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Mathematical Modeling on the Transmission Dynamics of HIV and Hepatitis B (HBV) Co‐Infection in the United States
(Wiley, 2025-06-26) F. A. Oguntolu; O. J. Peter; D. Aldila; G. B. Balogun; A. O. Ajiboye; B. I. Omede
Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) are major public health concern worldwide, contributing to significant morbidity and mortality. Managing co-infection between HIV and HBV presents additional challenges in clinical treatment and patient outcomes. In this article, we developed a comprehensive co-infection model to explore the complex transmission dynamics between HIV and HBV in the United States. Our model incorporates crucial factors such as infection through birth or migration, HBV vaccination, and the possibility of reinfection following HBV recovery. Our mathematical analysis started with the analysis of the two non-co-infection submodels, that is, for HIV-only and HBV-only models. We derived the basic reproduction number for each submodel and applied the Routh-Hurwitz criterion to assess the local stability of their respective disease-free equilibrium points. Our investigation revealed that the HIV-only submodel is globally asymptotically stable when its basic reproduction number remains below one. Conversely, the HBV-only submodel exhibits a backward bifurcation, meaning that both disease-free and endemic equilibrium states can coexist even when the reproduction number falls below one. This phenomenon complicates HBV control strategies under such conditions. However, in the absence of reinfection, the HBV-only model reaches global stability at the disease-free equilibrium whenever its reproduction number is below one. Using center manifold theory, we further demonstrated that the full HIV-HBV co-infection model also undergoes backward bifurcation. A sensitivity analysis was conducted on the basic reproduction numbers of HIV and HBV to identify critical parameters influencing the transmission dynamics of both infections. Our results indicate a positive correlation between the spread of one infection and the prevalence of the other. Additionally, we validated the model by fitting it to annual cumulative data on new HIV cases and reported acute HBV infections in the United States. Numerical simulations suggest that increasing condom use adherence, enhancing treatment coverage for both infections, and boosting HBV vaccination rates can substantially reduce the prevalence of HIV, HBV, and their co-infection.
Mathematical modeling on the dynamics of dengue fever with vaccination and transovarial transmission with real statistical data
(Springer Science and Business Media LLC, 2025-12-29) F. A. Oguntolu; O. J. Peter; O. Babasola; B. I. Omede; G. B. Balogun; A. A. Victor; A. I. Abioye
In this work, we developed a deterministic mathematical model to investigate the transmission dynamics of dengue fever while also incorporating both vaccination and transovarial transmission within the mosquito population. We conducted a mathematical analysis of the model, estimated the basic reproduction number, and examined the stability of the equilibria. By using the Center Manifold Theory, our analysis indicates the potential occurrence of backward bifurcation at the endemic equilibrium. To validate the model, we employed the actual dengue case data from Brazil during the first 30 weeks of 2024. The validation results showed strong agreement between the model projections and the observed data, which was then used for forecasting purposes. A sensitivity analysis was also carried out to identify the parameters with the most significant influence on transmission.
Furthermore, the model was extended to assess two time-dependent control strategies: the use of mosquito bed nets to minimize human exposure and environmental sanitation to eliminate mosquito breeding habitats. Finally, numerical simulations demonstrated that implementing both control strategies concurrently offers a significantly greater reduction in dengue virus transmission than using either intervention individually.
A Fractional Order Model for the Transmission Dynamics of Meningococcal Meningitis With Real Statistical Data
(Wiley, 2026-01-30) F. A. Oguntolu; O. J. Peter; B. I. Omede; G. B. Balogun; Z. O. Dere; S. Qureshi
In this paper, we propose a Caputo-based fractional-order derivative model for the transmission dynamics of meningo coccal meningitis (MM), incorporating the environmental concentration of Neisseria meningitidis as well as factors such as vaccination and the hygiene consciousness of susceptible individuals. The existence and uniqueness of solutions to the model are established using Banach’s and Schauder’s fixed-point theorems. Additionally, we compute the basic reproduction number and examine the local asymptotic stability of the disease-free equilibrium using the Routh–Hurwitz criterion. We analyze the stability of the fractional-order meningitis model using the Ulam–Hyers–Rassias stability method. Furthermore, we fit the model to the cumulative confirmed cases of cere brospinal meningitis in Nigeria using data obtained from the Nigeria Centre for Disease Control (NCDC) to validate the model. The model demonstrates a good fit with the reported cumulative cases. Numerical simulations are conducted for various values of the fractional order. The results reveal an inverse relationship between the fractional order and the total number of asymptomatic infected individuals (carriers), symptomatic infected individuals, and the environmental concentration of Neisseria meningitidis. This implies that increasing the order of the fractional derivative leads to a decrease in the number of infections and bacterial concentration. Moreover, increasing vaccine uptake and improving hygiene consciousness among susceptible individuals significantly reduce both the number of infections and the environmental concentration of Neisseria meningitidis.
Development of Draught Early Warning System (DEWS) in Nigeria: A Review of Progress, Challenges and Future Directions
(ICEC, 2025) AJiboye, Johnson Adegbenga; Ofeoshi, C. I.; Adesiji, A. R.; Saidu, M.
Drought Early Warning Systems (DEWS) are important tools for reducing the impact of drought on agriculture, water resources, and food security. This review explores drought trends in Nigeria, assessing the progress, challenges, and future directions of DEWS development. Analysis of past drought occurrences reveals that Nigeria has experienced notable drought episodes in 1914, 1924, 1935, 1943, 1951-1954, 1972-1973, and 1991-1995, with the driest decades recorded between 1970 and 1990. The increasing trend of drought events is linked to climate change, land degradation, and poor water management. Nigeria's primary DEWS, managed by the Nigerian Meteorological Agency (NiMet), employs indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index. However, these systems face significant challenges, including data gaps, limited technological integration, and inadequate community participation. An analysis of past studies shows advancements in satellite-based vegetation health indices, climate modelling, and machine learning algorithms. However, DEWS effectiveness is hindered by institutional weaknesses, data limitations, and insufficient stakeholder engagement. Key challenges include governance, coordination, funding, and capacity building. Future research should focus on intègrating local knowledge and indigenous practices, developing more complex and integrated DEWS models, improving data quality, and enhancing communication strategies. This review aims to inform policymakers, researchers, and practitioners about the need to strengthen DEWS to support drought resilience and sustainable development in Nigeria.
IoT-based Beverage Fraud Detection: A Theoretical Review
(CUJOSTECH, 2025) AJiboye, Johnson Adegbenga; Usman, A. U.; Oyewobi, S. S.; Abdulbaki, A. O.; SAlihu, B. A.; Mamman, T. A.
This theoretical review explores the foundational theories, frameworks, and models relevant to the design of an Artificial Intelligence (Al) and Internet of Things (IoT)-based system for detecting counterfeit and expired carbonated beverages in the Nigerian market. The growing incidence of beverage fraud, including dilution, mislabeling, and expiration concealment, necessitates the adoption of advanced detection mechanisms. This study explores the use of Bayesian Linear Regression (BLR) to analyze caffeine and CO: concentrations within established theories from food safety, sensor analytics, and machine learning. By comparing and critiquing traditional and modern approaches, the review highlights the strengths of integrated lot and Machine Learning (ML) technologies for scalable, real-time quality monitoring. The key finding from the review is that integrating lot-enabled sensors, BLR, and ML within the Fake Beverages Detection Systems (FaBEDs) framework offers a scalable, real-time, and interpretable approach for detecting counterfeit and expired carbonated beverages, particularly suitable for low-resource settings like Nigeria. This work contributes a critical perspective on how theoretical models can inform practical implementations for safeguarding public health and ensuring beverage supply chain integrity in developing economies.