| 1 |
Author(s):
Elakhe O.A., Isere A.O., Akerejola R.F..
Page No : 1-16
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Mathematical Model of Malaria Transmission with Anti-Malarial Herbal Therapy as Control
Abstract
Conventional anti–malarial drugs (chloroquine, Artesunate, Quinine, Amodiaquine etc) are used by most malaria-endemic countries as first-line treatment for uncomplicated malaria. However, resistance by plasmodium parasite against these conventional anti–malarial drugs has necessitated the need for herbal medicine as alternative. So in this study, we formulate a mathematical model of malaria transmission in two interacting population of human (host) and mosquito (vector) incorporating anti-malarial herbal therapy as first line treatment for uncomplicated malaria infection. The region where the model is epidemiological feasible and mathematically well–posed is established and the basic reproduction number R_0 is derived using next generation matrix approach. The numerical experiment carried out to access the impact of the control measure on malaria transmission revealed a reduction in the number of complicated infectious human population. Hence this research work suggests a massive campaign on use of anti-malarial herbal therapy as first- line treatment for malaria infection cases.
| 2 |
Author(s):
Abegye Shehu Yakubu, Kpanja Sunday Shammah.
Page No : 17-34
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Sensitivity Analysis of Mathematical Modeling of Tuberculosis Dynamics with a Control Measure
Abstract
Tuberculosis is a global threat to human existence. A model to investigate the transmission of tuberculosis was constructed and analysed. The threshold quantity ( R_0) that predicts the existence or extinction of the disease in a population was computed. It was found that the local stability is asymptotically stable when the basic reproduction number is less than unity at the disease-free – equilibrium point. A Lyapunov function was constructed in order to analyse the global stability which was proved to be globally asymptotically stable when the threshold quantity is less or equal to unity. Sensitivity analysis was conducted on the basic reproduction number in order to determine the parameters of the model that are most sensitive as a way to deduce suitable control measures. Numerical simulations are carried out, discussions were made and results are presented in graphical forms.
| 3 |
Author(s):
Samuel Olorunfemi Adams, Davies Abiodun Obaromi, Aminu Ibrahim.
Page No : 35-47
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Modeling the Effect of Population Density and Some Related Factors on Covid-19 Pandemic in Nigeria: An Application of Count Data Regression
Abstract
Aim: Nigeria's population density and other factors like confirmed, admitted, and discharged cases have adversely impacted health behaviors and the management of the COVID-19 pandemic. This study aims to investigate how population, population density, confirmed, admitted, and discharged cases affect the prevalence of the COVID-19 pandemic in the 36 states of Nigeria, including the FCT. Method: The number of COVID-19-related deaths, confirmed, admitted, and discharged individuals, from June 20, 2021, to December 31, 2022, were extracted from the Nigeria Centre for Disease Control (NCDC) online database, while data set on the Nigeria population and density were collected from Nigeria’s National Population Commission (NPC) website. Three count data regression techniques; Poisson, Negative Binomial, and Generalized Poisson Regression models were employed to analyze these count data. Result: It was found that the number of admitted patients has a significant negative impact on COVID-19, whereas the number of confirmed laboratory COVID-19 cases has a significant positive effect on the number of deaths related to COVID-19. Additionally, the result showed that Nigeria's COVID-19 death rate is negatively impacted by discharged cases, population, and population density. Conclusion: It is inferred that the Generalized Poisson Regression model is the most suitable count data regression model for over-dispersion and is the best model for predicting the number of COVID-19-related deaths in Nigeria between June 20, 2021, and December 31, 2022.
| 4 |
Author(s):
Okorie Charity Ebelechukwu, Balansana Kamalu Ibrahim, Bulus Sunday Michael.
Page No : 48-58
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Statistical Analysis of Infant Mortality Rate: A Case Study of Taraba State
Abstract
Reduction of infant mortality has been one of the key issues for both government and individuals. The purpose of this study is to predict and determine the infant mortality rate in the future. To achieve our aim, we used Linear Regression model and student t-test to analyze our data. Data were collected from Specialist Hospital, Jalingo, Taraba State and the analysis was done based on the stated methods in the stated models. The results showed that in 2019, the mortality will be 45 per 1000 and birth will be 124 per 1000; in 2025, the mortality will be 65 per 1000 and birth will be 191 per 1000; in 2030, the mortality will be 90 per 1000 and birth will be 272 per 1000; in 2035, the mortality will be 110 per 1000 and birth will be 340 per 1000. This shows that as the birth is increasing, death is also increasing. From Figure 2, we discovered that mortality is on the increase from year to year. The t-test indicated that there is a linear relationship between infant mortality and birth from 2018–2037, which shows us that t calculated (0.143) < t tabulated (2.021), thereby giving us room not to reject our H0.
| 5 |
Author(s):
Alanamu, T, Oyeyemi, G. M., Olaniran R. O., Adetunji K. O..
Page No : 59-69
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Review of Some Robust Estimators in Multiple Linear Regressions in the Presence of Outlier(s)
Abstract
Linear regression has been one of the most
important statistical data analysis tools. Multiple regression
is a type of regression where the dependent variable shows a
linear relationship with two or more independent variables.
OLS estimate is extremely sensitive to unusual observations
(outliers), with low breakdown point and low e ffi ciency. This
paper reviews and compares some of the existing robust
methods (Least Absolu te Deviation , Huber M Estimator,
Bisquare M Estimator, MM Estimator, Least Median
Square, Least Trimmed Square, S Estimator); a simulation
method is used to compare the selected existing methods. It
was concluded based on the results that for y direction
o utlier, the best estimator in terms of high efficiency and
breakdown point of at most 0.3 is MM; for x direction
outlier, the best estimator in term breakdown point of at
most 0.4 is S; for x, y direction outlier, the best estimator in
terms of high effici ency and breakdown point of at most 0.2
is MM.
| 6 |
Author(s):
L. Ebiwareme, K. W. Bunonyo, O. A. Davies.
Page No : 70-85
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Comparison of Adomian Decomposition Method with Differential Transformation Method for Unsteady MHD Flow and Heat Transfer Over a Stretching/Shrinking Permeable Sheet with Ohmic Heating
Abstract
In this paper, two semi-analytical techniques were implemented to solve a two-dimensional unsteady MHD fluid flow and heat transfer through a stretching/shrinking permeable sheet with ohmic heating and viscous dissipation. The governing flow equations in PDE form were reduced to ordinary differential equations using appropriate similarity transformation. We obtained approximate expressions for the velocity and temperature profiles. Comparative results obtained employing Adomian decomposition method and differential transformation method were benchmarked against a numerical solution using Keller box scheme. Our findings revealed that the approximate analytical solution obtained using DTM is more dependable with fast convergence, highly accurate with minimal calculations and computationally convenient. However, the requirement of Adomian polynomials to tackle the nonlinear terms in ADM makes its execution sometimes cumbersome and difficult.
| 7 |
Author(s):
Joy Ijeoma Adindu-Dick.
Page No : 84-92
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Calculation of a Class of Confluent Hypergeometric Equation and Analysis of its Roles in Option Pricing Models
Abstract
The confluent hypergeometric equation is one of the most important differential equations in physics, chemistry, finance and many more. This work deals with the power series solution of a class of confluent hypergeometric equation with α, a real constant and z, an independent variable. The confluent hypergeometric function of the first kind M(α,α+2,z) is derived together with the second power series solution, M ̃(α,α+2,z). The analysis of the roles of the derived function in option pricing models are given.
| 8 |
Author(s):
Cheikh Tidiane Seck, Ibrahima Faye, Aba Diop, Mouhamed Amine Niang, Seydou Nourou Sylla, Abdourahmane Ndao, Idrissa Sy.
Page No : 93-103
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Prognosis Method on the Outcome of Covid-19 Patients in Senegal
Abstract
There have been disturbing waves of Covid-19 deaths in many countries due to a lack of adequate treatment in the early stages of the pandemic but also to the presence of co-morbidities in many hospitalised patients. This work aims to determine the discriminatory features between the surviving patients and the deceased ones after their hospitalisation to propose a new method of prognosis on the outcome of a new patient under treatment. To this end, we use three supervised classification methods, each allowing us to select the most significant features associated with patient death. These are binary logistic regression (BLR), random forests (RF), and support vector machines (SVM). The data comes from the Ministry of Health and Social Action of Senegal and covers the period from March 2020 to December 2022. Age emerged as the most discriminatory factor between the two patient groups: survivors and deceased. The study found that patients 60 and older are more likely to die of Covid-19.
| 9 |
Author(s):
Abdourahmane Ndao, Cheikh Tidiane Seck.
Page No : 104-114
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Typology of Health Districts Based on Data from Ten Infectious Diseases Under Surveillance in Senegal
Abstract
The aim of this work is to construct a typology of health districts in Senegal based on the distribution frequency of ten infectious diseases under surveillance. Our methodology utilizes HCPC (Hierarchical Classification on Principal Components) algorithm which combines two data analysis techniques, namely Principal Component Analysis (PCA) and Hierarchical Ascending Classification (HAC). The data come from the Prevention Department of the Ministry of Health and Social Action and cover the period from January 2018 to November 2022. The results show that health districts in Senegal can be divided into three clusters according to the number of confirmed cases recorded for each of the ten considered infectious diseases. Moreover, the parangons’ principle allows us to select from the obtained clusters a representative stratified sample of health districts in view to identifying risk factors associated with these ten pathologies.
| 10 |
Author(s):
Harrison O. Amuji, Christy C. Nwachi, Bridget N. Okechukwu, Immaculata O. Okeoma, Sylvester A. Inah.
Page No : 115-127
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Approximating Linear Programming by Geometric Programming and Its Application to Urban Planning
Abstract
In this study, we approximated linear programming by geometric programming; the developed method converts linear programming to geometric programming. We applied the developed method to Neighbourhood planning, a vital aspect of urban planning, and obtained the optimal cost of Neighbourhood designs. The method demonstrated that geometric programming is a robust non-linear optimization model that can be extended to approximate linear optimization problems. This method has obvious advantages in the sense that it allows every decision variable to contribute to the optimal objective function. This is not the case with the known regular Simplex method and the Interior Point Algorithm of solution to linear programming which assign zeros to some variables when the matrix of the non-basic variables is rectangular or when some of the non-basic variables did not enter the basis. The developed method was used to find the global optimal solution, optimal primal and dual decision variables. The solution was better compared to the linear programming method via Simplex method or Interior Point Algorithm because it achieved the global optimal solution. We observed that in addition to achieving the global optimal solution, we obtained the optimal dual decision variables which was absent in the other methods and all the primal decision variables have value against the other methods that assigned some of the variables with zeroes.
