Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

Abstract

Big data analytics can revolutionize disease surveillance by integrating multiple data sources and analyzing those diverse data sources, allowing for early monitoring, detection, and response to disease outbreaks. Since the growth in technology, together with the advent of big data, there is growing interest in exploring the potential of multiple data sources for disease surveillance. However, there are still ethical concerns when it comes to big data usage for disease surveillance. Hence, this study uses of scoping review with the assistance of socio-technical systems theory, together with social contact theory to examine the ethical dimensions of employing big data analytics from diverse sources for disease surveillance, while emphasizing the importance of maintaining ethical standards in public healthcare practices. The study discovered that informed consent, privacy, and confidentiality are still major challenges when it comes to the integration of big data sources in disease surveillance. The study then recommends a unified ethical framework for enhanced disease surveillance in public healthcare through Multi-Data Source big data Analytics.