the simulation time window into two periods, 1970–2000 and 2001–2012, according to the implementation date of a new TB detection system.
Results
Two sets of parameters, including the transmission rate, the latent period, the recovery rate, and the proportion of exogenous reinfection, are estimated using the least-squares method and calibrated to data on the incidence of active TB.
Conclusion
Among some key parameters in the model, the case finding effort turned out to be the most significant impacting component on the reduction in the active TB cases.

Citations

Citations to this article as recorded by  
  • Analysis of the different interventions scenario for programmatic measles control in Bangladesh: A modelling study
    Md Abdul Kuddus, Azizur Rahman, Farzana Alam, M. Mohiuddin, Jan Rychtář
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    Sunmi Lee, Hae-Young Park, Hohyung Ryu, Jin-Won Kwon
    Mathematics.2021; 9(8): 804.     CrossRef
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    Md Abdul Kuddus, Michael T. Meehan, Md. Abu Sayem, Emma S. McBryde
    Scientific Reports.2021;[Epub]     CrossRef
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    Md Abdul Kuddus, Michael T. Meehan, Lisa J. White, Emma S. McBryde, Adeshina I. Adekunle, Hasnain Seyed Ehtesham
    PLOS ONE.2020; 15(7): e0236112.     CrossRef
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    Hae-Suk Seo, Hyunjoong Kim, Se-Min Hwang, Soo Hyun Hong, In-Young Lee
    Epidemiology and Health.2016; 38: e2016008.     CrossRef
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    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2014; 5: S1.     CrossRef
  • Journal Publishing: Never Ending Saga
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2014; 5(1): 1.     CrossRef
Role of Active and Inactive Cytotoxic Immune Response in Human Immunodeficiency Virus Dynamics
Hernan Dario Toro Zapata, Angelica Graciela Caicedo Casso, Derdei Bichara, Sunmi Lee
Osong Public Health Res Perspect. 2014;5(1):3-8.   Published online February 28, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.01.001
  • 2,944 View
  • 26 Download
  • 8 Crossref
AbstractAbstract PDF
Objectives
Mathematical models can be helpful to understand the complex dynamics of human immunodeficiency virus infection within a host. Most of work has studied the interactions of host responses and virus in the presence of active cytotoxic immune cells, which decay to zero when there is no virus. However, recent research highlights that cytotoxic immune cells can be inactive but never be depleted.
Methods
We propose a mathematical model to investigate the human immunodeficiency virus dynamics in the presence of both active and inactive cytotoxic immune cells within a host. We explore the impact of the immune responses on the dynamics of human immunodeficiency virus infection under different disease stages.
Results
Standard mathematical and numerical analyses are presented for this new model. Specifically, the basic reproduction number is computed and local and global stability analyses are discussed.
Conclusion
Our results can give helpful insights when designing more effective drug schedules in the presence of active and inactive immune responses.

Citations

Citations to this article as recorded by  
  • A Multi-Scale Model for the Spread of HIV in a Population Considering the Immune Status of People
    Sol de Amor Vásquez-Quintero, Hernán Darío Toro-Zapata, Dennis Alexánder Prieto-Medellín
    Processes.2021; 9(11): 1924.     CrossRef
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    John G. Alford, Stephen A. McCoy
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PHRP : Osong Public Health and Research Perspectives