A Brazilian classified data set for prognosis of tuberculosis, between January 2001 and April 2020.

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.

Illusion of Truth: Analysing and Classifying COVID-19 Fake News in Brazilian Portuguese Language

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%.

Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: An extended privacy calculus perspective

Mobile contact tracing applications have emerged as a potential solution to track and reduce the transmission of viruses such as Covid‐19. These applications require the disclosure of potentially sensitive personal information thus generating understandable implications for personal privacy. This research aims to determine the factors driving acceptance of these applications, with acceptance represented by three distinct variables, namely usage intentions, willingness to disclose personal data, and willingness to rely on health advice. The study examines the influence of perceived privacy, social influence, and benefits on acceptance of contact tracing applications among a sample of 1,114 Brazilian citizens. The study leverages social contract theory to demonstrate the importance of perceived control and perceived surveillance in the formation of individuals' perceptions of privacy. Integrating privacy calculus theory with social contract theory to include reciprocity and social influence, our findings suggest that perceived privacy, reciprocal benefits, and social influence all positively influence individuals' intentions to download or continue the use of contact tracing applications, while intentions to disclose information are influenced by adoption intentions, perceived privacy, and reciprocal benefits and individuals' willingness to rely on contact tracing applications for health advice is influenced by reciprocal benefits and disclosure intentions. [ABSTRACT FROM AUTHOR] Copyright of Journal of the Association for Information Science & Technology is the property of John Wiley & Sons, Inc. 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.)

Exploring the Competing Influences of Privacy Concerns and Positive Beliefs on Citizen Acceptance of Contact Tracing Mobile Applications

The continued proliferation of information technology in all aspects of our lives fosters benefits but also generates risks to individuals’ privacy. In emerging contexts, such as government surveillance technologies, there is a dearth of research investigating the positive and negative drivers of citizens’ acceptance. This is an important gap given the importance of citizen acceptance to the success of these technologies and the need to balance potentially wide-reaching benefits with any dilution of citizen privacy. We conduct a longitudinal examination of the competing influences of positive beliefs and privacy concerns on citizens’ acceptance of a COVID-19 national contact tracing mobile application among 405 Irish citizens. Combining privacy calculus theory with social exchange theory, we find that citizens’ initial acceptance is shaped by their perceptions of health benefits and social influence, with reciprocity exhibiting a sustained influence on acceptance over time and privacy concerns demonstrating a negative, albeit weak influence on willingness to rely on the application. The study offers important empirical and theoretical implications for the privacy literature in the government surveillance, location-based services, and mobile health application contexts, as well as practical implications for governments and developers introducing applications that rely on mass acceptance and reciprocal information disclosure.

#StayHome: Monitoring and benchmarking social isolation trends in Caruaru and the Região Metropolitana do Recife during the COVID-19 pandemic.

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.