Background In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated. Objective This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic. Methods We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country. Results Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19–related deaths. Conclusions The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil’s epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million.

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BACKGROUND The global impact of COVID-19 has been dreadful, undermining public health, considered to be the most severe, ever observed, as a respiratory disease, in the last years. It takes on a rapid dissemination, risking the health of a huge number of people, and consequently overburdening healthcare infrastructure, leading to eventual collapse. Nowadays, a country that draws a lot of attention is Brazil, which has shown expressive number of cases and deaths in comparison to other countries. Thus, there is a high chance that the actual number of infected in Brazil is far larger than those notified, and it is very likely that the actual growth of the disease is being underestimated. A proper estimation of the underreported or wrongly reported cases becomes paramount in order to have a better understanding of the actual epidemic scenario, allowing necessary and effective measures OBJECTIVE This study investigates the mortality underreporting related to COVID-19 in the most affected Brazilian cities in order to identify the real scenario of the pandemic in Brazil. METHODS This research used data from the historical series of deaths, due to respiratory problems and other natural causes, from two databases: DATASUS (2010 to 2018) and the Brazilian Transparency Portal of Civil Registry (2019 to 2020). These data were used to build time series models (modular regressions) able to predict the expected behavior of deaths in 2020. The predictions are used to estimate the possible number of death reports that were incorrectly registered during the pandemic in the most affected cities in the country. RESULTS The model found a significant disagreement between the real and expected values. The number of deaths due to SARS was considerably higher in all of the cities, presenting increases between 493% and 5820%. Considering the cities of the case study, an average underreporting of 40.68%, varying between 25.9% and 62.7%, is estimated for deaths related to COVID-19. CONCLUSIONS The quite significant rates of underreporting of deaths presented in our research allow us to realize that the officially released numbers to be much lower than the actual numbers, making it impossible for the authorities to take more effective actions. Considering the results and analyzes carried out with different fatality rates, it can be inferred that Brazil has a growing epidemic scenario and the real number of infected would already be between approximately 1,2 million and 5,4 millions, becoming new epicenter of the pandemic.

Twitter has more than 313 million users, is considered a subjective social network of content generation on the Internet, not only used to disclose personal information, but shares opinions and information about events and events in general, being a great source of research for the discovery of knowledge among relevant data. In this context, the present work presents a study on the implementation (application) of the Recurrent Neural Network LSTM for the analysis and classification of tweets that are related to crime and not crime. The set of data that is the object of the survey comes from an account of a newspaper in the city of Belém, containing 20000 records in a period of two months. The results obtained with the use of RNR LSTM proved to be quite satisfactory, reaching accuracy in some cases of 90% correctness. Based on the results of this research, we observed the effectiveness of this method in the analysis of feelings in comparison with other algorithms such as Nayve Bayes, Convolutional Neural Networks used in the literature. Because it is a network that uses a Long-Term Memory technique, it can adapt to large time intervals, such as Twitter texts.