Background: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective: The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods: Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results: Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions: The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

Digital technologies have transformed how information is accessed and shared; however, individuals with disabilities continue to face significant barriers in engaging with educational content. The shift to electronic formats presents an opportunity to promote inclusion by integrating accessibility features. Accessible e-books are essential for fostering equitable participation in learning environments, supporting lifelong learning, and aligning with global sustainability goals such as SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). While international frameworks like the Web Content Accessibility Guidelines (WCAG) advocate for accessible publishing, significant gaps remain. This paper presents a Systematic Literature Review (SLR) aimed at examining the role of accessibility features in shaping the quality, usability, and affordability of e-books. Based on well-defined guidelines, we defined and executed an SLR protocol, identifying 1618 studies in seven different scientific databases. At the end of the review process, we selected 74 studies to answer four research questions related to the accessibility features employed by e-books and their reading platforms. By synthesizing existing evidence, this review highlights barriers, best practices, and strategic pathways to inform educators, publishers, and policymakers. Ultimately, this study advocates for the development of inclusive and sustainable digital content ecosystems that meet the diverse needs of all learners.