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All Journal Journal of Multidisciplinary Science: MIKAILALSYS Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics
Ogunmola, Adeniyi Oyewole
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Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Predictive Autoregressive Integrated Moving Average Model; Meningitis Death and Alzheimer Death Ogunmola, Adeniyi Oyewole; Onowuzou, James Oruarooghene; Idi, Danjuma
Asian Journal of Science, Technology, Engineering, and Art Vol 2 No 6 (2024): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v2i6.4126

Abstract

Nigeria, being a developing country and by its limited resources, priority must be placed on deaths due to ill-health-related research. This study aims to investigate the temporal pattern and forecast mortality associated with Alzheimer’s disease and meningitis using ARIMA model techniques. Results showed that the best fitted for the Alzheimer series is the ARIMA (1, 2, 0) model and the best for the meningitis series is the ARIMA(1,1,0) model. The forecasted values revealed that there will be an inconsistent slight decrease in meningitis deaths while there will be an increase in Alzheimer's deaths over the years. For meningitis, the predicted deaths for 2020, 2021, and 2022 respectively, were 45401, 45216, and 45287, and their 95% confidence intervals were obtained. For Alzheimer's, predicted deaths for 2020, 2021, and 2022 respectively, are 12472, 12832, and 13191, and their corresponding 95% confidence intervals were also obtained. Diagnostic checks for the predictive models were carried out and assumptions were sastisfied.
Fitting Linear Probability Model and Logit to Prevalence of Hepatitis B and C Data in Donga Local Government Area of Taraba State Ogunmola, Adeniyi Oyewole; Sambo, Garsama
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5145

Abstract

Hepatitis B and C are significant global public health concerns, responsible for a substantial burden of liver disease. The prevalence of hepatitis B and C varies widely across different populations and regions, influenced by factors such as age, sex, geographic location, and risk behaviors. This study focuses on examining the effects of age and sex on the prevalence of hepatitis B and C. By analyzing data on the presence or absence of these infections across different age categories and between sexes, we aim to identify patterns that could inform targeted public health efforts. Linear probability model and logit through generalised linear model were fitted on the data collected at the first referral hospital laboratory Donga. Results showed that both models fit the data, and significant factors are age category and the interaction of age category and sex. But it is discovered that the logit model fitted the data more with a lower value of AIC and BIC.
A Deterministic Modeling Approach in Identifying the Optimal Screening for Human Immunodeficiency Virus Management Ogunmola, Adeniyi Oyewole; Jolayemi, Emmanuel Teju
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5146

Abstract

The enormous success made in the development of drugs for Human Immunodeficiency Virus (HIV) infection to suppress the viral load of the disease, avert death and suffering due to the disease, no HIV individual is supposed to experience morbidity or death due to the disease. However, most affected people only avail themselves for HIV test at symptomatic stage which leads to morbidity and mortality due to Acquired Immune Deficiency Syndrome (AIDS). In the absence of HIV vaccine for the prevention against HIV infection, HIV screening test would be the next close to vaccination. This aim of this study was to developed a model that determine the optimal HIV screening sequence as intervention for HIV in a population. The concept of screening was brought into system of non-linear differential equations to obtain the deterministic model. The screening sequence and the varying population proportions were used in determining the optimal screening. The findings were that; when the systematic HIV screening of the population was done in six years, mortality and morbidity occurrences were reduced, and subsequent systematic screening reduced morbidity and mortality more in the population; and screening thirty percent of the population every year saved the lives of ninety percent HIV individuals and forestalled ninety percent of them from experiencing morbidity. It was noted also that screening fifty percent of the population three times within six years produced the same effect.
Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria Ogunmola, Adeniyi Oyewole; Okoye, Benjamin Ekene
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.5322

Abstract

Body mass index is a measure of nutritional status of an individual. Malnutrition is a leading public health problem in developing countries like Nigeria, it is also a major cause of morbidity and mortality. In this study, Body mass index is modeled using ordinary least squares method and quantile regression method. Data is collected from Antiretroviral therapy Clinic in Federal Medical Centre, Jalingo. Variables in the data collected are the Body mass index, age, weight, height, sex and occupation of the patients. Results showed that the ordinary least square regression and quantile regression at 25th percentile, median percentile, 75th percentile and 95th percentile fit the data. Weight, age, sex and height of patients are significant in determining the BMI of the patients when OLS method is applied. While weight, sex and height of patients are significant in determining the BMI of the patients. It is also discovered that OLS method fits the data more than quantile regression method using AIC and MSE.
A Poisson Quasi Suja Distribution Ogunmola, Adeniyi Oyewole; Bamigbala, Olateju Alao
Mikailalsys Journal of Advanced Engineering International Vol 2 No 2 (2025): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v2i2.5398

Abstract

Two-parameter Poisson Quasi Suja distribution (PQSD) derived from the two-parameter quasi suja distribution is proposed for extremely positively count data. Its survival and hazard functions, first four raw moments’ measures were expressed. The variance, coefficient of variation, index of dispersion, skewness and kurtosis were also obtained. The impacts of each parameter in the new distribution were assessed.
Application of Linear Probability Model to Road Traffic Crash Ogunmola, Adeniyi Oyewole; Ogebe, Victor Ajibo; Onowuzou, James Oruarooghene
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.5777

Abstract

Road traffic crashes remain a critical public health and safety concern, particularly in developing countries such as Nigeria, where they constitute one of the leading causes of mortality and injury. This study investigates the likelihood that a road traffic crash in each of Nigeria’s six geopolitical zones and in the country as a whole results in a minor incident. Quarterly data on road traffic crashes were sourced from the official database of the Federal Road Safety Corps and analyzed using a linear probability model. The model estimates the probability of a crash being categorized as minor across regions. Findings indicate that the probability of minor road traffic crashes is consistently below 20 percent in all zones and nationally, suggesting that the majority of reported crashes result in major damage or casualties. These results point to a concerning trend in crash severity across Nigeria. The study highlights the urgent need for enhanced traffic safety interventions, stricter enforcement of road regulations, improved vehicle and infrastructure standards, and more effective emergency response systems. Emphasizing preventative strategies and public awareness campaigns could help shift the balance toward more minor, less harmful outcomes when crashes do occur. Ultimately, the goal should be to ensure that in the event of a road traffic crash, the incident remains minor in nature, minimizing harm to life and property.
Estimation of Hypertension Prevalence Among Diabetic Patients with Respect to Certain Covariates Ogunmola, Adeniyi Oyewole; Uhembe, Solomon
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.6365

Abstract

This study investigates the risk factors associated with the prevalence of hypertension among diabetic patients in Jalingo, Taraba State, Nigeria, using logistic regression analysis. The results indicate a significantly high prevalence rate of hypertension among diabetic individuals, estimated at approximately 89.8%, with a 95% confidence interval ranging from 86.3% to 93.3%. The odds of a diabetic patient developing hypertension are about 8.8 times higher than not developing it. Logistic regression analysis identified systolic blood pressure and a family history of diabetes as significant predictors of hypertension. Specifically, a one-unit increase in systolic blood pressure corresponds to a 9.14% increase in the odds of being hypertensive, with the 95% confidence interval for the true odds ratio ranging from 5.59% to 12.82%. Additionally, diabetic patients with a family history of diabetes exhibit a 296.81% higher likelihood of developing hypertension compared to those without such a history, with the confidence interval for this odds ratio spanning from 7.65% to 1362.73%. These findings highlight the importance of monitoring systolic blood pressure and family history as key covariates in predicting hypertension risk among diabetic populations. Overall, the binary logistic regression model demonstrates robust predictive power for identifying hypertensive risk among diabetic patients based on these factors.
Comparing Univariate Time Series Forecast Methods for Malaria Fever Cases Ogunmola, Adeniyi Oyewole; Jibo, Yunusa Namale
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.6366

Abstract

This study evaluates the forecasting accuracy of three univariate time series models, Decomposition, Holt-Winter’s, and Seasonal Autoregressive Integrated Moving Average (SARIMA) for predicting monthly malaria fever cases from January 2008 to December 2024. Data were obtained from the Federal Medical Centre, Jalingo, and analyzed using the three models. Forecasting performance was assessed using Root Mean Square Error (RMSE) as the primary evaluation metric. Among the models, the SARIMA (0, 0, 1) × (1, 1, 2) demonstrated the lowest RMSE, indicating superior forecasting accuracy over the Decomposition and Holt-Winter’s methods. Seasonal trend analysis revealed that malaria fever cases tend to be higher from April to August, with June showing the highest seasonal index representing a 92% increase over the annual average. These findings highlight the SARIMA model’s effectiveness in capturing the seasonal patterns of malaria incidence and its utility for public health planning and intervention.
Co-Authors BAMIGBALA, Olateju Alao Idi, Danjuma Jibo, Yunusa Namale Jolayemi, Emmanuel Teju Ogebe, Victor Ajibo Okoye, Benjamin Ekene Onowuzou, James Oruarooghene Sambo, Garsama Uhembe, Solomon