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The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Brazil was hit with four consecutive waves of COVID-19 until 2022 due to the ancestral SARS-CoV-2 (B.1 lineage), followed by the emergence of variants/subvariants. Relative risks of adverse outcomes for COVID-19 patients hospitalized during the four waves were evaluated. Data were extracted from the largest Brazilian database (SIVEP-Gripe), and COVID-19 patients who were hospitalized during the peak of each of the four waves (15-week intervals) were included in this study. The outcomes of in-hospital death, invasive (IMV) and non-invasive (NIV) ventilation requirements, and intensive care unit (ICU) admission were analyzed to estimate the relative risks. A higher risk of in-hospital death was found during the second wave for all age groups, but a significant reduction was observed in the risk of death for the elderly during the third and fourth waves compared to patients in the first wave. There was an increased risk of IMV requirement and ICU admissions during the second wave for patients aged 18-59 years old compared to the first wave. Relative risk analysis showed that booster-vaccinated individuals have lower risks of in-hospital death and IMV requirement in all age groups compared to unvaccinated/partially vaccinated patients, demonstrating the relevance of full/booster vaccination in reducing adverse outcomes for patients who were hospitalized during the variant prevalence.

Studies evaluating the local quality of death certification in Brazil focused on completeness of death reporting or inappropriate coding of causes of death, with few investigating missing data. We aimed to use missing and unexpected values in core topics to assess the quality of death certification in Brazilian municipalities, to evaluate its correlation with the percentage of garbage codes, and to employ a data-driven approach with non-linear models to investigate the association of the socioeconomic and health infrastructure context with quality of death statistics among municipalities. This retrospective study used data from the Mortality Information System (2010-2017), and municipal data regarding healthcare infrastructure, socioeconomic characteristics, and death rates. Quality of death certification was assessed by missing or unexpected values in the following core topics: dates of occurrence, registration, and birth, place of occurrence, certifier, sex, and marital status. Models were fit to classify municipalities according to the quality of death certification (poor quality defined as death records with missing or unexpected values in core topics ≥ 80%). Municipalities with poor quality of death certification (43.9%) presented larger populations, lower death rates, lower socioeconomic index, healthcare infrastructure with fewer beds and physicians, and higher proportion of public healthcare facilities. The correlation coefficients between quality of death certification assessed by missing or unexpected values and the proportion of garbage codes were weak (0.11-0.49), but stronger for municipalities with lower socioeconomic scores. The model that best fitted the data was the random forest classifier (ROC AUC = 0.76; precision-recall AUC = 0.78). This innovative way of assessing the quality of death certification could help quality improvement initiatives to include the correctness of essential fields, in addition to garbage coding or completeness of records, especially in municipalities with lower socioeconomic status where garbage coding and the correctness of core topics appear to be related issues., Competing Interests: Laboratório Sabin de Análises Clínicas SA, NeuralMed LLC, and Samel Serviços de Assistência Médico Hospitalar LTDA provided support in the form of salaries for some of the authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Zimeo Morais et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: The aim of this study was to investigate the prevalence of the main symptoms in Brazilian coronavirus disease 2019 (COVID-19) patients hospitalized during 4 distinct waves, based on their infection with different severe acute respiratory disease coronavirus 2 (SARS-CoV-2) variants., Methods: This study included hospitalized patients who tested positive for SARS-CoV-2 during 15 weeks around the peak of each of 4 waves: W1, ancestral strain/B.1 lineage (May 31 to September 12, 2020); W2, Gamma/P.1 variant (January 31 to May 15, 2021); W3, Omicron variant (December 5, 2021 to March 19, 2022); and W4, BA.4/BA.5 subvariants (May 22 to September 3, 2022). Symptom data were extracted from the Brazilian Severe Acute Respiratory Syndrome Database. Relative risks were calculated, and an analysis of symptom networks was performed., Results: Patients who were hospitalized during the prevalence of the Gamma/P.1 variant demonstrated a higher risk, primarily for symptoms such as fatigue, abdominal pain, low oxygen saturation, and sore throat, than patients hospitalized during the first wave. Conversely, patients who were hospitalized during the predominance of the Omicron variant exhibited a lower relative risk, particularly for symptoms such as loss of smell, loss of taste, diarrhea, fever, respiratory distress, and dyspnea. Similar results were observed in COVID-19 patients who were hospitalized during the wave of the Omicron subvariants BA.4/BA.5. A symptom network analysis, conducted to explore co-occurrence patterns among different variants, revealed significant differential profiles across the 4 waves, with the most notable difference observed between the W2 and W4 networks., Conclusions: Overall, the relative risks and patterns of symptom co-occurrence associated with different SARS-CoV-2 variants may reflect disease severity.

Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

Background and Objective: According to the National Cancer Institute, among all malignant tumors, non-melanoma skin cancer, and melanoma are the most frequent in Brazil. Despite having a lower incidence, the melanoma type has accelerated growth and greater lethality. Several studies have been performed in recent years in the computer vision area to assist in the early diagnosis of skin cancer. Despite being widely used and presenting good results, deep learning approaches require a large amount of annotated data and considerable computational cost for training the model. Therefore, the present work explores active learning approaches to select a small set of more informative data for training the classifier. For that, different selection criteria are considered to obtain more effective and efficient classifiers for skin lesions., Methods: We perform an extensive experimental evaluation considering three datasets and different learning strategies and scenarios for validation. In addition to data augmentation, we evaluated two segmentation strategies considering the U-net CNN model and the Fully Convolutional Networks (FCN) with a manual expert review. We also analyzed the best (handcrafted and deep) features that describe each skin lesion and the most suitable classifiers and combinations (extractor-classifier) for this context. The active learning approach evaluated different criteria based on uncertainty, diversity, and representativeness to select the most informative samples. The strategies used were Decreasing Boundary Edges, Entropy, Least Confidence, Margin Sampling, Minimum-Spanning Tree Boundary Edges, and Root-Distance based Sampling., Results: It can be observed that the segmentation with FCN and manual correction by the specialist, the Border-Interior Classification (BIC) extractor, and the Random Forest (RF) classifier showed a better performance. Regarding the active learning approach, the Margin Sampling strategy presented the best classification accuracies (about 93%) with only 35% of the training set compared to the traditional learning approach (which requires the entire set)., Conclusions: According to the results, it is possible to observe that the selection strategies allow for achieving high accuracies faster (fewer learning iterations) and with a smaller amount of labeled samples compared to the traditional learning approach. Hence, active learning can contribute significantly to the diagnosis of skin lesions, beneficially reducing specialists' annotation costs., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Providing an enabling nurturing care environment for early childhood development (ECD) that cuts across the five domains of the Nurturing Care Framework (i.e., good health, adequate nutrition, opportunities for early learning, security and safety and responsive caregiving) has become a global priority. Brazil is home to approximately 18.5 million children under 5 years of age, of which 13% are at risk of poor development due to socio-economic inequalities. We explored whether the Early Childhood Friendly Municipal Index (IMAPI) can detect inequities in nurturing care ECD environments across the 5570 Brazilian municipalities. We examined the validity of the IMAPI scores and conducted descriptive analyses for assessing sociodemographic inequities by nurturing care domains and between and within regions. The strong correlations between school achievement (positive) and socially vulnerable children (negative) confirmed the IMAPI as a multidimensional nurturing care indicator. Low IMAPI scores were more frequent in the North (72.7%) and Northeast (63.3%) regions and in small (47.7%) and medium (43.3%) size municipalities. Conversely, high IMAPI scores were more frequent in the more prosperous South (52.9%) and Southeast (41.2%) regions and in metropolitan areas (41.2%). The security and safety domain had the lowest mean differences (MDs) among Brazilian regions (MD = 5) and population size (MD = 3). Between-region analyses confirmed inequities between the North/Northeast and South/Southeast. The biggest within-region inequity gaps were found in the Northeast (from -22 to 15) and the North (-21 to 19). The IMAPI distinguished the nurturing care ECD environments across Brazilian municipalities and can inform equitable and intersectoral multilevel decision making., (© 2021 The Authors. Maternal & Child Nutrition published by John Wiley & Sons Ltd.)

Brazil is a country of continental dimensions, where many smaller countries would fit. In addition to demographic, socioeconomic, and cultural differences, hospital infrastructure and healthcare varies across all 27 federative units. Therefore, the evolution of COVID-19 pandemic did not manifest itself in a homogeneous and predictable trend across the nation. In late 2020 and early 2021, new waves of the COVID-19 outbreak have caused an unprecedented sanitary collapse in Brazil. Unlike the first COVID-19 wave, in subsequent waves, preliminary evidence has pointed to an increase in the daily reported cases among younger people being hospitalized, overloading the healthcare system. In this comprehensive retrospective cohort study, confirmed cases of hospitalization, ICU admission, IMV requirement and in-hospital death from Brazilian COVID-19 patients throughout 2020 until the beginning of 2021 were analyzed through a spatio-temporal study for patients aged 20-59 years. All Brazilian federative units had their data disaggregated in six periods of ten epidemiological weeks each. We found that there is a wide variation in the waves dynamic due to SARS-CoV-2 infection, both in the first and in subsequent outbreaks in different federative units over the analyzed periods. As a result, atypical waves can be seen in the Brazil data as a whole. The analysis showed that Brazil is experiencing a numerical explosion of hospitalizations and deaths for patients aged 20-59 years, especially in the state of São Paulo, with a similar proportion of hospitalizations for this age group but higher proportion of deaths compared to the first wave., (© 2022. The Author(s).)

The efficacy of convalescent plasma for coronavirus disease 2019 (COVID-19) is unclear. Although most randomized controlled trials have shown negative results, uncontrolled studies have suggested that the antibody content could influence patient outcomes. We conducted an open-label, randomized controlled trial of convalescent plasma for adults with COVID-19 receiving oxygen within 12 d of respiratory symptom onset ( NCT04348656 ). Patients were allocated 2:1 to 500 ml of convalescent plasma or standard of care. The composite primary outcome was intubation or death by 30 d. Exploratory analyses of the effect of convalescent plasma antibodies on the primary outcome was assessed by logistic regression. The trial was terminated at 78% of planned enrollment after meeting stopping criteria for futility. In total, 940 patients were randomized, and 921 patients were included in the intention-to-treat analysis. Intubation or death occurred in 199/614 (32.4%) patients in the convalescent plasma arm and 86/307 (28.0%) patients in the standard of care arm-relative risk (RR) = 1.16 (95% confidence interval (CI) 0.94-1.43, P = 0.18). Patients in the convalescent plasma arm had more serious adverse events (33.4% versus 26.4%; RR = 1.27, 95% CI 1.02-1.57, P = 0.034). The antibody content significantly modulated the therapeutic effect of convalescent plasma. In multivariate analysis, each standardized log increase in neutralization or antibody-dependent cellular cytotoxicity independently reduced the potential harmful effect of plasma (odds ratio (OR) = 0.74, 95% CI 0.57-0.95 and OR = 0.66, 95% CI 0.50-0.87, respectively), whereas IgG against the full transmembrane spike protein increased it (OR = 1.53, 95% CI 1.14-2.05). Convalescent plasma did not reduce the risk of intubation or death at 30 d in hospitalized patients with COVID-19. Transfusion of convalescent plasma with unfavorable antibody profiles could be associated with worse clinical outcomes compared to standard care., (© 2021. The Author(s).)

This research investigates the drivers of consumption-based carbon emissions in Brazil by using a dataset covering the period between 1990 and 2018. These dynamics were examined by employing the ARDL bounds, DOLS, and gradual shift causality tests. The ARDL long- and short-run estimation outcomes reveal that: (a) renewable energy use stimulates the sustainability of the environment; (b) economic growth increases environmental degradation; and (c) technological innovation enhances the quality of the environment. In addition, the gradual shift causality test results disclosed that renewable energy consumption, economic growth, technological innovation and public-private partnership investment in energy can predict consumption-based carbon emissions in Brazil. Therefore, Brazilian policymakers should actively encourage the R&D of low-carbon technologies and renewable energy consumption. Domestic consumption levels, on the other hand, should be targeted, specifically those that are more energy-intensive and cause a rise in CO 2 emissions due to consumption., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries---China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

We use a controlled experiment to analyze the impact of watching different types of educational traffic campaign videos on overconfidence of undergraduate university students in Brazil. The videos have the same underlying traffic educational content but differ in the form of exhibition. We find that videos with shocking content (Australian school) are more effective in reducing drivers' overconfidence, followed by those with punitive content (American school). We do not find empirical evidence that videos with technical content (European school) change overconfidence. Since several works point to a strong association between overconfidence and road safety, our study can support the conduit of driving safety measures by identifying efficient ways of reducing drivers' overconfidence. Finally, this paper also introduces how to use machine learning techniques to mitigate the usual subjectivity in the design of the econometric specification that is commonly faced in many researches in experimental economics., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: The communication skills of pharmacists are essential for the identification and reduction of patient́s drug related problems. Therefore, Pharmacy courses started the process of teaching Pharmaceutical Care to students in order to improve their communication skills. The use of virtual patients (VP) has been a widely used technique in health care courses, but many of the VP tools in Pharmacy field are in English and do not have clinical cases that are common in tropical countries, such as Brazil. The aim of this work is to describe the PharmaVP system, developed with the purpose of training Latin America students in Pharmaceutical Care. The main differential of PharmaVP is the availability in three languages (Portuguese, English and Spanish) and the possibility of clinical case evolution, simulating several visits made by the patient., Methods: The system was developed according to an incremental and interactive methodology, well suited for conducting multidisciplinary projects. Real clinical cases were collected from a Pharmaceutical Care program and added in PharmaVP to simulate the virtual patients. Then, 31 students of a Pharmacy course were trained and invited to participate of the evaluation study. They used the software and answered adapted instruments that assess the students' acceptance of, use of, learning of, and satisfaction with the system., Results: The results showed that the students found the cases realistic and learned significantly using the software. Another positive point is that the application process of PharmaVP did not consume much time., Discussion: We can conclude that the virtual patient tool contributed to the development of the skills required for the practice of Pharmaceutical Care, but should be used as complementary technique., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)