Oke Isaiah Idisi

Brain drain poses a critical threat to sustainable development in many developing nations. This study develops a Caputo‐fractional‐order compartmental model of emigration applied to Nigeria, Pakistan, and Iran. The model distinguishes aspirational and desperation‐driven migrants, as well as successful and unsuccessful outcomes, and incorporates a logistic job‐creation function to link employment opportunities with migration pressures. Analytical results establish the existence, uniqueness, and local stability of solutions, while the basic reproduction number defines the threshold for sustained emigration. Using particle swarm optimization and migration data, parameter estimation and sensitivity analysis identify recruitment, emigration rates, and job creation capacity as key drivers. Simulations highlight country‐specific trends: in Nigeria, limited job creation accelerates outflows; in Iran, structural sociopolitical factors prevail; and in Pakistan, persistent losses reflect poor opportunities. The fractional‐order approach was employed to capture memory effects more effectively than the classical differential approach, offering a robust tool for mathematical model design.

This study develops and analyzes an optimal control model for the transmission dynamics of Mpox across four countries (Nigeria, Spain, Italy, and the Democratic Republic of Congo (DRC)) with a focus on evaluating the cost-effectiveness of awareness, effective treatment, and a combination of both control strategies. Using a compartmental model and Pontryagin’s Maximum Principle, we implement numerical simulations to assess the efficiency indices (EI), total cost (TC), and incremental cost-effectiveness ratios (ICER) for awareness control only, treatment only, and combined control interventions. Results reveal that while combined strategies achieve higher infection prevention, effective treatment only strategies exhibit lower ICER values, indicating greater cost-effectiveness in all countries. These findings highlight that while integrated approaches are epidemiologically impactful, targeted effective treatment interven- tions may offer optimal public health value, particularly in settings with limited healthcare resources like DRC and Nigeria.

This study investigates optimal intervention strategies for controlling the spread of two co-circulating strains of SARS-CoV-2 within the Nigerian population. A newly formulated epidemiological model captures the transmission dynamics of the dual-strain system and incorporates three key control mechanisms: vaccination, non-pharmaceutical interventions (NPIs), and therapeutic treatment. To identify the most effective approach, Pontryagin’s Maximum Principle is employed, enabling the derivation of an optimal control function that minimizes both infection rates and associated implementation costs. Through numerical simulations, this study evaluates the performance of individual, paired, and combined intervention strategies. Additionally, a cost-effectiveness assessment based on the Incremental Cost-Effectiveness Ratio (ICER) framework highlights the most economically viable option, while results suggest that the combined application of vaccination and treatment strategies offers superior control over dual-strain transmission and implementing all three strategies together ensures the most robust suppression of the outbreak.

The emergence of a coronavirus disease (COVID-19) pandemic caused by severe acute respiratorysyndrome coronavirus 2 (SARS-COV 2) has caused an unprecedented global societal andpublic health crisis. A novel variable (genetically resistant humans, which they serve as reservoirhost) that could influence the prevalence of COVID-19 pandemic have been incorporatedand analyzed rigorously. Detection and testing for COVID-19 (in humans that are geneticallyresistant to COVID-19), proper preventive measures, and timely medical care are essential inmanaging and controlling COVID-19 outbreak. First, we analyze the disease-free state of theCOVID-19 model, calculate the baseline control reproduction number, and obtain the localstability of disease-free equilibrium. The model is calibrated using data obtained from NigeriaCentre for Disease Control and key parameters of the model are estimated. Sensitivity analysisis carried out to investigate the influence of the parameters in curtailing the disease. Numericalsimulations are then used to explore the behaviour of the model solutions involving the novelvariable (genetically resistant humans) and control measures. Our results suggest that strictintervention effort (early detection of genetically resistant individuals, testing and isolation ofthe infected individuals) is required for quick suppression of the disease.