Department of Statistics
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Item Utilizing A Multi-Stage Transition Model For Analysing Child Stunting In Two Urban Slum Settlements Of Nairobi: A Longitudinal Analysis, 2011-2014(PLOS ONE, 2024) Oduro, M.S.; Iddi, S.; Asiedu, L.; et al.Introduction Stunting is common among children in many low- and middle-income countries, particularly in rural and urban slum settings. Few studies have described child stunting transitions and the associated factors in urban slum settlements. We describe transitions between stunting and states and associated factors among children living in Nairobi slum settlements. Methods This study used data collected between 2010 and 2014 from the Nairobi Urban and Demographic Surveillance System (NUHDSS) and a vaccination study conducted within the surveillance system. A subset of 692 children aged 0 to 3 years, with complete anthropometric data, and household socio-demographic data was used for the analysis. Height-for-age Z-scores (HAZ) was used to define stunting: normal (HAZ 1), marginally stunted (-2 HAZ). < -1), moderately stunted (-3; HAZ < -2), and severely stunted (HAZ < -3). Transitions from one stunting level to another and in the reverse direction were computed. The associations between explanatory factors and the transitions between four child stunting states were modeled using a continuous-time multi-state model. Results We observed that 48%, 39%, 41%, and 52% of children remained in the normal, marginally stunted, moderately stunted, and severely stunted states, respectively. About 29% transitioned from normal to marginally stunted state, 15% to a moderately stunted state, and 8% to the severely stunted state. Also, 8%, 12%, and 29% back transitioned from severely stunted, moderately stunted, and marginally stunted states to the normal state,respectively. The shared common factors associated with all transitions to a more severe state include: male gender, ethnicity (only for mild and severe transition states), child’s age, and household food insecurity. In Korogocho, children whose parents were married and those whose mothers had attained primary or post-primary education were associated with a transition from a mild state into a moderately stunted state. Children who were breastfed exclusively were less likely to transition from moderate to severe stunting state. Conclusion These findings reveal a high burden of stunting and transitions in urban slums. Context-specific interventions targeting the groups of children identified by the socio-demographic factors are needed. Improving food security and exclusive breastfeeding could potentially reduce stunting in the slums.Item SARS‑CoV‑2 incidence monitoring and statistical estimation of the basic and time‑varying reproduction number at the early onset of the pandemic in 45 sub‑Saharan African countries(BMC Public Health, 2024) Oduro, M.S.; Arhin‑Donkor, S.; Asiedu, L.; Kadengye, D.T.; Iddi, S.The world battled to defeat a novel coronavirus 2019 (SARS-CoV-2 or COVID-19), a respiratory illness that is transmitted from person to person through contacts with droplets from infected persons. Despite efforts to disseminate preventable messages and adoption of mitigation strategies by governments and the World Health Organization (WHO), transmission spread globally. An accurate assessment of the transmissibility of the coronavirus remained a public health priority for many countries across the world to fight this pandemic, especially at the early onset. In this paper, we estimated the transmission potential of COVID-19 across 45 countries in sub-Saharan Africa using three approaches, namely, R0 based on (i) an exponential growth model (ii) maximum likelihood (ML) estimation and (iii) a time-varying basic reproduction number at the early onset of the pandemic. Using data from March 14, 2020, to May 10, 2020, sub-Saharan African countries were still grappling with COVID-19 at that point in the pandemic. The region’s basic reproduction number ( R0 ) was 1.89 (95% CI: 1.767 to 2.026) using the growth model and 1.513 (95% CI: 1.491 to 1.535) with the maximum likelihood method, indicating that, on average, infected individuals transmitted the virus to less than two secondary persons. Several countries, including Sudan ( R0 : 2.03), Ghana ( R0 : 1.87), and Somalia ( R0 : 1.85), exhibited high transmission rates. These findings highlighted the need for continued vigilance and the implementation of effective control measures to combat the pandemic in the region. It is anticipated that the findings in this study would not only function as a historical record of reproduction numbers during the COVID-19 pandemic in African countries, but can serve as a blueprint for addressing future pandemics of a similar nature.Item FaceNet recognition algorithm subject to multiple constraints: Assessment of the performance(Scientific African, 2024) Mensah, J.A.; Appati, J.K.; Boateng, E.K.A.; Ocran, E.; Asiedu, L.Literature has it that the performance of most face recognition algorithms still decline in multiple constrained environments (Occlusions and Expressions), despite the achieved successes of deep learning face recognition algorithms. Using expression variant test face images syn thetically occluded at 30% and 40% rates, the study evaluated the performance of FaceNet deep learning model for face recognition under the aforementioned constraints and when three (3) statistical multiple imputation methods (Multivariable Imputation using Chain Equations (MICE), MissForest and Regularized Expectation Maximization (RegEM)) are adopted for occlu sion recovery. Results of the study showed improved recognition rates of the study algorithm when the imputation-based recovered faces were used for recognition compared with using their multiple constrained counterparts. However, test faces reconstructed with the MissForest imputation method were more accurately recognized using the FaceNet deep learning algorithm. Furthermore, the study demonstrated that some simple augmentation schemes sufficed to further enhance the performance of the FaceNet model. Specifically, the FaceNet algorithms gave the highest average recognition rates (85.19% and 79.5% for 30% and 40% occlusion levels respectively) under augmentation scheme IV (slight rotations, horizontal flipping, shearing, brightness adjustments, and stretching) using MissForest as the de-occlusion mechanism. The study also found that, no disparity existed in its performance with the choice of either Support Vector Machines (SVM) or City Block (CB) for classification under augmentation scheme IV. The study recommends using the MissForest imputation method in dealing with moderately high occluded test faces with varying expressions to enhance the performance of the FaceNet face recognition model.Item On multiple imputation-based reconstruction of degraded faces and recognition in multiple constrained environments(Scientific African, 2023) Mensah, J.A.; Ocran, E.; Asiedu, L.Recognition of degraded frontal face images acquired under occlusion constraints remain challenging despite the plethora of reconstruction mechanisms. Though recent works have lever aged on some imputation mechanisms in this regard, their robustness in multiple constrained environments may not be guaranteed and may be affected by the choice of pre-processing mechanism. This paper proposes enhancement mechanisms that augment or complement the use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images prior to recognition. Experimental results showed that the proposed augmented enhancements improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face images) as compared with DWT in recognizing degraded frontal face images under moderately low levels of occlusions and varying expressions.Item Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach(Hindawi, 2021) Andoh, J.; Asiedu, L.; Lotsi, A.; Chapman-Wardy, C.Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it provides decision-makers with uncensored public views on products, government policies, and programs. Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. The challenge encountered in machine learning method-based sentiment classification still remains the abundant amount of data available, which makes it difficult to train the learning algorithms in feasible time. This eventually degrades the classification accuracy of the algorithms. From this assertion, the effect of training data sizes in classification tasks cannot be overemphasized. This study statistically assessed the performance of Naive Bayes, support vector machine (SVM), and random forest algorithms on sentiment text classification task. The research also investigated the optimal conditions such as varying data sizes, trees, and kernel types under which each of the respective algorithms performed best. The study collected Twitter data from Ghanaian users which contained sentiments about the Ghanaian Government. The data was preprocessed, manually labeled by the researcher, and then trained using the aforementioned algorithms. These algorithms are three of the most popular learning algorithms which have had lots of success in diverse fields. 'e Naive Bayes classifier was adjudged the best algorithm for the task as it outperformed the other two machine learning algorithms with an accuracy of 99%, F1 score of 86.51%, and Matthews correlation coefficient of 0.9906. The algorithm also performed well with increasing data sizes. 'e Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data.Item Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression(Hindawi, 2021) Nortey, E.N.N.; Pometsey, R.; Asiedu, L.; Iddi, S.; Mettle, F.O.Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. +e model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. +e overall accuracy of the fitted model was assessed to be 92%. +rough the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.Item Recognition of Augmented Frontal Face Images Using FFT-PCA/ SVD Algorithm(Hindawi, 2021) Ayiah-Mensah, F.; Asiedu, L.; Mettle, F.O.; Minkah, R.In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. )is study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same (90%) when face images with missingness and augmented face images were used as test images, respectively. )e statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.Item Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images(Hindawi, 2021) Asiedu, L.; Essah, B.O.; Iddi, S.; Doku-Amponsah, K.; Mettle, F.O.The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.Item Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm(Hindawi, 2021) Asiedu, L.; Mensah, J.A.; Ayiah-Mensah, F.; Mettle, F.O.'e drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environments with poor illuminations, varying head poses, ageing, facial expressions, and occlusions. Although several researchers have leveraged on the property of bilateral symmetry to reconstruct half-occluded face images, their approach becomes deficient in the presence of random occlusions. In this paper, we harnessed the benefits of the multiple imputation by the chained equation technique and image denoising using Discrete Wavelet Transforms (DWTs) to reconstruct degraded face images with random missing pixels. Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. 'e study also revealed that the average recognition rate for the augmented face images (75.5811) was significantly lower than the average recognition rate (430.7153) of the occluded face images. MICE augmentation is recommended as a suitable data enhancement mechanism for imputing missing data/pixel of occluded face images.Item An Enhanced Method for Tail Index Estimation under Missingness(Hindawi, 2021) Ayiah-Mensah, F.; Minkah, R.; Asiedu, L.; Mettle, F. O.Extreme events in earthquakes, wind speed, among others are rare but may lead to catastrophic effects on humans and the environment. The primary parameter in the estimation of such rare events is the tail index which measures the tail heaviness of an underlying distribution. Since extreme events are rare, the presence of missing observations may further lead to flawed. In view of this, there is a growing effort by researchers to address this problem. However, the existing methods of estimating the tail index use only the available nonmissing data. Thus, if the missing observations are influential values, ignoring them could introduce more bias and higher mean square error (MSE) in the tail index estimation and subsequently other extreme event– estimators such as high quantiles and small exceedance probabilities. In this study, we propose imputation of the missing observations before applying some standard estimators (Hill and geometric-type) to estimate the tail index. Through a simulation study, we assess the performance of the standard estimators under the proposed data enhancement method and the existing modified estimators of the tail index. The results show that the enhanced estimators have relatively lower bias and MSE. The estimation method was illustrated with a practical dataset on wind speed with missing values. Therefore, we recommend imputation mechanism as viable for enhancing the performance of tail index estimators in the case where there is missingness.