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The nurse rostering problem describes the task of distributing nurses over working time slots, called shifts, in such a way that the workforce demand for each shift in a scheduling period is met, while ensuring that each nurse is not overttasked or undertasked. This problem is a major issue in Ghana, which this research aims to tackle. To that end, the performance of a heuristic algorithm that showed promise, called the Harmony Search algorithm, is examined. The algorithm, as applied to solving the nurse rostering problem in a hospital in Ghana, was implemented and evaluated with the Python programming language. The results suggest that the algorithm performs well in generating 1-week duty rosters but falters for extended periods, indicating that it may not on its own be potent enough to handle optimization problems. Finally, we outline some recommendations future researchers may want to note., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The study aims to identify relevant variables to improve the prediction performance of the crash injury severity (CIS) classification model. Unfortunately, the CIS database is invariably characterized by the class imbalance. For instance, the samples of multiple fatal injury (MFI) severity class are typically rare as opposed to other classes. The imbalance phenomenon may introduce a prediction bias in favour of the majority class and affect the quality of the learning algorithm. The paper proposes an ensemble-based variable ranking scheme that incorporates the data resampling. At the data pre-processing level, majority weighted minority oversampling (MWMOTE) is employed to treat the imbalanced training data. Ensemble of classifiers induced from the balanced data is used to evaluate and rank the individual variables according to their importance to the injury severity prediction. The relevant variables selected are then applied to the balanced data to form a training set for the CIS classification modelling. An empirical comparison is conducted through considering the variable ranking by: 1) the learning of single inductive algorithm with imbalanced data where the relevant variables are applied to the imbalanced data to form the training data; 2) the learning of single inductive algorithm with MWMOTE data and the relevant variables identified are applied to the balanced data to form the training data; and 3) the learning of ensembles with imbalanced data where the relevant variables identified are applied to the imbalanced data to form the training data. Bayesian Networks (BNs) classifiers are then developed for each ranking method, where nested subsets of the top ranked variables are adopted. The model predictions are captured in four performance indicators in the comparative study. Based on three-year (2014-2016) crash data in Ghana, the empirical results show that the proposed method is effective to identify the most prolific predictors of the CIS level. Finally, based on the inference results of BNs developed on the best subset, the study offers the most probable explanations to the occurrence of MFI crashes in Ghana., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The purpose of this paper is to offer a critical understanding of the motivations and benefits of social media usage by small and medium financial enterprises with an empirical study from an emerging economy such as Ghana. Based on previous studies, the current study hypothesizes that customer-firm relationship, financial expectation, firm's visibility and growth, and market share expectation drive social media usage intensity in the financial industry. Quantitative data was gathered from management and staff of SMEs, specifically, financial institution outlets (having social media channels i.e., Facebook, Instagram, YouTube, LinkedIn, etc.) was used to investigate the proposed conceptual framework. Using structural Equation Modelling, the research showed that customer-firm relationship, financial expectation, firm's visibility and growth, and market share expectation are positively associated with social media usage intensity within the financial sector. Moreover, it was discovered that financial firms that offer physical products only were equally employing social media networks for marketing communication purposes based on cost-effective motives. At the same time, we found that SMEs are more likely to consider 'market share expectation' as a key motivation for social media adoption by the financial industry. The findings from this study have provided some solutions to potential research issues specific to strategic management practices. Limitations and future research directions are discussed in the concluding remarks. [ABSTRACT FROM AUTHOR]

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters. [ABSTRACT FROM AUTHOR]

Background: Mycobacterium ulcerans is know to cause the Buruli ulcer. The association between the ulcer and environmental exposure has been documented. However, the epidemiology of the ulcer is not well understood. A hypothesised transmission involves humans being bitten by the water bugs that prey on mollusks, snails and young fishes., Methods: In this paper, a model for the transmission of Mycobacterium ulcerans to humans in the presence of a preventive strategy is proposed and analysed. The model equilibria are determined and conditions for the existence of the equilibria established. The model analysis is carried out in terms of the reproduction number [Formula: see text]. The disease free equilibrium is found to be locally asymptotically stable for [Formula: see text] The model is fitted to data from Ghana., Results: The model is found to exhibit a backward bifurcation and the endemic equilibrium point is globally stable when [Formula: see text] Sensitivity analysis showed that the Buruli ulcer epidemic is highly influenced by the shedding and clearance rates of Mycobacterium ulcerans in the environment. The model is found to fit reasonably well to data from Ghana and projections on the future of the Buruli ulcer epidemic are also made., Conclusions: The model reasonably fitted data from Ghana. The fitting process showed data that appeared to have reached a steady state and projections showed that the epidemic levels will remain the same for the projected time. The implications of the results to policy and future management of the disease are discussed.

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327-343, ). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana. [ABSTRACT FROM AUTHOR]

Counselling students remains a pre-eminence for most tertiary institutions in Ghana to the extent that institutions now have counselling units that extend to the departmental level. This study used the K-prototype machine learning algorithm to cluster students' behaviour based on 28 relevant attributes and further proposed a classification model. The analysis of the experimental outcomes using the elbow method reveals the formation of three distinct clusters with decreasing intra-cluster similarities and increasing inter-cluster distances. The first cluster uniquely consists of active learners with three or more roommates, primarily in the first year. The second cluster with the highest membership consists mainly of second-year students who exhibit passive classroom conduct and reside in a two-occupancy hostel. The third cluster contains a mixture of third and final-year students who are highly passive in class and live in a tenancy occupancy of two. After clustering, the K-nearest neighbours, logistic regression, naïve Bayes (NB), and AdaBoost ensemble algorithms were implemented to create a model for future learner cluster prediction. Simulation results using the tenfold cross-validation technique show that AdaBoost (NB) has the highest accuracy of 99.88% with an F-measure score of 0.999 and receiver operating characteristic–area under the curve value of 1.00. [ABSTRACT FROM AUTHOR]

Purpose: Knowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana. Design/methodology/approach: The study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable. Findings: The developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan. Originality/value: The developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit. [ABSTRACT FROM AUTHOR]

Background: The RTS,S/AS01 vaccine against Plasmodium falciparum malaria infection completed phase III trials in 2014 and demonstrated efficacy against clinical malaria of approximately 36% over 4 years for a 4-dose schedule in children aged 5-17 months. Pilot vaccine implementation has recently begun in 3 African countries. If the pilots demonstrate both a positive health impact and resolve remaining safety concerns, wider roll-out could be recommended from 2021 onwards. Vaccine demand may, however, outstrip initial supply. We sought to identify where vaccine introduction should be prioritised to maximise public health impact under a range of supply constraints using mathematical modelling.Methods and Findings: Using a mathematical model of P. falciparum malaria transmission and RTS,S vaccine impact, we estimated the clinical cases and deaths averted in children aged 0-5 years in sub-Saharan Africa under 2 scenarios for vaccine coverage (100% and realistic) and 2 scenarios for other interventions (current coverage and World Health Organization [WHO] Global Technical Strategy targets). We used a prioritisation algorithm to identify potential allocative efficiency gains from prioritising vaccine allocation among countries or administrative units to maximise cases or deaths averted. If malaria burden at introduction is similar to current levels-assuming realistic vaccine coverage and country-level prioritisation in areas with parasite prevalence >10%-we estimate that 4.3 million malaria cases (95% credible interval [CrI] 2.8-6.8 million) and 22,000 deaths (95% CrI 11,000-35,000) in children younger than 5 years could be averted annually at a dose constraint of 30 million. This decreases to 3.0 million cases (95% CrI 2.0-4.7 million) and 14,000 deaths (95% CrI 7,000-23,000) at a dose constraint of 20 million, and increases to 6.6 million cases (95% CrI 4.2-10.8 million) and 38,000 deaths (95% CrI 18,000-61,000) at a dose constraint of 60 million. At 100% vaccine coverage, these impact estimates increase to 5.2 million cases (95% CrI 3.5-8.2 million) and 27,000 deaths (95% CrI 14,000-43,000), 3.9 million cases (95% CrI 2.7-6.0 million) and 19,000 deaths (95% CrI 10,000-30,000), and 10.0 million cases (95% CrI 6.7-15.7 million) and 51,000 deaths (95% CrI 25,000-82,000), respectively. Under realistic vaccine coverage, if the vaccine is prioritised sub-nationally, 5.3 million cases (95% CrI 3.5-8.2 million) and 24,000 deaths (95% CrI 12,000-38,000) could be averted at a dose constraint of 30 million. Furthermore, sub-national prioritisation would allow introduction in almost double the number of countries compared to national prioritisation (21 versus 11). If vaccine introduction is prioritised in the 3 pilot countries (Ghana, Kenya, and Malawi), health impact would be reduced, but this effect becomes less substantial (change of <5%) if 50 million or more doses are available. We did not account for within-country variation in vaccine coverage, and the optimisation was based on a single outcome measure, therefore this study should be used to understand overall trends rather than guide country-specific allocation.Conclusions: These results suggest that the impact of constraints in vaccine supply on the public health impact of the RTS,S malaria vaccine could be reduced by introducing the vaccine at the sub-national level and prioritising countries with the highest malaria incidence. [ABSTRACT FROM AUTHOR]

Background. Child mortality is a global health problem. The United Nations' 2018 report on levels and trends on child mortality indicated that under-five mortality is one of the major public health problems in Ghana with a rate of 60 deaths per 1000 live births. To further mitigate this problem, it is important to identify the drivers of under-five mortality in order to achieve the United Nations SDG Goal 3 target 2. Methods. In this study, we investigated the effects of some selected risk factors on child mortality using data from the 2014 Ghana Demographic Health Survey. We modelled the relationship between child mortality and the risk factors using a logistic regression model under the frequentist and Bayesian frameworks. We used the Metropolis-Hastings Algorithm to simulate parameter estimates from the posterior distributions, and statistical analyses were carried out using STATA version 14.1. Results. Results from the frequentist framework are in line with those from the Bayesian framework. The results showed an increased risk of death among children who were delivered through caesarean and reduced relative odds of death among children whose sizes are average or large at birth and whose mothers have formal education. Conclusions. There is a need for improved health facilities for better health-care for mothers and children. Education should, among other things, emphasise on the need for mothers to go for regular check-ups during antinatal and postnatal periods for improved mother and child health. [ABSTRACT FROM AUTHOR]

Background: There is limited evidence about the prevalence of depression among older people in sub-Saharan Africa, about access to treatment or the potential efficacy of community-based interventions. Objective: Using nationally representative data from the WHO SAGE survey, we examine the prevalence of and factors associated with depression among people aged 50 and over in Ghana. Compare self-reported diagnosis and a symptom algorithm to assess treatment gaps and factors associated with the size of gap. Assess the feasibility of a small community-based intervention specifically for older people. Method: Prevalence and treatment data were taken from the WHO SAGE 2007 survey in Ghana, including 4,725 people aged 50 or over. Outcomes of interest were self-reported depression and diagnosis of depression derived from a symptom-based algorithm. The data were subjected to bivariate and multivariate analysis. In parallel, a pilot intervention was conducted with 35 older people, which included screening by a trained psychiatrist and follow-up group sessions of psychotherapy. Results: The symptomatic algorithm reported an overall rate of 9.2 per cent for the study population, with associations with female sex and older age. The treatment gap for these cases was found to be 83.0 per cent. The implementation of the pilot study was perceived as effective and replicable by stakeholders and there was some evidence of enhanced outcomes for people with mild depression. Conclusions: Large numbers of older people in Ghana experience depression, but very few have access to treatment. There is an urgent need to develop and validate community-based services for older people experiencing this condition. [ABSTRACT FROM AUTHOR]

Background: Meticulous identification and investigation of patients presenting with tuberculosis (TB) suggestive symptoms rarely happen in crowded outpatient departments (OPDs). Making health providers in OPDs diligently follow screening procedures may help increase TB case detection. From July 2010 to December 2013, two symptom based TB screening approaches of varying cough duration were used to screen and test for TB among general outpatients, PLHIV, diabetics and contacts in Accra, Ghana.Methods: This study was a retrospective analysis comparing the yield of TB cases using two different screening approaches, allocated to selected public health facilities. In the first approach, the conventional 2 weeks cough duration with or without other TB suggestive symptoms was the criterion to test for TB in attendants of 7 general OPDs. In the second approach the screening criteria cough of >24 hours, as well as a history of at least one of the following symptoms: fever, weight loss and drenching night sweats were used to screen and test for TB among attendants of 3 general OPDs, 7 HIV clinics and 2 diabetes clinics. Contact investigation was initiated for index TB patients. The facilities documented the number of patients verbally screened, with presumptive TB, tested using smear microscopy and those diagnosed with TB in order to calculate the yield and number needed to screen (NNS) to find one TB case. Case notification trends in Accra were compared to those of a control area.Results: In the approach using >24-hour cough, significantly more presumptive TB cases were identified among outpatients (0.82% versus 0.63%), more were tested (90.1% versus 86.7%), but less smear positive patients were identified among those tested (8.0% versus 9.4%). Overall, all forms of TB cases identified per 100,000 screened were significantly higher in the >24-hour cough approach at OPD (92.7 for cough >24 hour versus 82.7 for cough >2 weeks ), and even higher in diabetics (364), among contacts (693) and PLHIV (995). NNS (95% Confidence Interval) varied from 100 (93-109) for PLHIV, 144 (112-202) for contacts, 275 (197-451) for diabetics and 1144 (1101-1190) for OPD attendants. About 80% of the TB cases were detected in general OPDs. Despite the intervention, notifications trends were similar in the intervention and control areas.Conclusion: The >24-hour cough approach yielded more TB cases though required TB testing for a larger number of patients. The yield of TB cases per 100,000 population screened was highest among PLHIV, contacts, and diabetics, but the majority of cases were detected in general OPDs. The intervention had no discernible impact on general case notification. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to propose the Least Absolute Shrinkage and Selection Operators procedure (LASSO) as an alternative to conventional variable selection models, as it allows for easy interpretation and handles multicollinearities. We developed a model on the basis of LASSO-selected parameters in order to link associated demographical, socio-economical, clinical and immunological factors to performing tuberculosis screening in HIV-positive patients in Ghana.Methods: Applying the LASSO method and multivariate logistic regression analysis on a large public health data set, we selected relevant predictors related to tuberculosis screening.Results: One Thousand Ninety Five patients infected with HIV were enrolled into this study with 691 (63.2 %) of them having tuberculosis screening documented in their patient folders. Predictors found to be significantly associated with performance of tuberculosis screening can be classified into factors related to the clinician's perception of the clinical state, as well as those related to PLHIV's awareness. These factors include newly diagnosed HIV infections (n = 354 (32.42 %), aOR 1.84), current CD4+ T cell count (aOR 0.92), non-availability of HIV type (n = 787 (72.07 %), aOR 0.56), chronic cough (n = 32 (2.93 %), aOR 5.07), intake of co-trimoxazole (n = 271 (24.82 %), aOR 2.31), vitamin supplementation (n = 220 (20.15 %), aOR 2.64) as well as the use of mosquito bed nets (n = 613 (56.14 %), aOR 1.53).Conclusions: Accelerated TB screening among newly diagnosed HIV-patients indicates that application of the WHO screening form for intensifying tuberculosis case finding among HIV-positive individuals in resource-limited settings is increasingly adopted. However, screening for TB in PLHIV is still impacted by clinician's perception of patient's health state and PLHIV's health awareness. Education of staff, counselling of PLHIV and sufficient financing are needed for further improvement in implementation of TB screening for all PLHIV. The LASSO approach proved a convenient method for automatic variable selection in a large public health data set that requires efficient and fast algorithms.Trials Registration: ClinicalTrials.gov NCT01897909 (July 5, 2013). [ABSTRACT FROM AUTHOR]

Traditionally within the mining industry, single models for both grade and geology of orebodies are created upon which all mine development decisions are based. These models provide a single interpretation of the extent and continuity of the mineralization envelope based on solids and sections interpreted from relatively widely spaced drilling. The inherent variable behavior of grade and geology cannot be understood from a single estimated resource model. To account for uncertainty in the geology and mineralization envelope, Newmont Mining Corporation uses multiple-point statistics (MPS), an emerging spatial simulation framework, which can be employed to generate multiple, geologically realistic, realizations of data representing attributes of mineral deposits that display complex non-linear features. MPS uses a conceptual model of the geology, termed a training image, to infer these high-order spatial relationships. A detailed application of the MPS algorithm at the structurally controlled Apensu gold deposit, Ghana, demonstrates the practical intricacies of the MPS framework and documents efficiency and effectiveness. Multiple realizations of the Apensu deposit allow for an assessment of the geologic and volumetric uncertainty, which is further combined with grade simulations to generate a more complete picture of the true uncertainty of the deposit. [ABSTRACT FROM AUTHOR]