Mathematical analysis and simulation of Ebola virus disease spread incorporating mitigation measures

Abah, Roseline Toyin; Zhiri, Abraham Baba; Oshinubi, Kayode; Adeniji, Adejimi

doi:10.1016/j.fraope.2023.100066

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…To simulate the process, we utilize data from the Ebola outbreak in Sierra Leone and Guinea. This outbreak began in Guinea in December 2013 and later spread to other West African countries, including Liberia and Sierra Leone, resulting in nearly 30,000 infections from 2014 to 2016 19 – 22 . In this case study, we estimate the dates of occurrence for CP and and calculate the time difference between them.…”

Section: Introductionmentioning

confidence: 99%

A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014–2016)

Kwak,

Sun,

et al. 2024

Sci Rep

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The E/S (exposed/susceptible) ratio is analyzed in the SEIR model. The ratio plays a key role in understanding epidemic dynamics during the 2014–2016 Ebola outbreak in Sierra Leone and Guinea. The maximum value of the ratio occurs immediately before or after the time-dependent reproduction number (Rt) equals 1, depending on the initial susceptible population (S(0)). It is demonstrated that transmission rate curves corresponding to various incubation periods intersect at a single point referred to as the Cross Point (CP). At this point, the E/S ratio reaches an extremum, signifying a critical shift in transmission dynamics and aligning with the time when Rt approaches 1. By plotting transmission rate curves, β(t), for any two arbitrary incubation periods and tracking their intersections, we can trace CP over time. CP serves as an indicator of epidemic status, especially when Rt is close to 1. It provides a practical means of monitoring epidemics without prior knowledge of the incubation period. Through a case study, we estimate the transmission rate and reproduction number, identifying CP and Rt = 1 while examining the E/S ratio across various values of S(0).

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Section: Introductionmentioning

confidence: 99%

A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014–2016)

Kwak,

Sun,

et al. 2024

Sci Rep

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Mathematical modeling of Ebola using delay differential equations

Raza,

Ahmed,

Rafiq

et al. 2024

Model. Earth Syst. Environ.

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Nonlinear delay differential equations (NDDEs) are essential in mathematical epidemiology, computational mathematics, sciences, etc. In this research paper, we have presented a delayed mathematical model of the Ebola virus to analyze its transmission dynamics in the human population. The delayed Ebola model is based on the four human compartments susceptible, exposed, infected, and recovered (SEIR). A time-delayed technique is used to slow down the dynamics of the host population. Two significant stages are analyzed in the said model: Ebola-free equilibrium (EFE) and Ebola-existing equilibrium (EEE). Also, the reproduction number of a model with the sensitivity of parameters is studied. Furthermore, the local asymptotical stability (LAS) and global asymptotical stability (GAS) around the two stages are studied rigorously using the Jacobian matrix Routh–Hurwitz criterion strategies for stability and Lyapunov function stability. The delay effect has been observed in the model in inverse relation of susceptible and infected humans (it means the increase of delay tactics that the susceptibility of humans increases and the infectivity of humans decreases eventually approaches zero which means that Ebola has been controlled into the population). For the numerical results, the Euler method is designed for the system of delay differential equations (DDEs) to verify the results with an analytical model analysis.

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A conformable mathematical model of Ebola Virus Disease and its stability analysis

Abbas,

Zanib,

Ramzan

et al. 2024

Heliyon

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Mathematical analysis and simulation of Ebola virus disease spread incorporating mitigation measures

Cited by 3 publications

References 17 publications

A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014–2016)

A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014–2016)

Mathematical modeling of Ebola using delay differential equations

A conformable mathematical model of Ebola Virus Disease and its stability analysis

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