Modeling of Gauss Elimination Technique and AHA Simplex Algorithm for Multi-objective Linear Programming Problems

Asian Journal of Probability and Statistics, Page 1-14
DOI: 10.9734/ajpas/2020/v8i430211

In this research paper, an effort has been made to solve each linear objective function involved in the Multi-objective Linear Programming Problem (MOLPP) under consideration by AHA simplex algorithm and then the MOLPP is converted into a single LPP by using various techniques and then the solution of LPP thus formed is recovered by Gauss elimination technique. MOLPP is concerned with the linear programming problems of maximizing or minimizing, the linear objective function having more than one objective along with subject to a set of constraints having linear inequalities in nature. Modeling of Gauss elimination technique of inequalities is derived for numerical solution of linear programming problem by using concept of bounds. The method is quite useful because the calculations involved are simple as compared to other existing methods and takes least time. The same has been illustrated by a numerical example for each technique discussed here.

COVID-19 Pandemic Data Visualization with Moment about Midpoint: Exploratory and Expository Analyses

Stephen Olusegun Are, Matthew Iwada Ekum

Asian Journal of Probability and Statistics, Page 15-37
DOI: 10.9734/ajpas/2020/v8i430212

Aims: To visualize COVID-19 data using Exploratory Data Analysis (EDA) to tell the COVID-19 story expository.

Study Design: The study uses EDA approach to visualize the COVID-19 data. The study uses secondary data collected from World Health Organization (WHO) in a panel form and partition the world using WHO regions. Moment about a midpoint and EDA are jointly used to analyze the data.

Place and Duration of Study: Department of Mathematics & Statistics, Statistical Laboratory, Lagos State Polytechnic and Federal Polytechnic, Ilaro. The data used covered all regions of the world from January 2020 to July 2020.

Methodology: We included 198 countries (cross-sections) partitioned into 7 WHO regions over 7 months (190 days) time period, spanning 3000 datasets. The EDA and moment about a midpoint is used for the analysis. This is a purely descriptive and expository analysis to tell the story of the novel coronavirus disease (COVID-19).

Results: The total sample points used for this analysis are 30,010, which can be taken as a big data and it is large enough to assume the central limit theorem. The results of the analysis showed that cumulative cases and deaths are increasing but at a slower rate. Some WHO region curves are already flattening.

Conclusion: The study concluded that average number of new cases and new deaths will decrease in coming months but there will be increase in the cumulative cases and deaths but at a slower rate.

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

J. Kisabuli, J. Ong'ala, E. Odero

Asian Journal of Probability and Statistics, Page 38-47
DOI: 10.9734/ajpas/2020/v8i430213

Infant mortality is an important marker of the overall society health. The 3rd goal of the Sustainable Development Goals aims at reducing infant deaths that occur due to preventable causes by 2030. Due to increased infant mortality the Kenyan government introduced Free Maternal Health Care as an intervention towards reducing infant mortality through elimination of the cost burden of accessing medical care by the mother and the infant. The study examines the impact of Free Maternal Health Care on infant mortality using Intervention time series analysis particularly the intervention Box Jenkins ARIMA (Autoregressive Integrated Moving Average) model. There was significant support that Free Maternal Health Care had a significant impact on infant mortality which was estimated to be a decrease of 10.15% in infant deaths per month.

Estimates of Intrinsic Growth Rates and Basic Reproduction Number (R0) for the First Historical Zika Outbreak in Salta, Argentina

Juan Carlos Rosales, Nels´on A. Acosta, Celeste Herrera

Asian Journal of Probability and Statistics, Page 48-60
DOI: 10.9734/ajpas/2020/v8i430214

Scopes and Objectives: After entering South America in May 2015 through northeast Brazil, the Zika virus spread to Argentina between April and June 2016 and reached Salta province the following year. We analyzed some aspects of the first historical outbreak of Zika that occurred in Salta, Argentina, in the year 2017. We obtained elementary estimates, such as the intrinsic growth rate of the cases accumulated in the first weeks of the outbreak, using expressions that relate it to the basic reproduction number thereof. Study design: Retrospective-descriptive studies and relational analysis.

Place and Duration of Study: Department of Mathematics, Faculty of Exact Sciences and Faculty of Engineering. National University of Salta, Argentina, from September 2019 to June 2020.

Methodology: Descriptive and relational analysis. Estimates of parameters and Simulation tests were also carried out in order to qualitatively describe the first Zika historical outbreak in Salta.

Results: Our study revealed that the Zika virus in the province of Salta mainly affects the localities of the departments of Or´an, General San Mart´ın, and Rivadavia, with infection forces α2017 ≈ 0.42 week-1 (SD 0.05) and α2017 ≈ 0.32 week-1 (SD 0.02) with the refined exponential model. On the other hand, we obtained estimates of the basic reproduction number R0 ≈ 1:105 95% CI[1:104 - 1:106] and R0 ≈ 1:111 95% CI[1:110 - 1:112].

Conclusion: Both the values of the estimates of the infection forces and R0 would seem to indicate that the first outbreak of Zika in Salta was of relatively low intensity and of short duration, coinciding with patterns generally present emerging diseases. We found practically no differences with the estimates provided by the two expressions of basic reproduction number used. Although the estimates slightly exceed the threshold value R0 = 1, with respect to other
estimates, we consider them quite reasonable for the first historical outbreak occurred in Salta, since it was short-lived and of little intensity.

Multiple Regression Analysis of Basal Metabolic Rate Using Dataset of 50 Adults at Federal Medical Center, Otuoke Outreach

Sylva Ligeiaziba, Kubugha Wilcox Bunonyo, Jason Biobaragha Goldie

Asian Journal of Probability and Statistics, Page 61-66
DOI: 10.9734/ajpas/2020/v8i430215

This data analysis aimed at investigating Basal Metabolic Rate (BMR) of patients around Otuoke region, in Ogbia Local Government Area, and the data were collated at Federal Medical Centre, Otuoke Outreach. The data collated involving 50 patients, of which, 25 are males and 25 female volunteers of different ages. The variables involved in this analysis include age, gender and basal metabolic index, using SPSS version 25. Descriptive analysis was carried out to summarize the data in terms of mean and standard deviation of the gender and age. Biserial correlation was carried out on gender, age and BMR, and Cohen standard was done to investigate the strength of the relationship between the variables. The results of the analysis showed a negative correlation between gender and BMR with a correlation coefficient of -0.70, indicating a large effect size. In addition, it is seen that the linear regression model is significant, F(2,47) = 25.09, p<0.001, and Rsq = 0.52, indicating 52% variance in BMR. The result goes further to reveal that a unit increase in age doesn’t cause an effect on BMR. However, the female category can significantly predict BMR, B = -267.10, t(47) = -7.06, p<0.001. Based on this sample, this suggests that moving from the Male to Female category of Gender will decrease the mean value of BMR by 267.10 units on average.