The novel coronavirus (COVID-19) has unleashed havoc across different countries and was declared a pandemic by the World Health Organization. Since certain evidences indicate a direct relationship of various viruses with the weather (temperature in particular), the same is being speculated about COVID-19; however, it is still under investigation as the pandemic is advancing the world over. In this study, we tried to analyze the spread of COVID-19 in the Indian sub-continent with respect to the local temperature regimes from 9 March, 2020, to 27 May, 2020. To establish the relation between COVID-19 and temperature in India, three different eco-geographical regions having signi cant temperature differences were taken into consideration for the analysis. We observed that except Maharashtra, Rajasthan and Kashmir showed a signi cantly positive correlation between the number of COVID-19 cases and the temperature during the period of study. The evidences based on the results presented in this research lead us to believe that the increasing temperature is bene cial to the COVID-19 spread, and the cases are going to rise further with the increasing temperature over India. We, therefore, conclude that the existing data, though limited, suggests that the spread of COVID-19 in India is not explained by the variation of temperature alone and is most likely driven by a host of other factors related to epidemiology, socioeconomics and other climatic factors. Based on the results, it is suggested that temperature should not be considered as a yardstick for planning intervention strategies for controlling the COVID-19 pandemic.

O ano de 2020 já está marcado como sendo o ano da pandemia COVID-19. Essa pandemia vem modificando as estruturas socioeconômicas no mundo, e as instituições educacionais no Brasil praticamente tiveram que interromper suas atividades presenciais devido às regras de distanciamento social. Conforme método de ensino, a aprendizagem mediada por tecnologia ganhou ênfase e abriu espaço para interações humanas diferenciadas. Essas metodologias de ensino já existiam, mas ainda alcançavam uma escala pequena. Este trabalho apresenta e discute as perspectivas brasileiras sobre os desafios e oportunidades para o uso das tecnologias educacionais para o ensino superior público. Os principais resultados apontam que a implementação de políticas de inclusão digital, visando diminuir as desigualdades regionais de acesso à internet é condição necessária para que qualquer estratégia de ensino remoto tenha possibilidade de êxito.

The world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries’ response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing.

On May 10, 2021, Brazil ranked second in the world in COVID-19 deaths. Understanding risk factors, or social and ethnic inequality in health care according to a given city population and political or economic weakness is of paramount importance. Brazil had a seriousness COVID-19 outbreak in light of social and economic factors and its complex racial demographics. The objective of this study was to verify the odds of mortality of hospitalized patients during COVID-19 infection based on their economic, social, and epidemiological characteristics. We found that odds of death are greater among patients with comorbidities, neurological (1.99) and renal diseases (1.97), and immunodeficiency disorders (1.69). While the relative income (2.45) indicates that social factors have greater influence on mortality than the comorbidities studied. Patients living in the Northern macro-region of Brazil face greater chance of mortality compared to those in Central-South Brazil. We conclude that, during the studied period, the chances of mortality for COVID-19 in Brazil were more strongly influenced by socioeconomic poverty conditions than by natural comorbidities (neurological, renal, and immunodeficiency disorders), which were also very relevant. Regional factors are relevant in mortality rates given more individuals being vulnerable to poverty conditions.

Abstract-In this work, a software application was developed to analyze and visualize messages over Twitter social network, ranking the posts relatively to variations in moods within the Brazilian territory. Artificial intelligence techniques such as text mining and sentiment analysis were used for this purpose. The use of methods of machine learning allows determining the polarity (positive or negative) of tweets collected. Results were displayed in cartograms, through representations of tweet's geographic locations. Surprisingly, another study of twitter's mood from United States Nation showed similar results for the variation of moods throughout the day, hypothesizing a humor pattern for human beings during the period of 24 hours.

In November 2020, Brazil ranked third in the number of cases of coronavirus disease 2019 (COVID-19) and second in the number of deaths due to the disease. We carried out a descriptive study of deaths, mortality rate, years of potential life lost (YPLL) and excess mortality due to COVID-19, based on SARS-CoV-2 records in SIVEP-Gripe (Ministry of Health of Brazil) from 16 February 2020, to 1 January 2021. In this period, there were 98,025 deaths from COVID-19 in Brazil. Men accounted for 60.5% of the estimated 1.2 million YPLLs. High YPLL averages showed prematurity of deaths. The population aged 45–64 years (both sexes) represented more than 50% of all YPLLs. Risk factors were present in 69.5% of deaths, with heart disease, diabetes and obesity representing the most prevalent comorbidities in both sexes. Indigenous people had the lowest number of deaths and the highest average YPLL. However, in indigenous people, pregnant women and mothers had an average YPLL of over 35 years. The excess mortality for Brazil was estimated at 122,914 deaths (9.2%). The results show that the social impacts of YPLL due to COVID-19 are different depending on gender, race and risk factors. YPLL and excess mortality can be used to guide the prioritization of health interventions, such as prioritization of vaccination, lockdowns, or distribution of facial masks for the most vulnerable populations.

This paper presents a study about which kind of interaction leads to better learning within a collaborative learning task, in terms of the effects of a speech act on both the actor and their collaborative partner, in a cooperative or competitive behavior. To perform this study, we have developed an analysis model that investigates features in dialogue interactions based on student's differences in understanding, as shown by their conversation protocol, and an investigation of the cognitive conflict based on Piagetian theories. We have analyzed data from a study conducted in a computer supported collaborative learning environment (CSCL) in the domain of fractions. The long-term goal of this model is to better understand collaborative learning and to support pedagogical agents in intervening in dialogue in order to keep collaboration productive.

This work explores (non)linear associations between relative humidity and temperature and the incidence of COVID-19 among 27 Brazilian state capital cities in (sub)tropical climates, measured daily from summer through winter. Previous works analyses have shown that SARS-CoV-2, the virus that causes COVID-19, finds stability by striking a certain balance between relative humidity and temperature, which indicates the possibility of surface contact transmission. The question remains whether seasonal changes associated with climatic fluctuations might actively influence virus survival. Correlations between climatic variables and infectivity rates of SARS-CoV-2 were applied by the use of a Generalized Additive Model (GAM) and the Locally Estimated Scatterplot Smoothing LOESS nonparametric model. Tropical climates allow for more frequent outdoor human interaction, making such areas ideal for studies on the natural transmission of the virus. Outcomes revealed an inverse relationship between subtropical and tropical climates for the spread of the novel coronavirus and temperature, suggesting a sensitivity behavior to climates zones. Each 1 °C rise of the daily temperature mean correlated with a −11.76% (t = −5.71, p < 0.0001) decrease and a 5.66% (t = 5.68, p < 0.0001) increase in the incidence of COVID-19 for subtropical and tropical climates, respectively.