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 R 0 of the model. The model had a unique endemic equilibrium point, which was stable for R 0 > 1 . 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 R 0 = 1.4722 . 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.

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In the method comparison approach, two measurement errors are observed. The classical regression approach (linear regression) method cannot be used for the analysis because the method may yield biased and inefficient estimates. In view of that, the Deming regression is preferred over the classical regression. The focus of this work is to assess the impact of censored data on the traditional regression, which deletes the censored observations compared to an adapted version of the Deming regression that takes into account the censored data. The study was done based on simulation studies with NLMIXED being used as a tool to analyse the data. Eight different simulation studies were run in this study. Each of the simulation is made up of 100 datasets with 300 observations. Simulation studies suggest that the traditional Deming regression which deletes censored observations gives biased estimates and a low coverage, whereas the adapted Deming regression that takes censoring into account gives estimates that are close to the true value making them unbiased and gives a high coverage. When the analytical error ratio is misspecified, the estimates are as well not reliable and biased.

The best mathematical tools for combining numerous inputs into a single result are aggregation operators. The aggregation operators work to combine all of the individual evaluation values provided in a uniform form, and they are very useful for evaluating the options provided in the decision-making process. To provide a larger space for decision makers, complex q -rung orthopair fuzzy rough sets can express their uncertain information. As a generalization of the algebraic operations, the Einstein t -norm and t -conorm, Hamacher operations have become significant in aggregation theory. The Hamacher aggregation operator’s major characteristic is that it can capture the interrelationship between several input arguments. In this article, some Hamacher aggregation operators for complex q -rung orthopair fuzzy rough sets are presented. We define a complex q -rung orthopair fuzzy rough Hamacher operation laws and a new score function. In addition, we propose a serious of averaging aggregation operators for complex q -rung orthopair fuzzy rough set. We present the essential properties of these operators. We use the defined operators and modified EDAS (evaluation based on distance from average solution) method to propose an approach for solving a multicriteria decision making problem. To demonstrate the practicality and effectiveness of our propose model, we consider a numerical example of area selection for an arboretum. Finally, a comparison between the suggested approach with existing operators has been presented for authenticity and reliability.

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. 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 used to characterized the model. Results: Public education was found to have less of an effect on those who were vulnerable than vaccine control. However, both approaches were successful in reducing the number of people who were exposed to the illness and reducing the number of fatalities. 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 vaccine control plan as the most effective preventative measure.