Osong Public Health and Research Perspectives

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

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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

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Stéphanie M.C. Abo, Robert Smith
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Data Fitting and Scenario Analysis of Vaccination in the 2014 Ebola Outbreak in Liberia
Zhifu Xie
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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
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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

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12 Download
7 Citations

Abstract 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

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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
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Summing Up Again
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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

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