Leverage effects on Robust Regression Estimators

In this study, we assess the performance of some robust regression methods. These are the least- trimmed squares estimator (LTSE), Huber maximum likelihood estimator (HME), S-Estimator (SE) and modified maximum likelihood estimator (MME) which are compared with the ordinary least squares Estimator (OLSE) at different levels of leverages in the predictor variables. Anthropometric data from Komfo Anokye Teaching Hospital (KATH) was used and the comparison is done using root mean square error (RMSE), relative efficiencies (RE), coefficients of determination (R-squared) and power of the test. The results show that robust methods are as efficient as the OLSE if the assumptions of OLSE are met. OLSE is affected by low and high percentage of leverages, HME broke-down with leverages in data. MME and SE are robust to all percentage of aberrations, while LTSE is slightly affected by high percentage leverages perturbation. Thus, MME and SE are the most robust methods, while OLSE and HME are the least robust and the performance of the LTSE is affected by higher percentages of leverage in this study.   Keywords: Leverages, estimators, power of the test, coefficient of determination, root mean square erro

On Some Compartmental Models for Ebola Disease

In this paper, we consider an epidemic model of Ebola disease which is deadly in its transmission. Local stability analysis of the model equilibria was investigated. We computed the basic reproduction number 〖 R〗_0 using the next generation method. The threshold parameter R_0 was found to be dependent on several hosts of model parameters in determining the stability of an invading epidemic into the population. We have numerically described the model trajectories using Matlab. KEYWORDS: Basic Reproduction number, Ebola virus, Next-generation matrix, Local stability analysis

An SITR Analysis of Treatment Model of Hepatitis B Epidemic

This research article focuses on the formulation of a treatment model of hepatitis epidemic of type B. The dynamics of the model were studied and the local stability analyses of the equilibrium points of the model were investigated. Lyapunov functions were defined for the equilibrium points and their global stabilities were performed..........

Comparative Analysis of Some Structural Equation Model Estimation Methods with Application to Coronary Heart Disease Risk

This study compared a ridge maximum likelihood estimator to Yuan and Chan (2008) ridge maximum likelihood, maximum likelihood, unweighted least squares, generalized least squares, and asymptotic distribution-free estimators in fitting six models that show relationships in some noncommunicable diseases. Uncontrolled hypertension has been shown to be a leading cause of coronary heart disease, kidney dysfunction, and other negative health outcomes. It poses equal danger when asymptomatic and undetected. Research has also shown that it tends to coexist with diabetes mellitus (DM), with the presence of DM doubling the risk of hypertension. The study assessed the effect of obesity, type II diabetes, and hypertension on coronary risk and also the existence of converse relationship with structural equation modelling (SEM). The results showed that the two ridge estimators did better than other estimators. Nonconvergence occurred for most of the models for asymptotic distribution-free estimator and unweighted least squares estimator whilst generalized least squares estimator had one nonconvergence of results. Other estimators provided competing outputs, but unweighted least squares estimator reported unreliable parameter estimates such as large chi-square test statistic and root mean square error of approximation for Model 3. The maximum likelihood family of estimators did better than others like asymptotic distribution-free estimator in terms of overall model fit and parameter estimation. Also, the study found that increase in obesity could result in a significant increase in both hypertension and coronary risk. Diastolic blood pressure and diabetes have significant converse effects on each other. This implies those who are hypertensive can develop diabetes and vice versa

Calculating non-centrality parameter for power analysis under structural equation modelling: An alternative

Identifying the most parsimonious model in structural equation modelling is of utmost importance and the appropriate power estimation methods minimize the probabilities of Type I and Type II errors. The power of a test depends on the sample size, Type I error, degrees of freedom and effect size. In this study, a modified approach of using effect size in calculating the non-centrality parameter for power is proposed. This is compared to the approach in Maccallum et al. (1996) at different degrees of freedom and sample size specifications --- taken from 50 to 2000. As the sample size increased the difference between the power of a test for both methods reduced to zero. The results showed that the values for power of a test are the same for the modified and original approaches for large sample sizes and degrees of freedom. The findings also revealed that the sample discrepancy function ($\hat{F}$) is asymptotically unbiased

Factors influencing information and communication technology knowledge and use among nurse managers in selected hospitals in the Volta region of Ghana

This study adopted a cross-sectional design to examine the factors that influence the use of information and communication technology among 108 nurse managers in selected hospitals in the Volta Region of Ghana. A self-administered questionnaire was used to gather data. A χ 2 test of association identified sex (P <.0001), age (P <.0001), years of work experience (P <.0001), rank of the respondents (P <.0001), computer training (P <.0001), computer ownership (P <.0001), and previous use of computers before appointment as a unit manager (P <.0001) as the factors that significantly influenced the use of information and communication technology among nurse managers. </p

Additional file 1: of Markov chain analysis of the rainfall patterns of five geographical locations in the south eastern coast of Ghana

Supplementary material: Appendix A, B, C, D, E. (DOCX 135 kb

Seroepidemiology of Hepatitis B and C Virus Infections: A Five-Year Retrospective Study among Blood Donors in Saboba District in the Northern Region of Ghana

Background and Objectives. Chronic hepatitis B and C infections are capable of progressing to liver cirrhosis and hepatocellular carcinoma. Globally, it has been estimated that over 2 billion and 170 million people are living with hepatitis B and C infections, respectively. Ghana remains one of the highly endemic countries challenged by the continuous spread of these viral agents in Africa. This study was aimed at determining the seroprevalence and trend of Hepatitis B and C coinfections among blood donors in Saboba District of the Northern Region of Ghana. Methods. A five-year hospital-based retrospective study was carried out among 8605 blood donors comprising 8517 males and 88 females using data on blood donors from Saboba Assemblies of God Hospital located in the Saboba District in the Northern Region of Ghana from 2013 to 2017. Blood bank records on HBV and HCV potential blood donors who visited the hospital to donate blood were retrieved. Donor demographic details, i.e., age and gender, were also recovered. Donors who were registered to the hospital but were not residents of the Northern Region were excluded from the study. Donors with incomplete records were also excluded from the study. The data was managed using Microsoft Excel spreadsheet 2016 and analysed using GraphPad Prism statistical software. Results. The overall prevalence of asymptomatic viral hepatitis B and C infections in the general adult population was 9.59% (95% CI: 9.00-10.20) and 12.71% (95% CI: 12.00-13.40), respectively, with an HBV/HCV coinfection rate of 2.23% (95% CI: 1.90-2.60). The number of donors generally declined with advancement in years from 2038 (23.68%) since 2013 to as low as 1169 (13.59%) in 2016, except for 2017 where a sharp increase of 1926 (22.38%) was observed. The first and second highest proportions of donors fell within the age categories of 20-29 (51.53% (4434)) and 30-39 (32.90% (2831)) respectively. The seroprevalence rate of HBV, HCV, and HBV/HCV coinfection rates were generally higher among the female group than those observed among the male category. The year-to-year variation in HBV, HCV, and HBV/HCV infections was statistically significant. The highest year-to-year HBV seropositivity rate was 11.48% in the year 2013, while that for HCV and HBV/HCV coinfection was 16.24% and 5.85%, respectively, both documented in the year 2014. HBV and HBV/HCV coinfection rates were highest among donors aged <20 years old, while HCV seroprevalence was highest among donors aged 50-59 years old. Significantly higher odds of HBV/HCV coinfection (OR=5.2; 95% CI:3.3-8.1) was observed in the 2014 compared to the year 2013. Donors aged <20years were at higher risks of HBV and HBV/HCV coinfection rates compared to the other age groups. Conclusion. The seroprevalence of HBV and HCV among donors in the Saboba District of the Northern Region of Ghana is endemic. The HBV/HCV coinfection rate also raises serious concern owing to its high prevalence rate among the younger age. Intensive public health education coupled with mobile screening and mass vaccination of seronegative individuals is advised so as to help curb further spread of the infection and in effect help safeguard the health status of potential donors in the district

Using machine learning algorithms to predict COVID-19 vaccine uptake: A year after the introduction of COVID-19 vaccines in Ghana

The impact of vaccine hesitancy on global health is one that carries dire consequences. This was evident during the outbreak of the COVID-19 pandemic, where numerous theories and rumours emerged. To facilitate targeted actions aimed at increasing vaccine acceptance, it is essential to identify and understand the barriers that hinder vaccine uptake, particularly regarding the COVID-19 vaccine in Ghana, one year after its introduction in the country.We conducted a cross-sectional study utilizing self-administered questionnaires to determine factors, including barriers, that predict COVID-19 vaccine uptake among clients visiting a tertiary and quaternary hospital using some machine learning algorithms. Among the findings, machine learning models were developed and compared, with the best model employed to predict and guide interventions tailored to specific populations and contexts. A random forest model was utilized for prediction, revealing that the type of facility respondents visited and the presence of underlying medical conditions were significant factors in determining an individual's likelihood of receiving the COVID-19 vaccine. The results showed that machine learning algorithms can be of great use in determining COVID-19 vaccine uptake