Search

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Out of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to investigate the conditional correlations between daily returns of 6 currencies of East African countries relative to the US dollar. We fitted the CCC-GARCH, DCC-GARCH and ADCC-GARCH models on the daily returns conditional covariance matrix. The findings of this paper provide evidence that the correlation parameters between the pair of exchange rate returns are significant. This shows that the conditional cor- relations among the six East African countries exchange rate returns change with time. Lastly, this paper provides insight into the nature of correlation among East African currency exchange rates over the sample period. [ABSTRACT FROM AUTHOR]

Area level linear mixed models can be generally applied to produce small area indirect estimators when only aggregated data such as sample means are available. This paper tries to fill an important research gap in small area estimation literature, the problem of constructing confidence intervals (CIs) when the estimated variance of the random effect as well as the estimated mean squared error (MSE) is negative. More precisely, the coverage accuracy of the proposed CI is of the order O(m-3/2), where m is the number of sampled areas. The performance of the proposed method is illustrated with respect to coverage probability (CP) and average length (AL) using a simulation experiment. Simulation results demonstrate the superiority of the proposed method over existing naive CIs. In addition, the proposed CI based on the weighted estimator is comparable with the existing corrected CIs based on empirical best linear unbiased predictor (EBLUP) in the literature. [ABSTRACT FROM AUTHOR]

Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing., Objective: This study aims to determine consistent predictors of HIV testing by applying supervised ML algorithms in repeated adult population-based surveys in South Africa., Methods: A retrospective analysis of multiwave cross-sectional survey data will be conducted to determine the predictors of HIV testing among South African adults aged 18 years and older. A supervised ML technique will be applied across the five cycles of the South African National HIV Prevalence, Incidence, Behavior, and Communication Survey (SABSSM) surveys. The Human Science Research Council (HSRC) conducted the SABSSM surveys in 2002, 2005, 2008, 2012, and 2017. The available SABSSM datasets will be imported to RStudio (version 4.3.2; Posit Software, PBC) to clean and remove outliers. A chi-square test will be conducted to select important predictors of HIV testing. Each dataset will be split into 80% training and 20% test samples. Logistic regression, support vector machines, random forests, and decision trees will be used. A cross-validation technique will be used to divide the training sample into k-folds, including a validation set, and models will be trained on each fold. The models' performance will be evaluated on the validation set using evaluation metrics such as accuracy, precision, recall, F 1 -score, area under curve-receiver operating characteristics, and confusion matrix., Results: The SABSSM datasets are open access datasets available on the HSRC database. Ethics approval for this study was obtained from the University of Johannesburg Research and Ethics Committee on April 23, 2024 (REC-2725-2024). The authors were given access to all five SABSSM datasets by the HSRC on August 20, 2024. The datasets were explored to identify the independent variables likely influencing HIV testing uptake. The findings of this study will determine consistent variables predicting HIV testing uptake among the South African adult population over the course of 20 years. Furthermore, this study will evaluate and compare the performance metrics of the 4 different ML algorithms, and the best model will be used to develop an HIV testing predictive model., Conclusions: This study will contribute to existing knowledge and deepen understanding of factors linked to HIV testing beyond traditional methods. Consequently, the findings would inform evidence-based policy recommendations that can guide policy makers to formulate more effective and targeted public health approaches toward strengthening HIV testing., International Registered Report Identifier (irrid): DERR1-10.2196/59916., (©Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.01.2025.)

Background: In South Africa, there is no centralized HIV surveillance system where key populations (KPs) data, including gay men and other men who have sex with men, female sex workers, transgender persons, people who use drugs, and incarcerated persons, are stored in South Africa despite being on higher risk of HIV acquisition and transmission than the general population. Data on KPs are being collected on a smaller scale by numerous stakeholders and managed in silos. There exists an opportunity to harness a variety of data, such as empirical, contextual, observational, and programmatic data, for evaluating the potential impact of HIV responses among KPs in South Africa., Objective: This study aimed to leverage and harness big heterogeneous data on HIV among KPs and harmonize and analyze it to inform a targeted HIV response for greater impact in Sub-Saharan Africa., Methods: The Boloka data repository initiative has 5 stages. There will be engagement of a wide range of stakeholders to facilitate the acquisition of data (stage 1). Through these engagements, different data types will be collated (stage 2). The data will be filtered and screened to enable high-quality analyses (stage 3). The collated data will be stored in the Boloka data repository (stage 4). The Boloka data repository will be made accessible to stakeholders and authorized users (stage 5)., Results: The protocol was funded by the South African Medical Research Council following external peer reviews (December 2022). The study received initial ethics approval (May 2022), renewal (June 2023), and amendment (July 2024) from the University of Johannesburg (UJ) Research Ethics Committee. The research team has been recruited, onboarded, and received non-web-based internet ethics training (January 2023). A list of current and potential data partners has been compiled (January 2023 to date). Data sharing or user agreements have been signed with several data partners (August 2023 to date). Survey and routine data have been and are being secured (January 5, 2023). In (September 2024) we received Ghana Men Study data. The data transfer agreement between the Pan African Centre for Epidemics Research and the Perinatal HIV Research Unit was finalized (October 2024), and we are anticipating receiving data by (December 2024). In total, 7 abstracts are underway, with 1 abstract completed the analysis and expected to submit the full article to the peer-reviewed journal in early January 2024. As of March 2025, we expect to submit the remaining 6 full articles., Conclusions: A truly "complete" data infrastructure that systematically and rigorously integrates diverse data for KPs will not only improve our understanding of local epidemics but will also improve HIV interventions and policies. Furthermore, it will inform future research directions and become an incredible institutional mechanism for epidemiological and public health training in South Africa and Sub-Saharan Africa., International Registered Report Identifier (irrid): DERR1-10.2196/63583., (©Refilwe Nancy Phaswana Mafuya, Edith Phalane, Amrita Rao, Kalai Willis, Katherine Rucinski, K Alida Voet, Amal Abdulrahman, Claris Siyamayambo, Betty Sebati, Mohlago Seloka, Musa Jaiteh, Lerato Lucia Olifant, Katharine Journeay, Haley Sisel, Xiaoming Li, Bankole Olatosi, Neset Hikmet, Prashant Duhoon, Francois Wolmarans, Yegnanew A Shiferaw, Lifutso Motsieloa, Mashudu Rampilo, Stefan Baral. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 22.01.2025.)

The mean flow of direct survey estimates is mainly concerning the sample adequacy fulfillment unless it has been produced large variance estimates, and therefore, the small area estimations are developed to manage this flaw of the path. Small area estimation improved the direct survey estimates by borrowing strength from the census data and at the same time by using historical data from consecutive surveys. In this paper, we applied the spatiotemporal Fay-Herriot (STFH) model for producing fairly reliable disaggregate-level estimates of undernutrition indicators across all zones. The STFH model is an appropriately fitted model to the undernutrition data since it has the lowest information criteria (IC) value. The spatiotemporal estimates improved both the direct and spatial estimates of undernutrition under the FH model and have brought efficiency gain in the percent coefficient of variation (CV). These results may provide useful information to the government's planners, policymakers, and legislative organs for effective policy formulation and budget allocation in all zones., Competing Interests: There are no conflicts of interest among the authors., (Copyright © 2022 Seyifemickael Amare Yilema et al.)