On the search for efficient face recognition algorithm subject to multiple environmental constraints.

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

Utilizing a multi-stage transition model for analysing child stunting in two urban slum settlements of Nairobi: A longitudinal analysis, 2011-2014.

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 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 nested 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 the 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.

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.

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, [Formula: see text] 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 ([Formula: see text]) 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 ([Formula: see text]: 2.03), Ghana ([Formula: see text]: 1.87), and Somalia ([Formula: see text]: 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.

Challenges associated with the availability, accessibility, and use of antivenoms for treating snakebite envenoming in Ghana: A MaxDiff experiment design.

Successful snakebite envenoming (SBE) treatment requires safe, effective, and quality-assured antivenom products specifically tailored to combat endemic venomous snake species. This study aims to identify the challenges associated with the availability, accessibility, and use of antivenoms for treating SBE. The data for this study were obtained from a cross-sectional study involving healthcare workers from two districts (namely Afram Plains North and Afram Plains South) in the Eastern Region of Ghana. Through the MaxDiff design methodology, we quantify the challenges associated with the availability, accessibility, and use of antivenoms. Responses from a simple random sample of 203 healthcare workers were included in this study. Participants identified the high cost of antivenoms as the most challenging factor that limits the availability, accessibility, and use of antivenoms for treating SBE. Other important challenges were the lack of access to effective antivenoms in remote areas when needed and the increased use of unorthodox and harmful practices, followed by resort to unorthodox and harmful practices and the lack of effective antivenoms to address envenoming from local species in some instances. However, poor outcomes from using substandard antivenoms, stock-outs, inadequate number of manufacturers, and the resort to substandard, cheap, and harmful antivenoms were traded off. Also, poor utilization of antivenoms, suboptimal utilization of antivenoms (low quality, under-dose), use of ineffective, substandard antivenoms, and flooding of the market with products that have not been evaluated thoroughly were underscored. Our findings provide essential data to guide discussions on barriers to the availability, accessibility, and use of antivenoms for treating SBE to improve the supply of antivenoms, enhance the effectiveness of snakebite treatment, and improve patient care quality in Ghana. Multi-component strategies are needed to address the challenges identified, such as intensified advocacy, ongoing education and community engagement, healthcare worker training, and leveraging institutional and governance structures.

The short-term effect of the government of Ghana's decision to open borders at the early-onset of the COVID-19 pandemic.

Non-Pharmaceutical Interventions (NPI) are used in public health to mitigate the risk and impact of epidemics or pandemics in the absence of medical or pharmaceutical solutions. Prior to the release of vaccines, COVID-19 control solely depended on NPIs. The Government of Ghana after assessing early NPIs introduced at the early stage of the pandemic began to ease some restrictions by the opening of international borders with isolation and quarantine measures enforced. It was argued by some experts that this was a hasty decision. In this study, we assessed the impact of the opening of borders to ascertain if this action caused a surge or otherwise in cases in the country. Using data from the database on Africa's records of COVID-19 from the John Hopkins University, the Generalized Linear Model (GLM) time-series regression model for count data was applied to study effects in Ghana during a 4-month and 8-month period post-opening of borders. The study showed that after the decision of the government to open international borders, Ghana's expected case count declined by 72.01 % in the 4-month period and 54.44 % in the 8-month period. This gives an indication of the gradual reversal of the gains made due to the early implementation of NPIs. Notably, this may not only be attributed to the opening of borders but the relaxation of the strict enforcement measures that were put in place at the onset of the pandemic in Ghana. There is therefore the need for continuous enforcement of intervention measures to reduce case counts, particularly with the emergence of new COVID-19 virus strains. The study provides some recommendations for policy and improvements in model building such as developing better data collection system in Ghana, investigating more control variables, estimating the decaying effect of interventions, and ensuring better preparations prior to easing of public health restrictions.

Modeling the Amount of Waste Generated by Households in the Greater Accra Region Using Artificial Neural Networks.

Waste can be defined as solids or liquids unwanted by members of the society and meant to be disposed. In developing countries such as Ghana, the management of waste is the responsibility of the metropolitan authorities. These authorities do not seem to have effective management of the waste situation, and therefore, it is not unusual to see waste clog the drains and litter the streets of the capital city, Accra. The impact of waste on the environment, along with its associated health-related problems, cannot be overemphasized. The Joint Monitoring Programme report in 2015 ranked Ghana as the seventh dirtiest country in the world. The lack of effective waste management planning is evident in the large amount of waste dumped in open areas and gutters that remains uncollected. In planning for solid waste management, reliable data concerning waste generation, influencing factors on waste generation, and a reliable forecast of waste quantities are required. This study used two algorithms, namely, Levenberg-Marquardt and the Bayesian regularization, to estimate the parameters of an artificial neural network model fitted to predict the average monthly waste generated and critically assess the factors that influence solid waste generation in some selected districts of the Greater Accra region. The study found Bayesian regularization algorithm to be suitable with the minimum mean square error of 104.78559 on training data and 217.12465 on test data and higher correlation coefficients (0.99801 on training data, 0.99570 on test data, and 0.99767 on the overall data) between the target variables (average monthly waste generated) and the predicted outputs. House size, districts, employment category, dominant religion, and house type with respective importance of 0.56, 0.172, 0.061, 0.027, and 0.026 were found to be the top five important input variables required for forecasting household waste. It is recommended that efforts of the government and its stakeholders to reduce the amount of waste generated by households be directed at providing bins, increasing the frequency of waste collection (especially in highly populated areas), and managing the economic activities in the top five selected districts (Ledzekuku Krowor, Tema West, Asheidu Keteke, Ashaiman, and Ayawaso West), amongst others.

Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana.

Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.

Statistical Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana.

Most mortality studies usually attribute death to single disease, while various other diseases could also act in the same individual or a population at large. Few works have been done by considering HIV, Tuberculosis (TB), and Hepatitis B (HB) as jointly acting in a population in spite of their high rate of infections in Ghana. This study applied competing risk methods on these three diseases by assuming they were the major risks in the study population. Among all opportunistic infections that could also act within HIV-infected individuals, TB has been asserted to be the most predominant. Other studies have also shown cases of HIV and Hepatitis B coinfections. The validity of these comorbidity assertions was statistically determined by exploring the conditional dependencies existing among HIV, TB, and HB through Bayesian networks or directed graphical model. Through Classification tree, sex and age group of individuals were found as significant demographic predictors that influence the prevalence of HIV and TB. Females were more likely to contract HIV, whereas males were prone to contracting TB.