Osong Public Health and Research Perspectives

Sunmi Lee

5 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

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59 Citations

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

On the analysis of Caputo fractional order dynamics of Middle East Lungs Coronavirus (MERS-CoV) model
Qura Tul Ain, Naveed Anjum, Anwarud Din, Anwar Zeb, Salih Djilali, Zareen A. Khan
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The transmission dynamics of Middle East Respiratory Syndrome coronavirus
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Travel Medicine and Infectious Disease.2022; 45: 102243. CrossRef
The asymptotic analysis of novel coronavirus disease via fractional-order epidemiological model
Tahir Khan, Saeed Ahmad, Rahman Ullah, Ebenezer Bonyah, Khursheed J. Ansari
AIP Advances.2022; 12(3): 035349. CrossRef
MODELING FRACTIONAL-ORDER DYNAMICS OF MERS-COV VIA MITTAG-LEFFLER LAW
HAIDONG QU, MATI UR RAHMAN, YE WANG, MUHAMMAD ARFAN, ADNAN
Fractals.2022;[Epub] CrossRef
Distinguishing viruses responsible for influenza-like illness
Julie A. Spencer, Deborah P. Shutt, S. Kane Moser, Hannah Clegg, Helen J. Wearing, Harshini Mukundan, Carrie A. Manore
Journal of Theoretical Biology.2022; 545: 111145. CrossRef
ON THE ANALYSIS OF FRACTAL-FRACTIONAL ORDER MODEL OF MIDDLE EAST RESPIRATION SYNDROME CORONAVIRUS (MERS-CoV) UNDER CAPUTO OPERATOR
LEI ZHANG, TAREQ SAEED, MIAO-KUN WANG, NUDRAT AAMIR, MUHAMMAD IBRAHIM
Fractals.2022;[Epub] CrossRef
Dynamics of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) involving fractional derivative with Mittag-Leffler kernel
Tariq Mahmood, Fuad S. Al-Duais, Mei Sun
Physica A: Statistical Mechanics and its Applicati.2022; 606: 128144. CrossRef
Approximate solution for the nonlinear fractional order mathematical model
Kahkashan Mahreen, Qura Tul Ain, Gauhar Rahman, Bahaaeldin Abdalla, Kamal Shah, Thabet Abdeljawad
AIMS Mathematics.2022; 7(10): 19267. CrossRef
Modeling and Dynamics of the Fractional Order SARS-CoV-2 Epidemiological Model
Tahir Khan, Roman Ullah, Ali Yousef, Gul Zaman, Qasem M. Al-Mdallal, Yasser Alraey, M. De Aguiar
Complexity.2022; 2022: 1. CrossRef
Product of natural evolution (SARS, MERS, and SARS-CoV-2); deadly diseases, from SARS to SARS-CoV-2
Mohamad Hesam Shahrajabian, Wenli Sun, Qi Cheng
Human Vaccines & Immunotherapeutics.2021; 17(1): 62. CrossRef
Analysis of the fractional corona virus pandemic via deterministic modeling
Nguyen Huy Tuan, Vo Viet Tri, Dumitru Baleanu
Mathematical Methods in the Applied Sciences.2021; 44(1): 1086. CrossRef
Mathematical analysis of SIRD model of COVID-19 with Caputo fractional derivative based on real data
Kottakkaran Sooppy Nisar, Shabir Ahmad, Aman Ullah, Kamal Shah, Hussam Alrabaiah, Muhammad Arfan
Results in Physics.2021; 21: 103772. CrossRef
COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model
Rachel Waema Mbogo, John W. Odhiambo
Afrika Matematika.2021; 32(5-6): 757. CrossRef
Fractal-fractional mathematical modeling and forecasting of new cases and deaths of COVID-19 epidemic outbreaks in India
Mansour A. Abdulwasaa, Mohammed S. Abdo, Kamal Shah, Taher A. Nofal, Satish K. Panchal, Sunil V. Kawale, Abdel-Haleem Abdel-Aty
Results in Physics.2021; 20: 103702. CrossRef
Mathematical analysis of COVID-19 via new mathematical model
Abdullah, Saeed Ahmad, Saud Owyed, Abdel-Haleem Abdel-Aty, Emad E. Mahmoud, Kamal Shah, Hussam Alrabaiah
Chaos, Solitons & Fractals.2021; 143: 110585. CrossRef
Modeling the transmission dynamics of middle eastern respiratory syndrome coronavirus with the impact of media coverage
BiBi Fatima, Manar A. Alqudah, Gul Zaman, Fahd Jarad, Thabet Abdeljawad
Results in Physics.2021; 24: 104053. CrossRef
Theoretical and numerical analysis for transmission dynamics of COVID-19 mathematical model involving Caputo–Fabrizio derivative
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
Assessment of Prediction Models of Confirmed, Recovered and Deceased cases due to COVID-19
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
Results in Physics.2021; 24: 104004. CrossRef
Middle East respiratory syndrome coronavirus – The need for global proactive surveillance, sequencing and modeling
Jaffar A. Al-Tawfiq, Eskild Petersen, Ziad A. Memish, Stanley Perlman, Alimuddin Zumla
Travel Medicine and Infectious Disease.2021; 43: 102118. CrossRef
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
Results in Physics.2021; 28: 104598. CrossRef
The Effect of Feedback Controls on Stability in a Fractional-Order SI Epidemic Model
Saad Z. Rida, Ahmed A. Farghaly, Fatma Hussien
International Journal of Applied and Computational.2021;[Epub] CrossRef
Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review
Jingxuan Wang, Xiao Chen, Zihao Guo, Shi Zhao, Ziyue Huang, Zian Zhuang, Eliza Lai-yi Wong, Benny Chung-Ying Zee, Marc Ka Chun Chong, Maggie Haitian Wang, Eng Kiong Yeoh
Computational and Structural Biotechnology Journal.2021; 19: 5039. CrossRef
Middle East Respiratory Syndrome Coronavirus
Jaffar A. Al-Tawfiq, Esam I. Azhar, Ziad A. Memish, Alimuddin Zumla
Seminars in Respiratory and Critical Care Medicine.2021; 42(06): 828. CrossRef
Early warning signal reliability varies with COVID-19 waves
Duncan A. O'Brien, Christopher F. Clements
Biology Letters.2021;[Epub] CrossRef
Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries
Johannes Stübinger, Lucas Schneider
Healthcare.2020; 8(2): 99. CrossRef
Super-spreading events and contribution to transmission of MERS, SARS, and SARS-CoV-2 (COVID-19)
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
Electronic Journal of General Medicine.2020; 17(6): em251. CrossRef
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
SEIR model for COVID-19 dynamics incorporating the environment and social distancing
Samuel Mwalili, Mark Kimathi, Viona Ojiambo, Duncan Gathungu, Rachel Mbogo
BMC Research Notes.2020;[Epub] CrossRef
Controlling the Spread of COVID-19: Optimal Control Analysis
Chinwendu E. Madubueze, Sambo Dachollom, Isaac Obiajulu Onwubuya
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
Sabri T.M. Thabet, Mohammed S. Abdo, Kamal Shah, Thabet Abdeljawad
Results in Physics.2020; 19: 103507. CrossRef
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
Ecological Complexity.2020; 44: 100885. CrossRef
Mathematical modeling, analysis, and simulation of the COVID-19 pandemic with explicit and implicit behavioral changes
Comfort Ohajunwa, Kirthi Kumar, Padmanabhan Seshaiyer
Computational and Mathematical Biophysics.2020; 8(1): 216. CrossRef
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
Scientific Reports.2019;[Epub] CrossRef
Development of a recombinant replication-deficient rabies virus-based bivalent-vaccine against MERS-CoV and rabies virus and its humoral immunogenicity in mice
Hirofumi Kato, Mutsuyo Takayama-Ito, Itoe Iizuka-Shiota, Shuetsu Fukushi, Guillermo Posadas-Herrera, Madoka Horiya, Masaaki Satoh, Tomoki Yoshikawa, Souichi Yamada, Shizuko Harada, Hikaru Fujii, Miho Shibamura, Takuya Inagaki, Kinjiro Morimoto, Masayuki S
PLOS ONE.2019; 14(10): e0223684. CrossRef
Healthcare-associated infections: the hallmark of Middle East respiratory syndrome coronavirus with review of the literature
J.A. Al-Tawfiq, P.G. Auwaerter
Journal of Hospital Infection.2019; 101(1): 20. CrossRef
Middle East respiratory syndrome coronavirus in pediatrics: a report of seven cases from Saudi Arabia
Sarah H. Alfaraj, Jaffar A. Al-Tawfiq, Talal A. Altuwaijri, Ziad A. Memish
Frontiers of Medicine.2019; 13(1): 126. CrossRef
Asymptomatic Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection: Extent and implications for infection control: A systematic review
Jaffar A. Al-Tawfiq, Philippe Gautret
Travel Medicine and Infectious Disease.2019; 27: 27. CrossRef
Clinical predictors of mortality of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection: A cohort study
Sarah H. Alfaraj, Jaffar A. Al-Tawfiq, Ayed Y. Assiri, Nojoom A. Alzahrani, Amal A. Alanazi, Ziad A. Memish
Travel Medicine and Infectious Disease.2019; 29: 48. CrossRef
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
Clinical Microbiology Reviews.2018;[Epub] CrossRef
Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea
Yunhwan Kim, Hohyung Ryu, Sunmi Lee
International Journal of Environmental Research an.2018; 15(11): 2369. CrossRef
Evaluation of visual triage for screening of Middle East respiratory syndrome coronavirus patients
S.H. Alfaraj, J.A. Al-Tawfiq, P. Gautret, M.G. Alenazi, A.Y. Asiri, Z.A. Memish
New Microbes and New Infections.2018; 26: 49. CrossRef
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
BMC Public Health.2018;[Epub] CrossRef
Working experiences of nurses during the Middle East respiratory syndrome outbreak
Hee Sun Kang, Ye Dong Son, Sun-Mi Chae, Colleen Corte
International Journal of Nursing Practice.2018; 24(5): e12664. CrossRef
Disaster medicine: current status and future directions of emergency medical team for overseas disaster crisis
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
Diagnostic Microbiology and Infectious Disease.2017; 89(2): 106. CrossRef
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
Journal of Infection.2016; 73(5): 468. CrossRef
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

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2 Citations

Abstract 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

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Abstract 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

1,885 View
14 Download
7 Citations

Abstract 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

Mathematical analysis of a two-strain tuberculosis model in Bangladesh
Md Abdul Kuddus, Emma S. McBryde, Adeshina I. Adekunle, Lisa J. White, Michael T. Meehan
Scientific Reports.2022;[Epub] CrossRef
Age-Specific Mathematical Model for Tuberculosis Transmission Dynamics in South Korea
Sunmi Lee, Hae-Young Park, Hohyung Ryu, Jin-Won Kwon
Mathematics.2021; 9(8): 804. CrossRef
Scenario analysis for programmatic tuberculosis control in Bangladesh: a mathematical modelling study
Md Abdul Kuddus, Michael T. Meehan, Md. Abu Sayem, Emma S. McBryde
Scientific Reports.2021;[Epub] CrossRef
Modeling drug-resistant tuberculosis amplification rates and intervention strategies in Bangladesh
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
Predictors of job satisfaction and burnout among tuberculosis management nurses and physicians
Hae-Suk Seo, Hyunjoong Kim, Se-Min Hwang, Soo Hyun Hong, In-Young Lee
Epidemiology and Health.2016; 38: e2016008. CrossRef
Is Tuberculosis Still the Number One Infectious Disease in Korea?
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

1,950 View
25 Download
8 Citations

Abstract 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

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John G. Alford, Stephen A. McCoy
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Revista de Salud Pública.2018; 20(1): 117. CrossRef
Summing Up Again
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Roll the Dice
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives.2014; 5(5): 243. CrossRef
Journal Publishing: Never Ending Saga
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives.2014; 5(1): 1. CrossRef

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