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All Journal Jurnal Diferensial Journal of Computing Theories and Applications
Okedoye, Akindele Michael
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Public Health

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Dynamics of COVID-19 Incorporating Preventive Measures and Treatment Arierhie, Eirene O; Akponana, Eloho B; Egbune, Ngozika J; Okedoye, Akindele Michael
JURNAL DIFERENSIAL Vol 6 No 2 (2024): November 2024
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v6i2.15346

Abstract

The surge of Coronavirus disease (COVID-19) was felt all over the world greatly after it was declared a pandemic in the year 2020. After 3 years in 2023, the disease passed the pandemic phaseand entered an endemic phase. But that didn’t reduce the global threat of the disease as the disease continues to still claim lives daily. In this work, we examined the dynamics of the coronavirusdisease from a mathematical view using a deterministic SEIAISQVRIP LP model. This consists ofinvestigating the disease-free and endemic equilibria, basic reproduction number and stability. Thelocal stability of the disease-free equilibrium was determined by solving the Jacobian matrix of thesystem of the system of differential equations while the basic reproduction number was calculatedusing the next generation matrix method. Numerical simulations to determine the active factor(s) inthe transmission, preventive and possible elimination of the disease were carried out using a computational software called Maple. It was revealed that over time when all modalities are out into place the rate of recovery increases and as the rate of the pathogen virus death increases, the pathogen virus gradually fades from the environment.
Harnessing Artificial Intelligence for Early Disease Detection: Opportunities and Challenges in Modern Healthcare Egbunu, Achile Solomon; Okedoye, Akindele Michael
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15367

Abstract

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.
Co-Authors Akponana, Eloho B Arierhie, Eirene O Egbune, Ngozika J Egbunu, Achile Solomon