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Young S. Lee 2 Articles
Modeling the Spread of Ebola
Tae Sug Do, Young S. Lee
Osong Public Health Res Perspect. 2016;7(1):43-48.   Published online February 28, 2016
DOI: https://doi.org/10.1016/j.phrp.2015.12.012
  • 1,923 View
  • 17 Download
  • 13 Citations
AbstractAbstract PDF
Objectives
This study aims to create a mathematical model to better understand the spread of Ebola, the mathematical dynamics of the disease, and preventative behaviors.
Methods
An epidemiological model is created with a system of nonlinear differential equations, and the model examines the disease transmission dynamics with isolation through stability analysis. All parameters are approximated, and results are also exploited by simulations. Sensitivity analysis is used to discuss the effect of intervention strategies.
Results
The system has only one equilibrium point, which is the disease-free state (S,L,I,R,D) = (N,0,0,0,0). If traditional burials of Ebola victims are allowed, the possible end state is never stable. Provided that safe burial practices with no traditional rituals are followed, the endemic-free state is stable if the basic reproductive number, R0, is less than 1. Model behaviors correspond to empirical facts. The model simulation agrees with the data of the Nigeria outbreak in 2004: 12 recoveries, eight deaths, Ebola free in about 3 months, and an R0 value of about 2.6 initially, which signifies swift spread of the infection. The best way to reduce R0 is achieving the speedy net effect of intervention strategies. One day's delay in full compliance with building rings around the virus with isolation, close observation, and clear education may double the number of infected cases.
Conclusion
The model can predict the total number of infected cases, number of deaths, and duration of outbreaks among others. The model can be used to better understand the spread of Ebola, educate about prophylactic behaviors, and develop strategies that alter environment to achieve a disease-free state. A future work is to incorporate vaccination in the model when the vaccines are developed and the effects of vaccines are known better.

Citations

Citations to this article as recorded by  
  • Mathematical Models for Typhoid Disease Transmission: A Systematic Literature Review
    Sanubari Tansah Tresna, Subiyanto, Sudradjat Supian
    Mathematics.2022; 10(14): 2506.     CrossRef
  • Fractional COVID-19 Modeling and Analysis on Successive Optimal Control Policies
    Mohammed Subhi Hadi, Bülent Bilgehan
    Fractal and Fractional.2022; 6(10): 533.     CrossRef
  • Analysis of a Covid-19 model: Optimal control, stability and simulations
    Seda İğret Araz
    Alexandria Engineering Journal.2021; 60(1): 647.     CrossRef
  • Modeling 2018 Ebola virus disease outbreak with Cholesky decomposition
    Lagès Nadège Mouanguissa, Abdul A. Kamara, Xiangjun Wang
    Mathematical Methods in the Applied Sciences.2021; 44(7): 5739.     CrossRef
  • Mitigation strategies and compliance in the COVID-19 fight; how much compliance is enough?
    Swati Mukerjee, Clifton M. Chow, Mingfei Li, Martin Chtolongo Simuunza
    PLOS ONE.2021; 16(8): e0239352.     CrossRef
  • A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic
    Hamdi Friji, Raby Hamadi, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
    IEEE Access.2021; 9: 13266.     CrossRef
  • Analytical solution for post-death transmission model of Ebola epidemics
    Abdul A. Kamara, Xiangjun Wang, Lagès Nadège Mouanguissa
    Applied Mathematics and Computation.2020; 367: 124776.     CrossRef
  • Modelling the daily risk of Ebola in the presence and absence of a potential vaccine
    Stéphanie M.C. Abo, Robert Smith
    Infectious Disease Modelling.2020; 5: 905.     CrossRef
  • Data Fitting and Scenario Analysis of Vaccination in the 2014 Ebola Outbreak in Liberia
    Zhifu Xie
    Osong Public Health and Research Perspectives.2019; 10(3): 187.     CrossRef
  • Effect of sexual transmission on the West Africa Ebola outbreak in 2014: a mathematical modelling study
    Dongmei Luo, Rongjiong Zheng, Duolao Wang, Xueliang Zhang, Yi Yin, Kai Wang, Weiming Wang
    Scientific Reports.2019;[Epub]     CrossRef
  • Mathematical modeling of contact tracing as a control strategy of Ebola virus disease
    T. Berge, A. J. Ouemba Tassé, H. M. Tenkam, J. Lubuma
    International Journal of Biomathematics.2018; 11(07): 1850093.     CrossRef
  • Challenges of Designing and Implementing High Consequence Infectious Disease Response
    Joan M. King, Chetan Tiwari, Armin R. Mikler, Martin O’Neill
    Disaster Medicine and Public Health Preparedness.2018; 12(5): 563.     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
A Differential Equation Model for the Dynamics of Youth Gambling
Tae Sug Do, Young S. Lee
Osong Public Health Res Perspect. 2014;5(4):233-241.   Published online August 31, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.06.008
  • 2,005 View
  • 12 Download
  • 7 Citations
AbstractAbstract PDF
Objectives
We examine the dynamics of gambling among young people aged 16–24 years, how prevalence rates of at-risk gambling and problem gambling change as adolescents enter young adulthood, and prevention and control strategies.
Methods
A simple epidemiological model is created using ordinary nonlinear differential equations, and a threshold condition that spreads gambling is identified through stability analysis. We estimate all the model parameters using a longitudinal prevalence study by Winters, Stinchfield, and Botzet to run numerical simulations. Parameters to which the system is most sensitive are isolated using sensitivity analysis.
Results
Problem gambling is endemic among young people, with a steady prevalence of approximately 4–5%. The prevalence of problem gambling is lower in young adults aged 18–24 years than in adolescents aged 16–18 years. At-risk gambling among young adults has increased. The parameters to which the system is most sensitive correspond to primary prevention.
Conclusion
Prevention and control strategies for gambling should involve school education. A mathematical model that includes the effect of early exposure to gambling would be helpful if a longitudinal study can provide data in the future.

Citations

Citations to this article as recorded by  
  • Whose Responsibility Is It to Prevent or Reduce Gambling Harm? A Mapping Review of Current Empirical Research
    Murat Akçayır, Fiona Nicoll, David G. Baxter, Zachary S. Palmer
    International Journal of Mental Health and Addicti.2022; 20(3): 1516.     CrossRef
  • Mathematical Modeling of the Population Dynamics of Age-Structured Criminal Gangs with Correctional Intervention Measures
    Oluwasegun M. Ibrahim, Daniel Okuonghae, Monday N.O. Ikhile
    Applied Mathematical Modelling.2022; 107: 39.     CrossRef
  • Emerging Gambling Problems and Suggested Interventions: A Systematic Review of Empirical Research
    Murat Akçayır, Fiona Nicoll, David G. Baxter
    Journal of Gambling Studies.2022;[Epub]     CrossRef
  • Optimal control model for criminal gang population in a limited-resource setting
    Oluwasegun M. Ibrahim, Daniel Okuonghae, Monday N. O. Ikhile
    International Journal of Dynamics and Control.2022;[Epub]     CrossRef
  • Roll the Dice
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2014; 5(5): 243.     CrossRef
  • Summing Up Again
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2014; 5(4): 177.     CrossRef
  • Optimal Implementation of Intervention Strategies for Elderly People with Ludomania
    Byul Nim Kim, M.A. Masud, Yongkuk Kim
    Osong Public Health and Research Perspectives.2014; 5(5): 266.     CrossRef

PHRP : Osong Public Health and Research Perspectives