Maize is one of the most extensively produced cereals in the world. The maize streak virus primarily infects maize but can also infect over 80 other grass species. Leafhoppers are the primary vectors of the maize streak virus. When feeding on plants, susceptible vectors can acquire the virus from infected plants, and infected vectors can transmit the virus to susceptible plants. However, because maize is normally patchy and leafhoppers are mobile, leafhoppers will always be foraging for food. Therefore, we want to look at how leafhoppers interact on maize farms using Holling’s Type III functional response in a Caputo fractal-fractional derivative sense. We show that the proposed model has unique positive solutions within a feasible region. We employed the Newton polynomial scheme to numerically simulate the proposed model to illustrate the qualitative results obtained. We also studied the relationship between the state variables and some epidemiological factors captured as model parameters. We observed that the integer-order versions of the model exaggerate the impact of the disease. We also observe that the increase in the leafhopper infestation on maize fields has a devastating effect on the health of maize plants and the subsequent yield. Furthermore, we noticed that varying the conversion rate of the infected leafhopper leads to a crossover effect in the number of healthy maize after 82 days. We also show the dynamics of varying the maize streak virus transmission rates. It indicates that when preventive measures are taken to reduce the transmission rates, it will reduce the low-yielding effect of maize due to the maize streak virus disease.

It is well established that people with diabetes are more likely to have serious complications from COVID-19. Nearly 1 in 5 COVID-19 deaths in the African region are linked to diabetes. World Health Organization (WHO) finds that 18.3% of COVID-19 deaths in Africa are among people with diabetes. In this paper, we have formulated and analysed a mathematical comorbidity model of diabetes-COVID-19 of the deterministic type. The basic properties of the model were explored. The basic reproductive number, equilibrium points, and stability of the equilibrium points were examined. Sensitivity analysis of the model was carried on to determine the impact of the model parameters on the basic reproductive number of the model. The model had a unique endemic equilibrium point, which was stable for . Time-dependent optimal controls were incorporated into the model with the sole aim of determining the best strategy for curtailing the spread of the disease. COVID-19 cases from March to September 2020 in Ghana were used to validate the model. Results of the numerical simulation suggest a greater number of individuals deceased when the infected individual had an underlying condition of diabetes. More so, the disease is endemic in Ghana with the basic reproduction number found to be . The numerical simulation of the optimal control model reveals the lockdown control minimized the rate of decay of the susceptible individuals whereas the vaccination led to a number of susceptible individuals becoming immune to COVID-19 infections. In all, the two preventive control measures were both effective in curbing the spread of the disease as the number of COVID-19 infections was greatly reduced. We conclude that more attention should be paid to COVID-19 patients with an underlying condition of diabetes as the probability of death in this population was significantly higher.

Research focus on optimal control problems brought on by fractional differential equations has been extensively applied in practice. However, because they are still open ended and challenging, a number of problems with fractional mathematical modeling and problems with optimal control require additional study. Using fractional-order derivatives defined in the Atangana–Baleanu–Caputo sense, we alter the integer-order model that has been proposed in the literature. We prove the solution’s existence, uniqueness, equilibrium points, fundamental reproduction number, and local stability of the equilibrium points. The operator’s numerical approach was put into practice to obtain a numerical simulation to back up the analytical conclusions. Fractional optimum controls were incorporated into the model to identify the most efficient intervention strategies for controlling the disease. Utilizing actual data from Ghana for the months of March 2020 to March 2021, the model is validated. The simulation’s results show that the fractional operator significantly affected each compartment and that the incidence rate of the population rose when $v\ge 0.6$ . The examination of the most effective control technique discovered that social exclusion and vaccination were both very effective methods for halting the development of the illness.

Background: The number of monkeypox cases is rising globally, but it’s unclear how many instances there will be in the near future. The disease has been one of the major problems for sub-Saharan Africans in the past few years. This study seeks to suggest optimal strategies for curbing the disease in Ghana and preventing future occurrences. Methods: A deterministic mathematical model incorporating optimal controls has been developed in this research to investigate the transmission of the monkeypox virus. The model’s fundamental properties such as positivity and boundedness of solution, and basic reproduction number have been examined. In order to assess the efficacy of two preventative control strategies—public education and vaccination—optimal controls were included in the model and Pontragyin’s maximum principle was used to characterize the model. Results: The disease was observed to be not endemic with infection going extinct after ten days. The Monkeypox-related deaths were insignificant. The optimal strategies revealed that public education had less of an effect on those who were vulnerable than the vaccine control strategy. However, both approaches were successful in reducing the number of people who were exposed to the illness and reducing the number of fatalities. Vaccination reduced the number by 32.35% and public education by 50% at the peak of the exposed phase. Additionally, vaccination increases a person’s immunity, which speeds up their recovery. Conclusions: A deterministic classical model incorporating optimal controls was proposed to study the monkeypox virus dynamics in a population. The disease is not endemic, which is explained by the model’s basic reproduction number, which was less than unity. Based on the findings of this study, we advise the use of both strategies in controlling the disease.

Fractional‐order derivative modeling continues to receive great interest among researchers across the globe. In this study, Tuberculosis‐COVID‐19 coinfection is studied using Atangana–Baleanu fractional‐order derivatives defined in Caputo sense. We confirmed the existence and singularity of the solution and investigated the model’s equilibrium points. Additionally, we examined the model’s stability in terms of the Ulam–Hyers and generalized Ulam–Hyers stability criteria. The basic reproduction number R₀ was calculated using the next‐generation matrix approach. We also looked into the model’s disease‐free equilibrium point’s regional stability. Numerical scheme for simulating the fractional‐order system with Mittag–Leffler Kernels are presented. Numerical simulations are given to validate the model. Results of the simulation showed a decline in the number of COVID‐19 infections within the population when the fractional operator was reduced.

The Transportation Model (TM) in the application of Linear Programming (LP) is very useful in optimal distribution of goods. This paper focuses on finding Initial Basic Feasible Solutions (IBFS) to TMs hence, proposing a Demand-Based Allocation Method (DBAM) to solve the problem. This unprecedented proposal goes in contrast to the Cost-Based Resource Allocations (CBRA) associated with existing methods (including North-west Corner Rule, Least Cost Method and Vogel’s Approximation Method) which make cost cell (i.e. decision variable) selections before choosing demand and supply constraints. The proposed ‘DBAM’ on page 4 is implemented in MATLAB and has the ability to solve large-scale transportation problems to meet industrial needs. A sample of five (5) examples are presented to evaluate efficiency of the method. Initial Basic Feasible Solutions drawn from the study (according to DBAM) represent the optimal with higher accuracy, in comparison to the existing methods. Results from the study qualify the DBAM as one of the best methods to solve industrial transportation problems.