Journal of Statistical Modeling & Analytics (JOSMA)

Mathematical Modelling of Population Dynamics with Growth Sensitivity of Abuja Federal Territory, Nigeria

Emmanuel Akaligwo — 2025-05-29

Keeping track of the human population is essential for proper planning for facilities such as healthcare, infrastructure, education, and other essential needs. There are various ways by which the government can ensure that service provision is improved and maintained for its citizens and very often this starts by knowing the changes in demography as a function of time. In this work, mathematical modeling and simulations are used to study the population dynamics of Abuja. The models are used for prediction of the population and how its dynamics change over time. With approximate growth rate at 9.3% per annum, the projected population of Abuja will hit 30,220,701 million by the year 2039 all things being equal. Parameter sensitivity analysis was performed using population census data, and the results show a huge influence of variations in the model parameters. The results indicate that the difference between the per capita birth and death rate parameters is crucial for changes in the population. Such findings can also be analogously applied to other cities with a similar population structure and economy.

Modelling the prevalence of some zoonotic diseases among farmers in Benue state using Poisson Autoregessive model

Fater-Mtomga Iveren Blessing Fater-Mtomga — 2025-05-29

The aim of this work is to model the infection rates of some infectious diseases among farmers in Benue state using Poisson autoregressive model. The study utilizes monthly secondary data on serologically confirmed infection cases of Human Immunodeficiency Virus (HIV), tuberculosis (TB), and viral hepatitis (VHP)—the data span from January 2010 to December 2022. The study employs summary statistics and the Anderson-Darling normality test, time plots, bar graphs, and the Poisson autoregressive model as the principal methods of investigation. Results show that HIV, TB, and VHP have positive and increasing trends over time with non-Gaussian tendencies. All three infections peaked between 2017 and 2019 and had their lowest occurrence in 2010, indicating a potential common relationship among them. The results of the PAR (1) model indicate positive time trends in the HIV, TB, and VHP infection rates, suggesting that infections are increasing over time. The results further revealed a significant monthly increase of 2.25% in HIV, 1.22% in TB, and 12.08% in VHP respectively over the study period. The coefficient of determinations of the models explained 83.3%, 78.8%, and 80.8% of the variability in the HIV, TB, and VHP data indicating better fit for all the fitted models. The positive time trend suggests that monitoring the infection rates over time is crucial. Public health strategies need to account for this increasing trend and aim to reverse it.

Evaluating the Impact of Drought on Palm Oil Productions in Malaysia: Insights for Agricultural Modernization

Halimatun Saadiah Md Salehan — 2025-05-29

This study investigates the impact of drought on palm oil production in Malaysia, focusing on Melaka. Using the six-month Standardized Precipitation-Evapotranspiration Index (SPEI-6), it quantifies drought severity and its influence on yields. Malaysia, as a major palm oil producer, faces challenges from climate variability, particularly during dry periods. Statistical analysis reveals a correlation coefficient (R) of 0.725, demonstrating a strong positive relationship between SPEI-6 values and palm oil yields, where wetter conditions correspond to higher yields. The study evaluates the effects of six-month drought intervals, detailing the statistical models and variables analyzed. These findings highlight patterns that can enhance agricultural resilience and resource management in the palm oil sector, particularly in Melaka. By addressing climate-related vulnerabilities, this research provides actionable insights for mitigating climate impacts and strengthening adaptive strategies within Malaysia’s key agricultural sectors.

Modeling Colon Cancer Survival using a Proportional Hazard Mixture Cure Model with Principal Component Covariates

Haruna Suleiman — 2025-05-29

This study explores how gene expression data can help predict the survival times of colon cancer patients. Since the dataset is high-dimensional, Principal Component Analysis (PCA) reduces complexity while retaining essential information. Based on eigenvalue one criteria, proportion of variance accounted for, and scree plot analysis, 60 principal components (PCs) are selected as covariates. These are then used in a Proportional Hazard Mixture Cure Model, applying both Cox and Weibull as baseline models to differentiate between cured and uncured patients over a five-year follow-up period. Maximum Likelihood Estimation (MLE) is applied to estimate the model parameters. The results show that the Cox model provides more reliable estimates, indicated by lower AIC values, higher hazard rates, and statistically significant p-values (<0.05). On the other hand, the Weibull model finds no significant covariates (p-values >0.05), with only the intercept being significant. Furthermore, the Weibull model estimates a 100% cure rate, while the Cox model estimates 56%, suggesting that the Cox model provides a better fit for predicting survival outcomes. By integrating gene expression data into survival modeling, this study offers a more accurate and interpretable way to understand patient outcomes. The findings highlight the Cox mixture cure model as a valuable tool for guiding clinical decisions.

Bayesian Structural Time Series Model and SARIMA Model for Rainfall Forecasting in Nigeria

ROTIMI OGUNDEJI — 2025-05-29

Nigeria is recognized as being susceptible to climate change, and global warming if not taken care of, will lead to serious problems on livelihoods in Nigeria, especially in the area of agricultural activities. Rainfall is a major determinant of climate change the world over and climate change is one of the foremost global challenge facing humans at the moment. Using monthly time series rainfall data, Bayesian structural time series (BSTS) methodology was applied to fit models through MCMC algorithm. Also, Seasonal Autoregressive Moving Average (SARIMA) models were fitted to the same dataset using Box-Jenkins approach. The two models are considered based on their respective capacities to capture trend, seasonal and structural components of rainfall data. On the basis of model evaluation criteria (RMSE, MAE, MAPE and MASE), the SARIMA model had values that were clearly significantly smaller than that of the BSTS time series model. This implies that the SARIMA model is more robust in its estimations and forecasting abilities. Similarly, the R squared was larger for the SARIMA model than the BSTS (MCMC) model indicating that the SARIMA model was a better fit for the rainfall data. This study shows that SARIMA model is a more precise and robust in dealing with this type of dataset than BSTS (MCMC) model. It is better because its computational process using differencing, lags and moving averages ensure that the underlying components of the model are properly identified and estimated.