ABSTRACT
The COVID-19 pandemic has remained a global health crisis following the declaration by the World Health Organization. As a result, a number of mechanisms to contain the pandemic have been devised. Popular among these are contact tracing to identify contacts and carry out tests on them in order to minimize the spread of the coronavirus. However, manual contact tracing is tedious and time consuming. Therefore, contact tracing based on mobile applications have been proposed in literature. In this paper, a cross platform contact tracing mobile application that uses deep neural networks to determine contacts in proximity is presented. The application uses Bluetooth Low Energy technologies to detect closeness to a Covid-19 positive case. The deep learning model has been evaluated against analytic models and machine learning models. The proposed deep learning model performed better than analytic and traditional machine learning models during testing. © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved
ABSTRACT
Healthcare initiatives backed by electronic-governance (e-governance) principles have contributed well to the extant literature and practice. Governments and healthcare systems across the world were taken aback by the swamping impact of the COVID-19 pandemic. However, they reacted quickly by developing contact-tracing mobile applications (apps) for creating awareness, providing information about various healthcare initiatives, and helping citizens to use the required information in case of emergency. The major challenge was to develop such e-governance interventions in a short time and ensure their quick adoption among the masses. Hence, it is worthwhile to investigate the factors leading to the adoption of such e-governance initiatives, especially in the context of a widespread pandemic situation. The present study is an attempt to analyze the factors driving the intention to use contact tracing mobile apps launched by governments globally during the COVID-19 pandemic. We have conducted the study in the context of India, where the government launched a community-driven contact tracing mobile app for its citizens during the COVID-19 pandemic in April 2020. The study adopted an empirical approach to test how epistemic value, convenience value, conditional value, functional value, and privacy concerns influenced the intention to use this approach. The study found that intention to use such an app was positively influenced by functional value, which in turn was positively influenced by convenience and conditional values. It suggests that the convenience of using the app, perceived seriousness of the pandemic (i.e., conditional value), and utilitarian benefits (i.e., functional value) of the contact-tracing mobile app enhanced its acceptance. However, its novelty (i.e., epistemic value) and privacy concerns are not significant predictors of intention to use. The study recommends that the government should place more emphasis on improving the functional value which is driven by convenience and context-specific features to push the use of an e-governance initiative during the crisis © 2021 authors. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 Australia License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and AJIS are credited
ABSTRACT
OBJECTIVE: Many governments are using contact tracing mobile applications (CTMAs) yet public adoption of such systems has been relatively low. The main objective of this paper is to profile adopters (and non-adopters) of Australia's COVIDSafe CTMA. MATERIALS AND METHODS: We use latent profile analysis to examine predictors of CTMA download behaviour. Specifically, we draw on a representative Australian sample (N = 2575) to examine the interplay between age, education, income, dispositional desire for privacy and political ideology on download behaviour. We examine trust in government as a mediating mechanism between profiles and download behaviour. RESULTS: Our analysis produces seven profiles. Trust in government mediates the relationship between most profiles and download behaviour. A combination of wealth and education appear to be key explanatory factors of CTMA download behaviour. Two profiles -- comprising individuals with high income and education -- had the highest rates of download behaviour. Profiles with low download percentages comprised politically left-leaning participants with average to low income and education. CONCLUSION: Our findings clearly indicate the profiles of people who are (not) likely to download a CTMA. Practical ways to improve widespread adoption include providing structural support to the more vulnerable members of society, making clear the societal benefits of downloading CTMAs, and engaging in bipartisan promotion of such apps.