Factors Influencing the Intention to Use PeduliLindungi Application among Indonesians during COVID-19

A COVID-19 pandemic struck a major section of the world's population in 2020, causing governments in various countries to put in place a range of tracking measures to identify and locate persons afflicted with the virus. While the government installs PeduliLindungi, a tracking application, there are always concerns over the reliability, personal information and data privacy recorded in the system. People might also be reluctant to use technology because of the ease of use. The purpose of this research is then to investigate the factors that have an impact on the likelihood that Indonesians will use the PeduliLindungi application. The UTAUT2 Theory was used as the overarching framework for this research. This theory takes into account a number of different aspects, some of which are performance expectancy, effort expectancy, social impact, an enabling environment, habit, and perceived privacy credibility. Purposive sampling, as well as a quantitative (survey) technique, were used in this study. In this technique, online questionnaires were issued to Indonesian residents who were aware of or had heard about the PeduliLindungi monitoring initiative, yielding 89 valid responses. The data show that the intention to use the PeduliLindungi application has a favorable and strong relationship with four variables. Social influence, facilitating conditions, habit, and perceived privacy credibility are examples of these characteristics. However, it was discovered that performance and effort expectations had no association with the intention to use the specific tracking instrument. This work adds to the field of media information technology studies by presenting the concept of perceived privacy credibility as a key construct for developing the UTAUT2 Theory in respect to the Internet of Things (IoT). This theory provides relevant ways for tracking suspicious patients for government authorities, medical professionals, and healthcare providers. © 2022 IEEE.

The Data Leakage Sentiment Analysis Using Naive Bayes Algorithm Based on Machine Learning Approach

Data leakage is a case that often occurs anywhere. Indonesia is one of the countries with the most population that is currently having data leakage cases. The leak of data on the COVID-19 PeduliLindung tracking application, triggered a public reaction because it was considered dangerous. Based on this, the aim of the study is to predict the sentiment pattern using Naïve Bayes. This study is important to do sentiment analysis to find out the public's reaction, it can become a recommendation in developing applications that are safer in data storage. The experiment in this study used data from Twitter which was taken for 14 days, between 16-21 May 2022. The data was processed using Google Collab and the Naïve Bayes approach. The experimental results are that negative sentiment is greater than positive sentiment and neutral sentiment, which is 93%. While the accuracy of positive sentiment is 81% and Neutra sentiment is 90%. This means the leak of public data from a COVID-19 tracing application in Indonesia has a greater negative sentiment. The difference in the study is the data testing process was carried out five times to get good accuracy from the model. And the results show that Naïve Bayes is a model that is quite recommended for prediction of sentiment patterns. © 2022 IEEE.

“You have been in Close Contact with a Person Infected with COVID-19 and you may have been Infected”: Understanding Privacy Concerns, Trust and Adoption in Mobile COVID-19 Tracing Across Four Countries

Through the past two and a half years, COVID-19 has swept through the world and new technologies for mitigating spread, such as exposure notification applications and contact tracing, have been implemented in many countries. However, the uptake has differed from country to country and it has not been clear if culture, death rates or information dissemination have been a factor in their adoption rate. However, these apps introduce issues of trust and privacy protection, which can create challenges in terms of adoptions and daily use. In this paper we present the results from a cross-country survey study of potential barriers to adoption of in particular COVID-19 contact tracing apps. We found that people’s existing privacy concerns are an have a reverse correlation with adoption behavior but that the geographical location, as well as other demographics, such as age and gender, do not have significant effect on either adoption of the app or privacy concerns. Instead, a better understanding of what data is collected through the apps lead to a higher level of adoption. We provide suggestions for how to approach the development and deployment of contact tracing apps and more broadly health tracking apps. © 2022 Association for Computing Machinery.

Privacy Concerned on Contact-Tracing Application during COVID-19

The COVID-19 pandemic has greatly affected humanity by destabilizing the world economy through strain on hospital systems and deaths. Medical personnel is working around the clock to establish vaccines. On the other hand, technology contributes to the fight against the virus by tracking COVID-19 infections. Many digital contact tracking smartphone applications have been created to address this epidemic successfully. However, the applications lack transparency, raising worries about their privacy. Contact tracing has been employed to stop the spread of the disease. When battling the coronavirus epidemic, computerized contact tracking has quickly emerged as an essential tool. Therefore, the research conducted in this paper focuses on the challenges of tracking applications to analyze the perspective view of privacy issues. Besides, the paper proposes policies for data privacy to aid in making the tracking applications more effective and successful. © 2022 IEEE.

Application of Bluetooth, Wi-Fi and GPS Technologies in the Means of Contact Tracking

The spread of infectious diseases, including COVID-19, is a complex epidemiological situation that is exacerbated by strong transcontinental migration processes that pose a potential threat to human health worldwide. This requires the implementation of a set of tasks aimed at full control over the risks and threats to human life. Modern information technology can be useful in solving a number of scientific and technical problems related to the use of cyberspace for global population monitoring, ie when monitoring and tracking contacts can predict possible adverse scenarios and prevent the spread of emergencies and crises, especially in the COVID-19 pandemic. A high overall level of e-society can keep epidemics and pandemics at the national and international levels under control. Global solutions to build monitoring systems to prevent the spread of infectious diseases already exist and are evolving rapidly. This section analyzes, explores and substantiates the possibility of using various information technologies (eg, Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools as the main subsystem of global population monitoring. In particular, the principles of construction and the possibility of using these technologies to track contacts are studied. Their advantages and disadvantages, potential attacks on monitoring programs, etc. are analyzed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A cross-sectional international study shows confidence in public health scientists predicts use of COVID-19 non-pharmaceutical interventions.

BACKGROUND: We examine the antecedents of COVID-19 phone tracking applications use, social distancing, and mask use, in the United States, Hong Kong and Japan. METHODS: We draw on online panel surveys of over 1000 respondents each in the USA, Hong Kong and Japan, using broadly representative quota sample selections. Results are tested by ordinal logistic regression for the two ordinal dependent variables and logistic regression for phone app use. RESULTS: Confidence in public health scientists predicts use of phone tracking applications, social distancing, and mask use, albeit statistically insignificant for tracer phone application use in Hong Kong. Trust in government predicts the use of a phone tracking application. Counterintuitively, trust in government is negatively and significantly associated with mask use and social distancing in Hong Kong and Japan. Women are more likely to wear masks and practice social distancing. Government employees are more likely to use a phone tracking application, but less likely to mask and social distance. Voting and civic participation are positively associated with trust in government and confidence in public health scientists, in all three countries. There are interesting variations across all three countries on other antecedents and controls. CONCLUSIONS: Building and maintaining confidence in public health scientists provides a key tool to manage pandemics. Credible, effectively communicative - and independent - medical and scientific leaders may be central to pandemic control success. For digital measures, trust in government and privacy protection is central. Political and social factors are important to understand successful public health policy implementation.

Contagious Patient Tracking Application Spotlight: Privacy and Security Rights

As the Covid-19 outbreak spreads across the globe and has killed many lives, many applications have been created to track patients and fight this pandemic. However, several applications lack safety and privacy. This paper designs and develops a mobile app to track patients with the Covid-19 or any other pandemic disease through using GPS in Iraq. Moreover, the app maintains a privacy for users by encrypting their personal data before sending them to the cloud using a MODE CBC AES block encryption algorithm. The app keeps the identity and location of the users, supports two language interfaces English and Arabic, and works in Android and iOS environments. Only the health care providers can decrypt these data and know about the patient's location. Also, to make the patient trusts the application, his/her information will be deleted after sending his/her negative test after 21 days. In addition, the app provides users with information regarding healthcare places in the case of emergency. For the evaluation of this app, a data was collected from 20 users, including males and females and their ages were between (20-50) in Mosul city. The results showed that the app works properly and the users are notified when they are in close with other registered infected people. In addition, the users found that the app was simple, easy to use, and useful to do contact safely. To convince the users to utilize this app, the app is provided with button trial option to try it. © 2021 IEEE.

CTAP: Covid-19 tracking application

In this paper, we propose an android mobile application, Covid-19 tracking application, that checks the social distance between the users, informs them of any encounter with a user positively tested with the virus, and informs the user of the basic coronavirus stats and information. This proposed application has been successfully implemented using Kotlin for the front-end, and Java for the back-end. The implementation of this application resulted in alerting the users who are violating social distancing by standing less than 2 meters next to other users, and notifying them of any encounter with a user positively tested with Covid-19. The percentage of error in distance measurement using Global Positioning System (GPS) is low, having an average value of 3.304%. © 2021 IEEE.