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After COVID-19, tuberculosis (TB) is the leading cause of death by an infectious disease in the world. This work presents a data set based on data collected from the Brazilian Information System for Notifiable Diseases (SINAN) for the period from January 2001 to April 2020 relating to patients diagnosed with tuberculosis in Brazil. The data from SINAN was pre-processed to generate a new data set with two distinct treatment outcome classes: CURED and DIED. The data set comprises 37 categorical attributes (including socio-demographic, clinical, and laboratory data) as well as the target class. There are 927,909 records of patients classified as CURED and 36,190 classified as DIED, totaling 964,099 records. [ABSTRACT FROM AUTHOR]

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine learning and deep learning techniques to identify fake news in online communications in the Brazilian Portuguese language relating to the COVID-19 pandemic. We build a dataset of 11,382 items comprising data from January 2020 to February 2021. Exploratory data analysis suggests that fake news about the COVID-19 vaccine was prevalent in Brazil, much of it related to government communications. To mitigate the adverse impact of fake news, we analyse the impact of machine learning to detect fake news based on stop words in communications. The results suggest that stop words improve the performance of the models when keeping them within the message. Random Forest was the machine learning model with the best results, achieving 97.91% of precision, while Bi-GRU was the best deep learning model with an F1 score of 94.03%. [ABSTRACT FROM AUTHOR]

One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical countries. Brazil has the largest absolute number of cases in Latin America. This work presents a unified data set with clinical, sociodemographic, and laboratorial data on confirmed patients of Dengue and Chikungunya, as well as patients ruled out of infection from these diseases. The data is based on case notification data submitted to the Brazilian Information System for Notifiable Diseases, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), from 2013 to 2020. The original data set comprised 13,421,230 records and 118 attributes. Following a pre-processing process, a final data set of 7,632,542 records and 56 attributes was generated. The data presented in this work will assist researchers in investigating antecedents of arbovirus emergence and transmission more generally, and Dengue and Chikungunya in particular. Furthermore, it can be used to train and test machine learning models for differential diagnosis and multi-class classification. Measurement(s) clinical data Technology Type(s) interview [ABSTRACT FROM AUTHOR]

Background: The Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn. Methods: The Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system. Results: The main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system. Conclusions: The proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

Background: Although considerable success in reducing the incidence of malaria has been achieved in Brazil in recent years, an increase in the proportion of cases caused by the harder-to-eliminate Plasmodium vivax parasite can be noted. Recurrences in P. vivax malaria cases are due to new mosquito-bite infections, drug resistance or especially from relapses arising from hypnozoites. As such, new innovative surveillance strategies are needed. The aim of this study was to develop an infographic visualization tool to improve individual-level malaria surveillance focused on malaria elimination in the Brazilian Amazon. Methods: Action Research methodology was employed to deal with the complex malaria surveillance problem in the Amazon region. Iterative cycles were used, totalling four cycles with a formal validation of an operational version of the Malaria Trigram tool at the end of the process. Further probabilistic data linkage was carried out so that information on the same patients could be linked, allowing for follow-up analysis since the official system was not planned in such way that includes this purpose. Results: An infographic user interface was developed for the Malaria Trigram that incorporates all the visual and descriptive power of the Trigram concept. It is a multidimensional and interactive historical representation of malaria cases per patient over time and provides visual input to decision-makers on recurrences of malaria. Conclusions: The Malaria Trigram is aimed to help public health professionals and policy makers to recognise and analyse different types of patterns in malaria events, including recurrences and reinfections, based on the current Brazilian health surveillance system, the SIVEP-Malária system, with no additional primary data collection or change in the current process. By using the Malaria Trigram, it is possible to plan and coordinate interventions for malaria elimination that are integrated with other parallel actions in the Brazilian Amazon region, such as vector control management, effective drug and vaccine deployment strategies. [ABSTRACT FROM AUTHOR]

These authors contributed equally: Sebastião Rogério da Silva Neto, Thomás Tabosa de Oliveira, Igor Vitor Teixiera, Leonides Medeiros Neto, Vanderson Souza Sampaio, Theo Lynn and Patricia Takako Endo. Author Correction: Arboviral disease record data - Dengue and Chikungunya, Brazil, 2013-2020 Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01312-7, published online 10 May 2022 In this article the funding information relating to support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 was omitted. [Extracted from the article]

Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease., Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance., Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice., Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.

This technical report presents information related to the Social Isolation Index (SII) of the city of Caruaru, Pernambuco, Brazil. The data was provided by In Loco, a technology startup that has collected the movement of around 60 million Brazilians through cell phone location.