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The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: Knearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long-term memory (LSTM) as DL methods. These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo, Brazil. This data consists of 5644 laboratory results from different patients, with 10% being Covid-19 positive cases. The dataset includes 18 attributes that characterizeCOVID-19. We used accuracy, f1-score, recall and precision to evaluate the different developed systems. The obtained results confirmed these approaches' effectiveness in identifying COVID-19, However, ML-based classifiers couldn't performup to the standards achieved byDL-basedmodels. Among all, NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56% and 85% accuracies, respectively. Besides these, DL models attained better performance as CNN, DNN and LSTM secured more than 90% accuracies. The LTSM outperformed all by achieving an accuracy of 96.78% and an F1-score of 96.58%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average. [ABSTRACT FROM AUTHOR]

The transition to remote learning in the context of COVID-19 led to dramatic setbacks in education. Is the return to in-person classes sufficient to eliminate these losses eventually? We study this question using data from the universe of secondary students in São Paulo State, Brazil. We estimate the causal medium-run impacts of the length of exposure to remote learning during the pandemic through a triple-differences strategy, which contrasts changes in educational outcomes across municipalities and grades that resumed in-person classes earlier (already by Q4/2020) or only in 2021. We find that relative learning losses from longer exposure to remote learning did not fade out over time--attesting that school reopening was at the same time key but not enough to mitigate accumulated learning losses in face of persistence. Using observational and experimental variation in local responses across 645 municipalities, we further document that remedial educational policies in the aftermath of the pandemic boosted learning recovery. [ABSTRACT FROM AUTHOR]

Using the example of the city of São Paulo (Brazil), in this paper, we analyze the temporal relation between human mobility and meteorological variables with the number of infected individuals by the COVID-19 disease. For the temporal relation, we use the significant values of distance correlation t 0 (DC), which is a recently proposed quantity capable of detecting nonlinear correlations between time series. The analyzed period was from February 26, 2020 to June 28, 2020. Fewer movements in recreation and transit stations and the increase in the maximal temperature have strong correlations with the number of newly infected cases occurring 17 days after. Furthermore, more significant changes in grocery and pharmacy, parks, and recreation and sudden changes in the maximal pressure occurring 10 and 11 days before the disease begins are also correlated with it. Scanning the whole period of the data, not only the early stage of the disease, we observe that changes in human mobility also primarily affect the disease for 0–19 days after. In other words, our results demonstrate the crucial role of the municipal decree declaring an emergency in the city to influence the number of infected individuals. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic revealed the necessity of measuring the statistical relationship between the transmission rate of epidemic diseases and the social/behavioral, logistical, and economic variables of the affected region. This paper introduces a regression model to estimate the impact of such covariates on the infectious rate of epidemiological agents. Hidden logistical predictor components, such as weekly seasonality of reported data, can also be accessed with the proposed methodology. For this, we assume that the dynamics of officially reported data of emerging pandemics, related to infected groups, follows a stochastic SEIR model. The main advantage of our method is that it is based on a new three-step algorithm that combines the classical likelihood principle, the minimization of the mean squared error, and a tri-section algorithm to estimate, simultaneously, the coefficients of the covariates and the parameters of the compartmental model. Simulation studies are provided to certify the accuracy of the proposed inference methodology. The model is further applied to analyze the official statistical reports of COVID-19 data in the state of São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

Copyright of Cadernos de Gestão Pública e Cidadania is the property of Fundacao Getulio Vargas, Escola Brasileira de Administracao Publica e de Empresas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform. [ABSTRACT FROM AUTHOR]

Featured Application: A new algorithm that uses self-organizing maps and entropy calculus to generate a tree structure based on XAI software principles. Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

This paper reflects upon the everyday agency of marginalized youth in the Brazilian urban periphery in responding to the climate crisis. In the context of the global covid-19 crisis, which has exacerbated patterns of social exclusion between urban centers and peripheries, we reflect on the extent to which current theorizations of youth activism are appropriate for understanding youth agency in the periphery of Sao Paulo. We seek to 'blur the -ism' in climate activism by introducing more multi-faceted understandings of agency based on young people's everyday (inter-)actions with their environment as well as their contradicting emotions regarding their vulnerabilities and potential. [ABSTRACT FROM AUTHOR]

Copyright of Direito da Cidade is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper discusses the complex nature of the pandemic by highlighting the various intrinsic and extrinsic dimensions in the development of SARS-CoV-2 vaccines, with an emphasis on the two most advanced products in clinical testing, namely, the vaccine developed by the University of Oxford associated with the British pharmaceutical company AstraZeneca, and the one developed by Chinese company Sinovac. This choice also stems from the fact that both have testing activities, which, if successful, will lead to future production in Brazil, by Bio-Manguinhos/Fiocruz, Rio de Janeiro, and the Butantã Institute, in São Paulo, respectively. From a conceptual viewpoint, this paper builds on the reflection from the field of Collective Health that addresses the boundaries between the biological and the social spheres. It also seeks to show that, if successful and while important tools for coping with the pandemic, vaccines will not dispense with the continuity of other non-pharmacological measures already used. [ABSTRACT FROM AUTHOR]

With more people staying at home since the COVID-19 outbreak, there has been a growing number of digitally active users vocal about government responses to the pandemic, especially regarding lockdown measures. User-generated content on social networking sites (SNS) has served as a valuable source of information for exploring public attitudes toward the pandemic, as SNS users often reveal their perceptions of government actions through these platforms. SNS have allowed for large-scale data collection from citizens within a specific timeframe and location, making information readily available in real-time. Brazil presents a particularly interesting case, as it suffers from one of the highest mortality rates in Latin America despite having the highest Global Health Security Index (GHSI) in the region. This paper examines public attitudes in São Paulo, Brazil's hardest-hit city, using Twitter as the preferred platform for text mining. Because information on public sentiment toward government lockdown responses may help weigh in on future policy decisions under similar developments, sentiment analysis is then conducted on the data using the VADER model (Valence Aware Dictionary and sEntiment Reasoner) as a way to conceptualize the results. [ABSTRACT FROM AUTHOR]

This paper presents the results of the research nested in the international project "Gender and COVID-19", which includes several topics related to the impact of the pandemic on the lives of women and their families, including food insecurity and hunger. Semi-structured interviews were conducted from December 2020 to November 2021 with 49 women living in two urban conglomerates, Cabana do Pai Tomás (Belo Horizonte, MG) and Sapopemba (São Paulo, SP), and two rural quilombola communities, Córrego do Rocha (Chapada do Norte, MG) and Córrego do Narciso (Araçuaí, MG). The analyses were based on the following categories: hunger-related feelings and terms; reduced food amount and quality; lack of food and nutrients; difficulties producing food, receiving emergency aid or food donations; governments evaluation and support networks. The respondents' reports show the challenges they experienced, their coping methods, and criticism of the government's lack of responses. Besides presenting a gender perspective, women, especially the leaders who worked in the construction of solidarity networks, are fundamental voices in planning actions to prevent and mitigate the impacts of emergencies in their communities. [ABSTRACT FROM AUTHOR]

This paper contributes to discussions about subnational responses to the coronavirus disease (COVID-19) pandemic in federal countries. In the scholarship on federalism and public policy, few studies seek to understand the factors that shape subnational differences in welfare levels. This article seeks to better understand this issue in Brazil by exploring how, in a context with little national-level coordination, subnational governments tackle the inequalities exacerbated by the COVID-19 pandemic. This study analyzes two social policy areas with distinct national-level coordination mechanisms and federal responsibilities: social assistance and education. Two multi-level cases are examined: the states of Amazonas and São Paulo and the cities of Manaus and São Paulo. This analysis relies on quantitative data, mainly social indicators, and qualitative data collected through documents and in-depth interviews. Social assistance and education policy actors in Amazonas and São Paulo faced at times significant obstacles adapting and/or creating policies to tackle inequalities, resulting in a fend-foryourself federalism and fragmented subnational policies. Differences in subnational responses can be explained by distinct policy legacies and previous capacity, which were key in organizing a useful response to the pandemic. However, to fully explain subnational responses, the role of actors within institutional contexts must also be taken into account. In social assistance, shared responsibilities among different levels of government led to competition and credit claiming dynamics, reinforcing fragmented and uncoordinated responses. In education, decentralization and more stable funding allowed political leadership to activate and mobilize subnational capacities and other actors at the subnational level, producing more sustainable responses. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Saúde e Sociedade is the property of Universidade de Sao Paulo, Faculdade de Saude Publica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study employs surface and remote sensing data jointly with deep learning techniques to examine the influence of vehicular traffic in the seasonal patterns of CO, NO2, PM2.5, and PM10 concentrations in the São Paulo municipality, as the period of physical distancing (March 2020 to December 2021), due to SARS-CoV-2 pandemic and the resumption of activities, made it possible to observe significant variations in the flow of vehicles in the city of São Paulo. Firstly, an analysis of the planetary boundary layer height and ventilation coefficient was performed to identify the seasons' patterns of pollution dispersion. Then, the variations (from 2018 to 2021) of the seasonal average values of air temperature, relative humidity, precipitation, and thermal inversion occurrence/position were compared to identify possible variations in the patterns of such variables that would justify (or deny) the occurrence of more favorable conditions for pollutants dispersion. However, no significant variations were found. Finally, the seasonal average concentrations of the previously mentioned pollutants were compared from 2018 to 2021, and the daily concentrations observed during the pandemic period were compared with a model based on an artificial neural network. Regarding the concentration of pollutants, the primarily sourced from vehicular traffic (CO and NO2) exhibited substantial variations, demonstrating an inverse relationship with the rate of social distancing. In addition, the measured concentrations deviated from the predictive model during periods of significant social isolation. Conversely, pollutants that were not primarily linked to vehicular sources (PM2.5 and PM10) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series. [ABSTRACT FROM AUTHOR]

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread. [ABSTRACT FROM AUTHOR]

São Paulo is the most populous state in Brazil, home to around 22% of the country's population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country's fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model's coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic primarily impacted long-term care facilities by restricting visiting and circulation of visitors, affecting the quality of life (QoL) of older adults living in these institutions. Volunteer activities, essential for older adults' daily life, were also interrupted and potentially negatively impacted the QoL of older adults, volunteers themselves, and also employees in these institutions. In this context, this study aims to evaluate the impact of the return of volunteer-led activities in a long-term care institution on the QoL of older adult residents, employees, and volunteers. Methods: This study used a pre-test and post-test design within the same group. The first round of data collection was conducted before volunteer-led activities returned and the second round after 1 month of return. The instrument used to assess QoL was the EUROHIS-QoL-8 scale. This study was conducted within a nursing home in São Paulo, Brazil, created in 1937 by members of the Israeli community living in Brazil. Volunteer-led activities were part of residents' daily life before the COVID-19 pandemic, when these activities were interrupted for about 20 months. A total of 79 individuals participated in both rounds (pre and post), of which: 29 residents, 27 volunteers, and 23 employees of the long-term care institution. Results: Using a Wilcoxon signed-rank test, the analyses indicated improvements after the 1 month return in different QoL aspects for the three groups. Volunteers improved their personal relationships (Z − 2.332, p <.05), residents their overall health (Z − 2.409, p <.05) and employees in their overall QoL perception (Z − 2.714, p <.05). Influencing factors for residents were the number of activities (3 or more), gender (male), and education (undergraduate/graduate). For employees, those who assumed additional activities due to the volunteer-led activities interruption had a significant impact on their overall QoL. Conclusions: Evidence from this study suggests that volunteers' return positively impacted different QoL aspects for volunteers, residents, and employees. [ABSTRACT FROM AUTHOR]

The seventh human coronavirus was discovered and reported primarily in Wuhan, China. After intense seasons with repercussions in all areas of humanity, the pandemic demonstrates a new perspective. In Brazil, the pandemic concept had impacts in vast areas, including healthcare hospitals. This present study aims to describe and synthesize data from a determined period from the year 2021 that correlate the symptoms of passive and/or active patients for COVID-19 and their respective results of IgG/IgM serological tests in hospitals in the city of Cruzeiro, São Paulo, Brazil. The form had been applied to 333 people and obtained conclusive results and several symptoms were presented; in addition, asymptomatic cases were also analyzed and directed in the genomic study of variants of concern, as well as vaccination data in the study region. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine whether Post-acute Sequelae of SARS-CoV-2 Infection (PASC) are associated with physical inactivity in COVID-19 survivors. This is a cohort study of COVID-19 survivors discharged from a tertiary hospital in Sao Paulo, Brazil. Patients admitted as inpatients due to laboratory-confirmed COVID-19 between March and August 2020 were consecutively invited for a follow-up in-person visit 6 to 11 months after hospitalization. Ten symptoms of PASC were assessed using standardized scales. Physical activity was assessed by questionnaire and participants were classified according to WHO Guidelines. 614 patients were analyzed (age: 56 ± 13 years; 53% male). Frequency of physical inactivity in patients exhibiting none, at least 1, 1–4, and 5 or more symptoms of PASC was 51%, 62%, 58%, and 71%, respectively. Adjusted models showed that patients with one or more persistent PASC symptoms have greater odds of being physically inactive than those without any persistent symptoms (OR: 1.57 [95% CI 1.04–2.39], P = 0.032). Dyspnea (OR: 2.22 [1.50–3.33], P < 0.001), fatigue (OR: 2.01 [1.40–2.90], P < 0.001), insomnia (OR: 1.69 [1.16–2.49], P = 0.007), post-traumatic stress (OR: 1.53 [1.05–2.23], P = 0.028), and severe muscle/joint pain (OR: 1.53 [95% CI 1.08–2.17], P = 0.011) were associated with greater odds of being physically inactive. This study suggests that PASC is associated with physical inactivity, which itself may be considered as a persistent symptom among COVID-19 survivors. This may help in the early identification of patients who could benefit from additional interventions tailored to combat inactivity (even after treatment of PASC), with potential beneficial impacts on overall morbidity/mortality and health systems worldwide. [ABSTRACT FROM AUTHOR]

Copyright of Gestão e Regionalidade is the property of Universidade Municipal de Sao Caetano do Sul and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brazil experienced one of the fastest increasing numbers of coronavirus disease (COVID-19) cases worldwide. The Sao Paulo State (SPS) reported a high incidence, particularly in Sao Paulo municipality. This study aimed to identify clusters of incidence and mortality of hospitalized patients with severe acute respiratory syndrome for COVID-19 in the SPS, in 2020-2021, and describe the origin flow pattern of the cases. Cases and mortality risk area clusters were identified through different analyses (spatial clusters, spatio-temporal clusters, and spatial variation in temporal trends) by weighting areas. Ripley's K12-function verified the spatial dependence between the cases and infrastructure. There were 517,935 reported cases, with 152,128 cases resulting in death. Of the 470,441 patients hospitalized and residing in the SPS, 357,526 remained in the original municipality, while 112,915 did not. Cases and death clusters were identified in the Sao Paulo metropolitan region (SPMR) and Baixada Santista region in the first study period, and in the SPMR and the Campinas, Sao Jose do Rio Preto, Barretos, and Sorocaba municipalities during the second period. We highlight the priority areas for control and surveillance actions for COVID-19, which could lead to better outcomes in future outbreaks. [ABSTRACT FROM AUTHOR]

Background: Risk factors for severe SARS-CoV2 infection in pregnancy have not been extensively studied. This information can help guide the management of pregnant women with COVID-19. Objective: Examine risk factors for severe COVID-19 in pregnant women. Methods: We reviewed the records of all pregnant women with positive SARS-CoV2 tests (qRT-PCR) managed at a single tertiary private maternity in Sao Paulo, Brazil. We categorized women as having non-severe (mild or moderate) or severe (severe or critical) COVID-19. We conducted multivariable analyses to identify differences in sociodemographic and clinical characteristics of the two groups as risk factors for severe COVID-19. Results: Between March 13 and June 7 2020, 114 women tested positive for SARS-CoV-2; 80.7% (n = 92) had non-severe disease (69 mild, 23 moderate), 15.7% (n = 18) had severe, and 3.5% (n = 4) had critical COVID-19. Women with severe/critical COVID-19 (n = 22) were significantly older (35.0 ± 5.9 × 31.8 ± 5.1 years, p = 0.011), more likely to have at least one medical comorbidity (81.8% × 52.2%, p = .011) or a history of asthma (18.2% × 3.3%, p = .025), and tended to have a higher median body mass index (30.1 kg/m2, IQR 28.1–33.9 × 28.6, IQR 26.2–32.0, p = .056) than women with non-severe disease. Multivariate logistic regression analysis identified four factors as independent predictors of severe/critical COVID-19: asthma (OR 34.469, 95% CI 1.151–78.030, p = .026), non-white ethnicity (OR 7.932, 95% CI 1.311–47.973, p = .024), maternal age with a best cutoff of ≥ 34 years (OR 1.195, 95% CI 1.001–1.427, p = .048) and gestational age at diagnosis with a best cut-off of ≥ 35 weeks (OR 0.876, 95% CI 0.780–0.983, p = .025). The predictive value of the model including all variables was 0.823 (p < .001). Conclusion: A history of asthma, non-white ethnicity, and older maternal age were risk factors for, while higher gestational age was protective against severe/critical COVID-19 in pregnant Brazilian women. [ABSTRACT FROM AUTHOR]

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately. [ABSTRACT FROM AUTHOR]

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020 led to a significant change in human behaviors, mainly because of the quarantine to avoid the spread of the virus. Measures affected both economic activities and citizens' behaviors as they developed more intense hygiene habits to avoid contamination and switched to home offices. These exceptional behaviors also affected the way that water is consumed and need to be fully understood to manage supply systems. Therefore, this study aims to investigate changes in residential and commercial water consumption in 31 municipalities in the state of São Paulo, Brazil, during SARS-CoV-2. To do this, the expected consumption for the first half of 2020 was forecasted using the Holt-Winters multiplicative method and compared with the data observed for the same period. In addition, we compared monthly records of new contaminations and the social distancing index to establish a correlation with changes in water consumption. The results show an average difference between forecasted and observed consumption equal to +6.23% and −18.59% for residential and commercial activities, respectively. For the first one, the consumption per capita increased at the rate of 8.44 L.person−1.day−1. The observed changes in consumption seem to be a consequence of hygiene habits, social distancing and the closing of nonessential services in commerce. [ABSTRACT FROM AUTHOR]

aime Barbosa,3 Marjorie de Assis Golim,2 André Luís Balbi Snr11Department of Internal Medicine, University Estadual Paulista (UNESP), Botucatu, São Paulo, Brazil; 2Clinical Hospital of Botucatu Medical School, Botucatu, São Paulo, Brazil; 3Department of Infectious Disease, University Estadual Paulista (UNESP), Botucatu, São Paulo, BrazilCorrespondence: Daniela Ponce, Department of Internal Medicine – UNESP, Univ Estadual Paulista, Rubião Jr, s/n, Botucatu, SP, 18.618-970, Brazil, Tel/Fax +55 14 3811-6000, Email [email protected] Background: In the absence of direct therapy for COVID-19, extracorporeal blood treatment (EBT) could represent an option for cytokine removal. Objective: This study aimed to describe and compare cytokine removal during intermittent haemodialysis (IHD) and continuous renal replacement therapy (CRRT) in COVID-19 patients with Acute Kidney Injury (AKI). Methods: It was a cohort study that studied patients with COVID-19-related AKI according to KDIGO criteria and admitted at Intensive Care Unit (ICU). Blood samples were collected at the start and end of both IHD using high flux (HF) membranes (10 patients) and continuous venovenous haemodiafiltration (CVVHDF:10 patients) in two sessions for measuring 13 different plasma interleukins and calculating the cytokine removal rate. Results: There was no difference between the two groups regarding mechanical ventilation, vasoactive drug, age or prognostic scores. Patients treated by CRRT presented higher levels of IL-2 and IL-8 than patients treated by IHD at dialysis start. Cytokine removal ranged from 9% to 78%. Patients treated by CRRT presented higher cytokine removal for IL-2, IL-6 IL-8, IP-10 and TNF. The removal rates of IL-4, IL-10, IL-17A, IFN, MCP-1 and TGF-B1 were similar in two groups. After one session of CVVHDF (24 h), IL-2 and IL-1β levels did not vary significantly, whereas IL-4, IL-6, IL-8, IL-10, IL-17A, TNF, IFN, IP-10, MCP-1, IL-12p70 and TGF-B1 decreased by 33.8– 76%, and this decrease was maintained over the next 24 h. In IHD groups, IL-2, IL-6, TNF, IP-10 and IL-1β levels did not decrease significantly whereas IL-4, IL-8, IL-10, IL-17A, IFN, MCP-1, IL-12p70 and TGF-B1 decreased by 21.8– 72%; however, cytokine levels returned to their initial values after 24 h. Conclusion: Cytokine removal is lower in IHD using HF membranes than in CVVHDF, and in IHD the removal is transient and selective, which can be associated with mortality during cytokines storm-related COVID-19. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) epidemic is an unprecedented global health crisis and the effects may be related to environmental and socio-economic factors. In São Paulo, Brazil, the first death occurred in March 2020 and since then the numbers have grown to 175 new deaths per day in April 2021, positioning the city as the epicenter of the number of cases and deaths in Brazil. São Paulo is one of the largest cities in the world with more than 12 million inhabitants, a fleet of about 8 million vehicles and frequent pollutant concentrations above recommended values. Social inequalities are evident in the municipality, similarly to other cities in the world. This paper focuses on transportation activities related to air pollution and associated with cardiovascular and respiratory diseases especially on people who developed comorbidities during their whole life. This study relates travel trip data to air quality analysis and expanded to COVID-19 disease. This work studied the relationship of deaths in São Paulo due to COVID-19 with demographic density, with family income, with the use of public transport and with atmospheric pollution for the period between March 17th, 2020 and April 29th, 2021. The main results showed that generally passenger kilometers traveled, commuting times and air quality related diseases increase with residential distance from the city center, and thus, with decreasing residential density. PM 2.5 concentrations are positively correlated with COVID-19 deaths, regions with high urban densities have higher numbers of deaths and long-distance frequent trips can contribute to spread of the disease. • Mobility, air-quality, sociodemographic and COVID-19 data were related. • Personal exposure levels differ by type of activity, transport mode and income classes. • Private cars contribute to worsening air quality and affect the health of transit users. • Reducing traffic commuting times is crucial to prevent air pollution related diseases. • Public policies must be employed to improve living conditions of peripherical regions. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Crescimento e Desenvolvimento Humano is the property of Centro de Estudos de Crescimento e Desenvolvimento do Ser Humano and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The spatial distribution of socio-environmental vulnerability and COVID-19 deaths were evaluated. • Investments applied in the last ten years still disregard important aspects related to the unequal urban structure. • Government responses to the pandemic and climate change must be linked to technology, urban smartness, and nature-based solutions. This paper is divided into two parts to explore some aspects of municipal development related to national and subnational investments in disaster risk reduction and urban sustainability related to Covid-19 and climate change response. In Part I, a survey on disasters and national transfers to 45 Brazilian municipalities is presented. In Part II, the local-scale approach enabled to compare the areas most affected by COVID-19 with those impacted by climate change. There are large uncertainties around financial support from the federal government and their impact at local scale. São Paulo city was chosen because it reveals some important aspects of spatial structure carried out through local investments. In this sense, updated information on floods and warmer surfaces were updated to provoke a discussion about a potential confluence with the effects of pandemic. The results highlighted the effects of scarce federal transfers and the maps help us to identify the spatial distribution of people at risk, which can be beneficial for municipal decisions as they highlight a significative relationship between pandemic effects and an uneven social structure. In conclusion, the trade-off between this unequal structure and a necessary and effectively sustainable change leads us to reflect on local investment trends. [ABSTRACT FROM AUTHOR]

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO2 and O3 are significant independent variables, however, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America. [ABSTRACT FROM AUTHOR]

To control the transmission of coronavirus disease 2019 (COVID‐19), Brazilian local governments have adopted partial lockdown measures in economic sectors, thereby triggering transmission shocks along input–output supply chains. The national internal market and territorial disparities favor the formation of subnational production networks within borders, thus increasing the potential effects of lockdown measures on regional integration production networks. Therefore, this study makes hypothetical simulations of COVID‐19 mitigation policy decisions to understand the regional impacts on integration in supply chains, considering both domestic and global value chains. The generalized hypothetical extraction method is applied to a Brazilian interregional input–output model with 68 industries and 27 regions, imputing partial removals on intermediate consumption and final demand. The results suggest that richer subnational areas, mainly São Paulo and Rio de Janeiro, are proportionally more impacted by COVID‐19 trade shocks. However, the poorer peripheries are doubly affected, either by the foreign shock, which would damage their economic structure, or by the retraction of the subnational demand from core states. The findings highlight that economic shocks are spatially distributed through different industrial structures, thus stressing the need to avoid 'one size fits all' regional policies to mitigate the potential negative effects on exposed regions. [ABSTRACT FROM AUTHOR]

A two-dose regimen of the Oxford-AstraZeneca (ChAdOx1) Covid-19 vaccine with an inter-dose interval of three months has been implemented in many countries with restricted vaccine supply. However, there is limited evidence for the effectiveness of ChAdOx1 by dose in elderly populations in countries with high prevalence of the Gamma variant of SARS-CoV-2. Here, we estimate ChAdOx1 effectiveness by dose against the primary endpoint of RT-PCR-confirmed Covid-19, and secondary endpoints of Covid-19 hospitalization and Covid-19-related death, in adults aged ≥60 years during an epidemic with high Gamma variant prevalence in São Paulo state, Brazil using a matched, test-negative case-control study. Starting 28 days after the first dose, effectiveness of a single dose of ChAdOx1 is 33.4% (95% CI, 26.4–39.7) against Covid-19, 55.1% (95% CI, 46.6–62.2) against hospitalization, and 61.8% (95% CI, 48.9–71.4) against death. Starting 14 days after the second dose, effectiveness of the two-dose schedule is 77.9% (95% CI, 69.2–84.2) against Covid-19, 87.6% (95% CI, 78.2–92.9) against hospitalization, and 93.6% (95% CI, 81.9–97.7) against death. Completion of the ChAdOx1 vaccine schedule affords significantly increased protection over a single dose against mild and severe Covid-19 outcomes in elderly individuals during widespread Gamma variant circulation. Here, the authors investigate the effectiveness of the Oxford-AstraZeneca (ChAdOx1) vaccine during extensive Gamma variant SARS-CoV-2 circulation in São Paulo state, Brazil, and find that a two-dose regime is more effective than one dose against mild to severe Covid-19 outcomes in older adults. [ABSTRACT FROM AUTHOR]

Background: Kidney transplant recipients have higher COVID‐19 associated mortality compared to the general population. However, as only symptomatic patients seek medical attention, the current level of exposure, the main sources of acquisition, and the behavior of humoral immunity over time are poorly understood. Methods: This cross‐sectional prospective single‐center study recruited kidney transplant recipients of any age living in Sao Paulo. A sample size of 401 patients was calculated considering the 17.2% seroprevalence in the municipality population from a published survey, a 95% confidence interval and an absolute error of 2%. Results: Of the 2636 eligible patients, 416 were included. The seroprevalence for IgG anti‐SARS‐CoV‐2 was 8.2%. Seroconversion rate decreased with increasing age, from 15.7% (18–35 years) to 8.3% (36–60 years) and 4.2% (>60 years, p = 0.042). Seropositivity among previously confirmed COVID‐19 patients was 68.4%, followed by 9.4% in those with flu‐like symptoms and only 4.6% among asymptomatic patients (p < 0.0001). Seroprevalence was significantly higher among patients reporting household contact (p = 0.018). Twenty‐seven from the 34 IgG+ patients had a second test after 59 (IQR 50–63) days, and, in 33%, the IgG index became below the positivity threshold. Conclusions: In this cohort of kidney transplant recipients, the seroprevalence for IgG anti‐SARS‐CoV‐2 was lower than that of the general population, decreased with ageing, and was associated with household contacts. In a considerable proportion of the patients, there was a significant decay in the IgG levels in a short period of time. Therefore, preventive strategies, such as prioritization for vaccination, should be urgently considered. [ABSTRACT FROM AUTHOR]

Background: Multidrug-resistant tuberculosis (MDR-TB) is a serious phenomenon on a global scale that can worsen with the COVID-19 pandemic. The study aimed to understand the perceptions of health professionals about MDR-TB, their strategies to ensure adherence to treatment and their challenges in the context of the COVID-19 pandemic in a priority municipality for disease control.Methods: We conducted a qualitative study and recruited 14 health providers (four doctors, three nurses, three nursing technicians, three nursing assistants and a social worker) working in a city in the state of São Paulo, Brazil. Remote semi-structured interviews were conducted with the participants. For data analysis, the thematic content analysis technique was applied according to the study's theoretical framework.Results: The study revealed the causes of MDR-TB are associated with poverty, vulnerability, and social risk. A pre-judgement from the providers was observed, namely, all patients do not adhere due their resistance and association with drug abuse or alcoholism. The study also observed difficulty among health providers in helping patients reconstruct and reframe their life projects under a care perspective, which would strengthen adherence. Other issues that weakened adherence were the cuts in social protection and the benefits really necessary to the patients and a challenge for the providers manage that. The participants revealed that their actions were impacted by the pandemic and insecurity and fear manifested by patients after acquiring COVID-19. For alleviating this, medical appointments by telephone, delivery of medicine in the homes of patients and visits by health professionals once per week were provided.Conclusion: The study advances knowledge by highlighting the challenges faced by the health system with the adherence of patients with MDR-TB in a context aggravated by the pandemic. An improvement in DOT is really necessary to help the patients reframe their lives without prejudices, face their fears and insecurity, recover their self-esteem and motivate in concluding their treatment. [ABSTRACT FROM AUTHOR]

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A second wave of COVID-19 has demonstrated how challenging it will be to achieve sustained control of the disease, even with vaccination underway in many countries. Therefore, it remains relevant to keep improving our understanding of the distribution of COVID-19, especially of asymptomatic individuals, among different populations, and particularly in vulnerable regions. Hence, this population-based serosurvey had the objective of estimating the prevalence of individuals 18 years of age or older infected by SARS-CoV-2, and the proportion of asymptomatic individuals, among a vulnerable population living in an urban setting. This was a cross-sectional single-stage cluster sampling serosurvey conducted between September and December of 2019, in a vulnerable region of the city of São Paulo, Brazil. Families covered by three public primary healthcare units represented the selected clusters. After study inclusion, participants were asked about signs and symptoms related to COVID-19, and had collected 10 mL of blood for serology testing. A total of 272 individuals from 185 families were included in the study, out of the 400 eligible individuals for inclusion, resulting in a non-response rate of 32%. The post stratified prevalence of individuals infected by SARS-CoV-2 was 45.2% (95% CI: 39.4–51.0%), with a proportion of asymptomatic cases of 30.2% (95% CI: 23.3–38.0%). This population-based serosurvey identified a greater prevalence of infected individuals by SARS-CoV-2 compared to data from the beginning of the pandemic, and from a recent citywide serosurvey, with a similar proportion of asymptomatic individuals. It demonstrated the value of primary healthcare services for disease surveillance activities, and the importance of more focused serosurveys, especially in vulnerable locations, and the need to evaluate new surveillance strategies to take into account asymptomatic cases. [ABSTRACT FROM AUTHOR]

Background: At the moment we have more than 177 million cases and 3.8 million deaths (as of June 2021) around the world and vaccination represents the only hope to control the pandemic. Imperfections in planning vaccine acquisition and difficulties in implementing distribution among the population, however, have hampered the control of the virus so far. Methods: We propose a new mathematical model to estimate the impact of vaccination delay against the 2019 coronavirus disease (COVID-19) on the number of cases and deaths due to the disease in Brazil. We apply the model to Brazil as a whole and to the State of Sao Paulo, the most affected by COVID-19 in Brazil. We simulated the model for the populations of the State of Sao Paulo and Brazil as a whole, varying the scenarios related to vaccine efficacy and compliance from the populations. Results: The model projects that, in the absence of vaccination, almost 170 thousand deaths and more than 350 thousand deaths will occur by the end of 2021 for Sao Paulo and Brazil, respectively. If in contrast, Sao Paulo and Brazil had enough vaccine supply and so started a vaccination campaign in January with the maximum vaccination rate, compliance and efficacy, they could have averted more than 112 thousand deaths and 127 thousand deaths, respectively. In addition, for each month of delay the number of deaths increases monotonically in a logarithmic fashion, for both the State of Sao Paulo and Brazil as a whole. Conclusions: Our model shows that the current delay in the vaccination schedules that is observed in many countries has serious consequences in terms of mortality by the disease and should serve as an alert to health authorities to speed the process up such that the highest number of people to be immunized is reached in the shortest period of time. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (CoViD-19), with the fatality rate in elder (60 years old or more) being much higher than young (60 years old or less) patients, was declared a pandemic by the World Health Organization on March 11, 2020. A mathematical model considering young and elder subpopulations under different fatality rates was formulated based on the natural history of CoViD-19 to study the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The model considered susceptible, exposed, asymptomatic, pre-symptomatic, mild CoViD-19, severe CoViD-19, and recovered compartments, besides compartments of isolated individuals and those who were caught by test. This model was applied to study the epidemiological scenario resulting from the adoption of quarantine (isolation or lockdown) in many countries to control the rapid propagation of CoViD-19. We chose as examples the isolation adopted in São Paulo State (Brazil) in the early phase but not at the beginning of the epidemic, and the lockdown implemented in Spain when the number of severe CoViD-19 cases was increasing rapidly. Based on the data collected from São Paulo State and Spain, the model parameters were evaluated, and we obtained a higher estimation for the basic reproduction number R0 (9.24 for São Paulo State, and 8 for Spain) compared to the currently accepted estimation of R0 around 2 using the SEIR (susceptible, exposed, infectious, and recovered compartments) model. In comparison with the lockdown in Spain, the relatively early adoption of the isolation in São Paulo State resulted in enlarging the period of the first wave of the epidemic and delaying its peak. The model allowed to explain the flattening of the epidemic curves by quarantine when associated with the protective measures (face mask, washing hands with alcohol and gel, and social distancing) adopted by the population. The description of the epidemic under quarantine and protections can be a background to foreseen the epidemiological scenarios from the release strategies, which can help guide public health policies by decision-makers. [ABSTRACT FROM AUTHOR]

Objective: To investigate food insecurity (FI) prevalence in two favelas in Brazil in the early weeks of the social distancing policy, from 27 March 2020 to 1 June 2020.Design: A cross-sectional study using an online questionnaire to elicit information on socio-economic and demographic characteristics, the types of stores visited to buy food, and FI screening. The FI experience was evaluated according to the Brazilian Food Insecurity Scale. Factors associated with moderate or severe FI were investigated using the logistic regression model.Setting: São Paulo city, Brazil.Participants: Totally, 909 householders.Results: Eighty-eight per cent of the households included young women working as cleaners or kitchen assistants and in sales services. One-fifth of the participants were involved in the federal cash transfer programme, called Bolsa Família. There were 92 % households with children. The most frequent experience reported was uncertainty about food acquisition or receiving more (89 %), eating less than one should (64 %), not being able to eat healthy and nutritious food (46 %), and skipping a meal (39 %). Forty-seven per cent of the participants experienced moderate or severe FI. Factors associated with moderate and severe FI were low income, being a Bolsa Família recipient, having a low level of education and living in a household without children.Conclusions: Half of the participants experienced moderate or severe FI, and almost 10 % experienced hunger. Our data suggest that families with children were at a lower risk of moderate to severe FI. It is possible that nationally established social programmes such as Bolsa Família were protecting those families. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the discourses of patients who were diagnosed with multidrug-resistant tuberculosis, the perception of why they acquired this health condition and barriers to seeking care in a priority city in Brazil during the COVID-19 pandemic. This was an exploratory qualitative study, which used the theoretical-methodological framework of the Discourse Analysis of French matrix, guided by the Consolidated Criteria for Reporting Qualitative Research. The study was conducted in Ribeirão Preto, São Paulo, Brazil. Seven participants were interviewed who were undergoing treatment at the time of the interview. The analysis of the participants' discourses allowed the emergence of four discursive blocks: (1) impact of the social determinants in the development of multidrug-resistant tuberculosis, (2) barriers to seeking care and difficulties accessing health services, (3) perceptions of the side effects and their impact on multidrug-resistant tuberculosis treatment, and (4) tuberculosis and COVID-19: a necessary dialogue. Through discursive formations, these revealed the determinants of multidrug-resistant tuberculosis. Considering the complexity involved in the dynamics of multidrug-resistant tuberculosis, advancing in terms of equity in health, that is, in reducing unjust differences, is a challenge for public policies, especially at the current moment in Brazil, which is of accentuated economic, political and social crisis. The importance of psychosocial stressors and the lack of social support should also be highlighted as intermediary determinants of health. The study has also shown the situation of COVID-19, which consists of an important barrier for patients seeking care. Many patients reported fear, insecurity and worry with regard to returning to medical appointments, which might contribute to the worsening of tuberculosis in the scenario under study. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data. [ABSTRACT FROM AUTHOR]

With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in São Paulo, Brazil. The results indicate that the optimal strategy for São Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5–10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations. Incidence of COVID-19 has been high in parts of South America including Brazil, and information on effective intervention strategies is needed. Here, the authors use mathematical modelling to show that reductions in social distancing should be made gradually to avoid a severe second peak of cases. [ABSTRACT FROM AUTHOR]

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Introduction: There is a growing awareness about the noxious effects of the 2019 Coronavirus Disease (COVID-19) pandemic on the mental health of the elderly. However, there is limited information from clinically driven research. The objectives of the present study were to examine the magnitude of psychiatric symptoms and to determine their association with caregiver distress, in a cross-section of community-dwelling older adults and a subsample of aging adults with Down syndrome (DS) attending a psychogeriatric service in São Paulo, Brazil. Method: Telephone-based interviews and electronically filled self-assessment questionnaires were used to collect information from patients and caregivers, addressing their impressions and concerns about the pandemic and related effects on the patient's emotional state and behavior. Clinical information was obtained from hospital charts, medical records, and psychometric tests administered through telephone interviews [Hospital Anxiety and Depression Scale (HADS) and Neuropsychiatric Inventory Questionnaire (NPI-Q)]. Results: We included 100 consecutive participants, comprising 71 older adults with psychogeriatric/neurocognitive disorders and 29 aging adults with DS. Higher HADS and NPI-Q scores were significantly associated with caregiver distress (p < 0.05) in both groups. Correlation analyses indicated strong, positive associations between caregiver burden and scores in HADS anxiety (HADS-A) and HADS depression (HADS-D) scales in the subsamples of euploid and DS subjects. Higher NPI-Q scores in the former group were also correlated with caregiver distress, with stronger associations for neuropsychiatric symptoms. Similar findings were observed among DS subjects. ANOVA tests indicated significant associations between NPI-Q scores and caregiver distress among dementia patients, as well as with HADS scores. Similar results were found after multiple linear regressions; as such, among the elderly subsample, higher scores in HADS-A (p = 0.002) and HADS-D (p = 0.001) predict a significant impact on caregiver burden (p < 0.00001, R 2 0.46); taking into consideration caregiver burden as a dependent variable and NPI-Q total score as an independent variable, we obtained significant strong prediction values for either DS (p < 0.00001, R 2 0.95) or elderly adults (p < 0.00001, R 2 0.88). Conclusion: During the COVID-19 pandemic, patients with neurocognitive disorders present with clinically relevant neuropsychiatric symptoms, with significant impact on caregiver distress. Apathy, aberrant motor behavior, sleep disorders, and psychoses were the main psychopathological domains, which had determined caregiver burden worsening. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Medicine & International Health is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease. [ABSTRACT FROM AUTHOR]